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Section 1: The Problem
Introduction
Personalized learning promotes student individuality throughout the learning
process; however, it is not easily implemented across all curricular areas (Basham, Hall,
Carter, & Stahl, 2016). A midwestern urban school district that integrated personalized
learning across the curriculum was the focus of this research. An explanatory sequential
mixed-methods design was used to investigate the challenges that arise when secondary
teachers implement a personalized learning environment in their content area.
The era of high-stakes testing and dictated learning standards has created an
education system where students are primarily offered learning opportunities that are
influenced by political movements (Bingham, Pane, Steiner, & Hamilton, 2016;
Rutledge, Cohen-Vogel, Osborne-Lampkin, & Roberts, 2015). Student learning has
become micro-managed and reaching the needs of the average ability learner has become
the norm in education while the interests of struggling and high ability learners are often
ignored (Gillard, Gillard, & Pratt, 2015). The Every Student Succeeds Act (ESSA)
constitutes a conscious effort to provide equal educational opportunities for all students
while holding all students to high academic standards (U. S. Department of Education,
2016).
Through the analysis of ACT, SAT, and college remediation rates, Childress and
Benson (2014) found that only 37% of students are prepared to succeed in college (p. 33-
34). With the diverse student demographics in the public school setting and the
responsibility of educators to help all students succeed, there is a need for school systems
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to implement an instructional program that integrates students’ voice into curricular
decisions and personalizes learning to maximize educational opportunities for them all
(Busher, 2012; Childress & Benson, 2014; Deed et al., 2014a). Personalized learning
creates a learning atmosphere that is more engaging to students since it is tailored to their
individual needs and thus is relevant to each student (U. S. Department of Education,
2016a). Implementation of this mode of instruction generates challenges in the teachers’
preparation of daily instruction. Bingham et al. (2016) stated that the implementation of a
personalized learning model “requires some significant changes in teacher practice . . .
teachers had to learn new teaching methods” (p. 21). Although personalized learning has
the potential to increase student achievement, it also changes the course of instruction
provided by classroom teachers.
The Local Problem
The problem addressed in this study was the challenge secondary teachers across
content areas have when implementing personalized learning opportunities for students.
In this study, I investigated the differences in secondary teachers’ efforts to implement
personalized learning in their content area, secondary teachers’ perceived value of
personalized learning, and the challenges teachers encountered in the implementation
process.
The subject of this study, a midwestern urban public school district with a history
of commitment to excellence, innovative practices, and community support, sought to
maintain a curricular structure that prepared students to be internationally competitive.
The focus on the district’s strategic plan was continued work on innovative instructional
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practices (AdvancED, 2016). In recent years, this district has experienced increased
student enrollment, changing student demographics and budget shortfalls. Each of which
greatly affected the district, including class size and curricular program options.
Personalized learning emphasizes students’ voice and choice to increase their
engagement in the learning process. Based upon informal conversations with teachers in
multiple content areas at this midwestern urban public school district, the implementation
of personalized learning has been a challenge, due to a lack of school infrastructure,
ineffective use of available data, a lack of teacher preparation and buy in, and student
assessment practices (Abbott & Wren, 2016; Basham et al., 2016; & Bingham et al.,
2016). Teacher and student raw data from the district’s strategic plan survey, illustrated
varied implementation efforts of the core characteristics of personalized learning amongst
departments.
In 2014 while updating its strategic plan, this midwestern urban public school
district, conducted focus groups led by administrative leadership with district
stakeholders in order to research and discuss what education could and should look like
(Westside Community Schools, 2014). The school district’s superintendent stated
It is my sense that public education is at a crossroads nationally and is in flux in
the Metro area. Our challenge is to create student-driven learning environments.
Our goal is to prepare our next generation of learners to confront a rapidly
changing global society. Our work is to create a vision for the future and to
develop a set of policy recommendations and implementation strategies to
accomplish our goal. (McCann, 2013, p. 3)
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The focus group results directed the school district towards a personalized learning
concept where students and teachers examine learner profiles and collaboratively design
educational opportunities tailored to the students’ strengths and interests (McCann,
2016).
The essential elements of personalized learning adopted by this school district
consist of knowing your learners, student voice and choice, flexible groupings and space,
data-informed instruction, and technology integration. To start the integration process,
cohorts of K-12 teachers were invited to participate in a yearlong professional learning
experience centered on personalized learning. Three yearlong cohorts, starting in the fall
of 2014, have completed the process. Some of the district’s professional learning
communities set personalized learning goals and teachers had opportunities to attend
additional professional learning experiences throughout the year (personal
communication, Assistant Superintendent of Curriculum, Instruction, and Assessment,
September, 19, 2016). However, there is more work to be done.
According to the 2015-2016 Strategic Plan Teacher and Student Survey, which
was administered to district students and teachers, implementation of personalized
learning elements was not consistently implemented (Westside Community Schools,
2016). For instance, student surveys reported that 39% of students in Grades 7-8, and
46% of students in Grades 9-12 disagreed or strongly disagreed that they had choices in
their learning. The majority of Grades 7-12 teachers reported the following elements are
integrated into instructional practices less than five days in a typical 10-day period: 62%
of teachers incorporated student voice and choice, 63% of teachers included flexible
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grouping in classroom settings, 68% of teachers made data-informed instructional
decisions, 32% of teachers integrated technology, and 50% of teachers related instruction
to what they knew about their students. These data illustrated low implementation of
personalized learning elements in the secondary grade levels. As the school district
continues to advocate for personalized learning opportunities for students, it is essential
to understand the reasoning for low implementation efforts amongst secondary teachers.
This will allow the school district to provide appropriate resources to help teachers
overcome implementation challenges specific to the teachers’ content area.
Peeters, De Backer, Kindekens, Triquet, and Lombaerts (2016) stated in their
research on teacher differences on student-regulated learning that teachers were
concerned about meeting the needs of all students during personalized instruction; such
as: ensuring high ability students remained challenged and providing enough support for
struggling students. They were also concerned that students were responsible enough to
handle this mode of instruction (2016, p. 91). Such concerns could influence the
expectancies and values that teachers hold of personalized learning as an effective
instructional tool. A closer examination of secondary teacher raw data, showed that
implementation varied by department as well. Allison (2013) stated that “robust self-
efficacy” in teachers would lead to meaningful change in classroom dynamics; however,
this could only happen when teachers’ learning experiences connect content knowledge
and instructional practice (2013, p. 181). The counseling, engineering and technology,
mathematics, music, and science departments implement each of the four essential
elements less than five days in a typical 10-school-day period. Flexible grouping and
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space and data-informed instructional decisions are the least implemented amongst these
departments. Secondary teachers in the mathematics and music department reported the
least amount of implementation overall. Secondary teachers in the business, language
arts, and social science departments reported the highest implementation of personalized
learning elements; student voice and choice had the highest implementation. (Westside
Community Schools, 2016). The survey results demonstrated differences amongst the
Grades 7-12 curricular departments, which poses the question: What is causing poor
implementation of personalized learning in some departments and higher implementation
in other departments? During conversations with secondary teachers who visited a high
school in a nearby state that has implemented personalized learning, multiple concerns
were shared. One teacher reported that unless the district planned to purchase software to
individualize instruction then personalization was near impossible; another reported that
most examples were shown in English classrooms and there were few examples in other
content areas to examine (personal communication, secondary teacher, July 2016). These
concerns showed devalued personalized learning as a meaningful learning opportunity
across curricular areas. Teachers with higher “task value beliefs” of learning experiences
that focus on student voice are more likely to offer opportunities for personalized
instruction (Peeters, De Backer, Kindekens, Triquet, & Lombaerts, 2016, p. 92).
The continued district focus on personalized learning is supported by the school
improvement priority, as stated in the 2016 AdvancED External Review Exit Report from
Indicator 3.3: “implement and monitor use of a district wide instructional process in all
classrooms … to provide all students with engaging, challenging, and personalized
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learning experiences that ensure achievement of learning expectations” (Assistant High
School Principal, personal communication, May 4, 2016). The comparison of the
Effective Learning Environments Observation Tool (ELEOT) to the AdvancEd network
(AEN) scores supported this statement. The school district’s ELEOT scores were lower
across the board when compared to the AEN average, which surprised the external
review committee due to the district’s strategic plan and reputation (AdvancEd, April 27,
2016). The expectation of Indicator 3.3, that personalized learning will be embedded in
classroom instruction, will require teachers to modify current instructional practices. The
school district’s strategic plan data, the AdvancED priority statement, and personal
communications with district staff members supported the need to research teachers’
challenges in implementing personalized learning along with instructional support needed
to overcome these challenges so that classroom practices provide opportunities for
increased student achievement.
Rationale
Even though personalized learning offers potential for student-focused learning
and meeting the needs of individual learners, there is limited knowledge of its application
in the educational environment (Basham et al., 2016, p. 126). The local problem
illustrated the desire of a school district to implement the innovative instructional practice
of personalized learning to increase student achievement. Initial data pointed to
inconsistent implementation amongst secondary teachers in the five elements of
personalized learning: knowing your learners, student voice and choice, flexible
groupings and space, data-informed instruction, and technology integration. For the
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purpose of this study, I investigated the challenges faced by secondary teachers in
implementing a personalized learning environment in their content area. As a result of
this research, potential solutions to eliminate the inconsistent implementation practices
amongst content areas may be designed.
Definition of Terms
Data-informed instruction: Instructional decisions based on ongoing, transparent,
and actionable use of student data (Basham et al., 2016, p. 133)
Content areas: The domain of knowledge that creates a set of standards students
are expected to learn and master (U. S. Department of Education, 2008). For the purpose
of this study, the content areas are language arts (including world languages),
mathematics (including engineering and technology), performing and visual arts, science
(including health and physical education), and social sciences (including business).
e-Learning: The use of technology, such as: computers, tablets, or hand-held
devices, to enhance learning experiences beyond the classroom (Delgado et al., 2015;
Sahin & Kisla, 2016).
Flexible grouping: Learning spaces are created based on the needs of the student
for the current learning task, including individual and group work opportunities (Deed et
al., 2014a; Deed et al., 2014b).
Knowing your learners: Examining students’ strengths and weaknesses and
designing learning to help students grow as individual learners based on the level of
content mastery (Basham et al., 2016; Bingham et al., 2016).
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Online learning: Online learning, also referred to as distance education or web-
based learning, provides students with access to content not available at their school site.
This learning platform may include courses made available at other educational sites that
are accessible using the Internet and software programs designed to meet individual
student needs. Although, online learning provides additional learning experiences for
students little, if any, face-to-face contact is provided throughout the learning process
(Delgado et al., 2015; Mitchell et al.,2016).
Personalized learning: Personalized learning places students’ interests, needs, and
strengths at the center of classroom instruction allowing students to take ownership of
their learning experiences. This mode of instruction utilizes flexibility and learner voice
to support student achievement (Basham et al., 2016; Childress & Benson, 2014).
Professional development: Learning experiences for educators to strengthen
connections between a teacher’s pedagogy and their discipline (Allison, 2013).
Professional learning network (PLN): Professional learning networks consist of
learning experiences to promote professional growth. Learning experiences may include:
district orchestrated sessions, peer observations and discussions, conferences, and online
networking. Teachers participating in PLNs have opportunities to search for personalized
learning experiences to meet professional goals (Krutka, Carpenter, & Trust, 2017).
Student engagement: Student participation in the learning process. Active
participation throughout the learning process is the result of peer and teacher interactions,
value of the learning task, relevance of learning standards to the student, and level of
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student effort (Harbour, Evanovich, Sweigart, & Hughes, 2015; Reeve & Lee, 2014;
Tlhoaele, Hofman, Winnips, & Beetsma, 2014).
Student voice and choice: Multiple options for engagement in learning are
provided to empower students in the educational process including demonstrating
mastery of learning concepts in multiple ways (Basham et al., 2016; Busher, 2012).
Technology integration: Using technology to enhance instruction for students.
Technology integration may include learner profiles and online learning environments
(Basham et al., 2016; Bingham et al., 2016; Chen, Huang, Shih, & Chang, 2016).
Significance of the Study
This study investigated the challenges secondary teachers encounter in creating a
personalized learning classroom environment in their content areas. The results of this
research is expected to provide valuable feedback on personalized learning instructional
delivery, which can improve overall student achievement. As a midwestern urban public
school district implemented personalized learning programming in its schools, I used a
mixed-methods approach to investigate the current challenges of implementing
personalized learning and interpret the results to determine the resources necessary to
meet the district’s strategic goal of maximizing student engagement and achievement.
This research promotes positive social change through the study of an innovative
instructional modality, personalized learning, which places the learner at the center of the
education process, thus making education student-focused. Technological resources have
changed education for 21st learners by placing information at students’ fingertips.
Teachers can no longer be the delivery agents of knowledge. Instead, it is vital to charge
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students with the initiative to take ownership of their learning (Gillard, Gillard, & Pratt,
2015, p. 3). If a personalized learning program is successfully implemented, such that it
increases student achievement, the program may provide a means to create lifelong
learners after high school.
Research Questions
In this study, I examined the challenges that arise as midwestern urban public
school secondary content area teachers implemented personalized learning in their
classroom environments: knowing your learners, student voice and choice, flexible
groupings and space, data-informed instruction, and technology integration. In this study,
I investigated the instructional support that secondary teachers perceived would help
them overcome the challenges in personalized learning implementation across content
areas.
The quantitative research question was used to analyze the reported use of the five
elements of personalized learning by secondary teachers from the following content area
departments: language arts (including world languages), mathematics (including
engineering and technology), performing and visual arts, science (including health and
physical education), and social sciences (including business). Thus, Research Question 1
was broken into five subquestions based on the essential elements of personalized
learning.
RQ1 (Quantitative): How does the implementation of each of the personalized
learning essential elements differ between content area departments?
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RQ1(a): How does the implementation of the personalized learning
essential element, knowing your learners, differ between content area
departments?
RQ1(b): How does the implementation of the personalized learning
essential element, student voice and choice, differ between content area
departments?
RQ1(c): How does the implementation of the personalized learning
essential element, flexible groupings and space, differ between content
area departments?
RQ1(d): How does the implementation of the personalized learning
essential element, data-informed instruction, differ between content area
departments?
RQ1(e): How does the implementation of the personalized learning
essential element, technology integration, differ between content area
departments?
For the quantitative component of this study, a null and a nondirectional alternative
hypothesis was generated:
H0: There is no significant difference in the implementation of personalized
learning essential elements amongst secondary content area departments in a
midwestern urban school district.
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H0(a): There is no significant difference in the implementation of the
personalized learning element, knowing your learners, amongst secondary
content area departments in a midwestern urban school district.
H0(b): There is no significant difference in the implementation of the
personalized learning element, student voice and choice, amongst
secondary content area departments in a midwestern urban school district.
H0(c): There is no significant difference in the implementation of the
personalized learning element, flexible grouping and space, amongst
secondary content area departments in a midwestern urban school district.
H0(d): There is no significant difference in the implementation of the
personalized learning element, data-informed instruction, amongst
secondary content area departments in a midwestern urban school district.
H0(e): There is no significant difference in the implementation of the
personalized learning element, technology integration, amongst secondary
content area departments in a midwestern urban school district.
HA: There is a difference in the implementation of personalized learning essential
elements amongst secondary content area departments in a midwestern urban
school district.
HA(a): There is a difference in the implementation of the personalized
learning element, knowing your learners, amongst secondary content area
departments in a midwestern urban school district
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HA(b): There is a difference in the implementation of the personalized
learning element, student voice and choice, amongst secondary content
area departments in a midwestern urban school district.
HA(c): There is a difference in the implementation of the personalized
learning element, flexible grouping and space, amongst secondary content
area departments in a midwestern urban school district.
HA(d): There is a difference in the implementation of the personalized
learning element, data-informed instruction, amongst secondary content
area departments in a midwestern urban school district.
HA(e): There is a difference in the implementation of the personalized
learning element, technology integration, amongst secondary content area
departments in a midwestern urban school district.
Two research questions focused on the qualitative component of this research.
Classroom observations and interviews with secondary teacher participants were
conducted to gather qualitative data.
RQ2 (Qualitative): How do teachers demonstrate the implementation of
personalized learning elements in their content area?
RQ3 (Qualitative): What do teachers describe as challenges in implementing a
personalized learning environment in their content area?
Research Question 2 was answered using data collected from classroom observations
while Research Question 3 was addressed during teacher interviews.
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Review of the Literature
The review of the literature was conducted using multiple databases: Education
Source, ERIC, Google Scholar, ProQuest Central, and SAGE Journals. The following
phrases were used: instructional practices, instructional technology, K-12 technology
integration, personalized learning, student engagement, student motivation, and teacher
professional development.
Theoretical Framework
Expectancy-value theory is the theoretical framework for this study investigating
teachers’ challenges in implementing personalized learning in their specific content area.
Wigfield, Allan, Tonks, and Lutz (2009) defined expectancies and values as beliefs that
are “task-specific” including competence, difficulty level, personal goals, experiences
related to the task, and achievement (p. 56). Expectancies and values influence individual
choices through performance, effort, and persistence (Atkinson, & Reitman, 1956;
Wigfield, Allan, Tonks, & Lutz, 2009; Wigfield & Eccles, 2000). Teacher expectancies
and values of personalized learning will influence the success of classroom
implementation and overall student achievement. Atkinson and Reitman (1956) stated
If more than one of an individual’s motives are engaged by expectancies that the
same act will lead to several different goals, the total motivation for performance
of that act will be the sum of the contributions made by the particular motives
which have been engaged. (p. 361)
Expectancies and values of personalized learning as seen by teachers has the potential to
influence teacher success on identified goals, such as curriculum design and planning
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classroom instruction. Wiggins and Eccles (1999) stated that expectancies and values
have a direct influence on achievement choices (p. 69). The value a teacher places on the
effectiveness of personalized learning in their content area has the potential to motivate
teachers to overcome challenges in its implementation.
Expectancy-value theory integrates individual values and beliefs and how they
mold future outcomes. Utility value focuses on personal goals and future plans while
building intrinsic motivation to reach a goal (Wigfield, Allan, Tonks, & Lutz, 2009;
Wigfield & Eccles, 2000). Ability beliefs are defined by the competence an individual
has regarding a specific task and influence future success (Wigfield & Eccles, 2000, p.
70). When teachers implement personalized learning in their content areas, their value
and beliefs of this instructional mode will define the teachers’ ability to refine learning
opportunities throughout instruction and overall student achievement. As challenges arise
during implementation efforts, the teacher’s values and beliefs may decrease and deter
the teacher from integrating personalized learning opportunities in their content area.
Atkinson and Reitman (1956) explained that expectancies are aroused when the
“performance of an act is instrumental to the attainment of the goal of the motive” (p.
366). If teachers believe that personalized learning will increase student engagement in
learning their content, then personalized learning will be considered as a valuable means
of instruction.
Expectancy-value theory supports the purpose of this research and informs the
research questions by relating teachers’ values and beliefs of personalized learning to
how successful they are able to integrate personalized learning experiences in their
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content areas. The data analysis following the investigation of the quantitative research
question will show if different secondary content area departments have higher
expectancies of the essential elements of personalized learning (knowing your learners,
student voice and choice, flexible groupings and space, data-informed instruction, and
technology integration) through the frequency of the departments’ use of each element.
The two qualitative research questions will provide more in depth information regarding
individual secondary teachers’ value of personalized learning and the challenges that
arise during implementation efforts along with the teachers’ response to challenges.
Personalized Learning Defined
The innovative instructional practice called personalized learning places the
individual student as the focus of all instructional design. The definition varies from
author to author; however, each definition includes student and teacher responsibilities to
orchestrate this mode of classroom instruction. According to the United States
Department of Education (2016b), personalized learning is defined as
instruction in which the pace of learning and the instructional approach are
optimized for the needs of each leaner. Learning objectives, instructional
approaches, and instructional content (and its sequencing) all may vary based on
learner needs. In addition, learning activities are meaningful and relevant to
learners, driven by their interests, and often self-initiated.
If learning is personalized for students it will provide an engaging curriculum that meets
the unique needs of each individual student; thus, intrinsically motivating students to take
greater ownership in their learning while providing a foundation for success in college
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and career experiences (Childress and Benson, 2014; Pane, et al., 2015; Prain, et al.,
2013; U. S. Department of Education, 2016a).
Content area teachers have an integral role in a personalized learning classroom
by designing a productive and learning focused environment, assisting students with goal
setting, leading multiple instructional approaches, providing student support and
guidance throughout the learning process, and providing timely feedback to students
(Childress & Benson, 2014; Deed, et al., 2014a; Pane, et al., 2015; Prain, et al., 2013;
Waldrop, et al., 2014). These responsibilities along with the personal investment of
students and teachers will generate a classroom that becomes an adaptive learning
community that provides a responsive, flexible curricular program while offering
individual freedom for students to grow as learners (Deed, et al., 2014a; Waldrip et al.,
2014). Rutledge, Cohen-Vogel, Osborne-Lampkin, and Roberts (2015) found that
personalized learning schools gain higher achievement, when all adults in the school
exhibit a socio-emotional responsibility to knowing students’ interests, learning about
student backgrounds, and investing in building a cohesive community that values student
voice (p. 1069).
Student voice is the element of personalized learning that empowers students to
take responsibility for their learning (Busher, 2012; Childress & Benson, 2014; Garn &
Jolly, 2014; Gillard, Gillard, & Pratt, 2015; Hopkins, 2014). A democratic learning space,
where teachers and students negotiate on learning space and instruction modes, is created
in a personalized learning environment (Deed et al., 2014b, p. 370). Learning becomes a
mutual responsibility of both the students and the teachers. Teachers are responsible for
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facilitation of the learning while students develop the ability to become independent
learners. For example, student voice allows students to make instructional choices to best
supports students’ learning styles such as: which groups to work with or the mode of
instruction that is most effective for that student (Basham et al., 2016, p. 134). Self-
regulating is an essential student characteristic in personalized learning environments
(Basham et al., 2016, p. 128). Student and teacher collaboration is essential in a
personalized learning classroom to successfully increase student motivation and
achievement (Deed, et al., 2014a; Prain, et al., 2013; Sahin & Kisla, 2016).
Purpose of Personalized Learning
Student motivation is key when maximizing learning to achieve greater results.
Pink argues that what motivates individuals is not created through compliance, but that
intrinsic motivation must be embraced to drive individuals towards success (Pink, 2009).
The future of education does not include learning more information and educators must
move beyond being “facilitators of learning” and move towards becoming “motivators of
purpose” (Gillard, Gillard, & Pratt, 2015, p. 3). Personalization has been a component of
higher achieving schools and found in the school design, initiatives, and serves as an
integral component of the school language (Rutledge, Cohen-Vogel, Osborne-Lampkin,
and Roberts, 2015). In their comparative case study, Rutledge, Cohen-Vogel, Osborne-
Lampkin, and Roberts (2015) found that commonalities of highly successful urban
schools include the orchestrated effort to build a community focused on relationships
with students, both culturally and academically (p. 1078). Administrators along with
teachers prioritized the personalized environment and believed it was a key element of
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their success. Self-efficacy in school leaders and teachers influences culture in the
classroom as it provides a means for adults to role model what it means to be a dynamic
learner for students (Alison, 2013; Rutledge, Cohen-Vogel, Osborne-Lampkin, and
Roberts, 2015).
Teachers are challenged with the problem of teaching a variety of learners in the
same classroom. Deci argued that this micromanagement of students creates a trend in
education to focus on average-ability students while leaving high-ability and low-ability
students underserved (Deci, 1972). In their research, Garn and Jolly (2014) focused on
the definition of motivation according to high ability students. Two major themes
emerged through their data analysis: the “fun factor of learning” and “rewards and
pressure of good grades” (Garn & Jolly, 2014, p. 15-17). The fun factor of learning
includes personalization and empowering student choice, which provides “optimal”
learning motivation. The research found that motivation was at its peak when learning
was personalized to meet student interests, built real world connections to student goals,
and provided conditions of choice in presentation and products (Garn & Jolly, 2014, p.
15-16). Rewards and the pressure of good grades were found as external motivators for
high-achieving students, which emphasized the effect of positive and negative motivators
in the learning process; the researchers deemed that this claim desired additional research
to have a clear understanding it their influence on learning (Garn & Jolly, 2014, p. 16-
20). Personalized learning also has the potential to greatly impact learning experiences
for students with disabilities. Deschler (2015) determined that using data to provide
students with disabilities with a personalized education has great potential in increasing
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academic achievement. Academic data points based on student mastery of learning
standards would allow teachers to individualize learning to match the educational needs
of the individual student. To meet the specific needs of individual learners, available data
must include learner variability, learning outcomes, performance measures, resources,
and instruction (Deschler, 2015, p. 74-75). Personalized instruction is created through the
dissection, then synthesis of student interests, academic data, and content standards. Only
through this process will the needs of low, average, and high ability learners in a single
classroom be met.
Student Engagement
Engagement can be defined as the action taken by students “to advance from not
knowing, not understanding, not having skill and not achieving to knowing,
understanding, having skills and achieving” (Reeve, 2013, p. 580). Student engagement
can be divided into three distinct types: behavioral, emotional, and cognitive (Harbour et
al., 2015; Reeve & Lee, 2014). Behavioral student engagement is the actions students
exhibit during the learning process. Emotional student engagement is the result of
students’ reactions to the learning standards and delivery of instruction. Cognitive student
engagement is the amount of effort and level of investment students have in the learning.
Together these types of student engagement can promote or distract from overall student
achievement (Harbour et al., 2015). School district visions focus on the individual learner
and how the prescribed educational program will lead the students to high academic
achievement. Personalized learning has the potential to transform student behavior to
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develop higher levels of student engagement and academic growth that is not evident in
other instructional modalities (Basham et al., 2016).
Behavioral student engagement is measured by the actions taken by the learner
throughout the learning process. Reeve (2013) described behavioral engagement as the
attention to learning and persistence throughout the learning process. “Interactive
engagement” is defined as the ongoing process of immediate feedback provided to the
learner from teachers and peers and encourages active participation between all parties
involved in the learning process (Tlhoaele et al., 2014). Research conducted by Tlhoaele
et al. (2014) found that behavioral engagement components such as active participation
and self-assessment activities promote higher levels of student achievement (p. 1029). In
Busher’s (2012) analysis of three studies on students’ perspectives of education, his
findings include that teacher support such as asking if there is anything that needs to be
discussed, helping with specific content, and positive praise along with feedback creates a
classroom culture of engagement according to students (p. 115-6).
Research conducted by Tlhoaele et al. (2014) illustrated that high achievement is
the result of learning tasks given to students that connect to real-world and possible future
careers. Thus, learning that shares a personal connection to student interests inspires
higher greater student engagement in the task. Emotional engagement focuses on the
positive emotions exhibited during the learning task (Reeve & Lee, 2014). Personal
connections to learning can only be constructed when teachers are able to build
relationships with students, igniting the emotional level of student engagement.
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Connecting content to a student’s life required knowing something about the
students, of course. This knowledge requires creating a relationship with each of
them. When we ask the right questions, treat them with respect, show empathy
(and sometimes sympathy) for their concerns, then can we create a bond that will
allow us to almost intuitively know how to make each of our lessons relevant to
our students’ lives. (Nordgren, 2013, p. 9)
Effort and investment in the learning, cognitive student engagement, can be
influenced by the amount of confidence students have in their ability to successfully
complete the assigned task along with the value of the task (Tlhoaele et al., 2014). Reeve
(2013) defined cognitive engagement as sophisticated strategic thinking instead of
commonly practiced learning strategies (p. 581). Nordgren (2013) iterates the importance
of challenging students to attain “high levels of understanding and application can change
their beliefs about themselves, leading to a self-fulfilling prophecy of success” (p. 9).
Reeve (2013) stated the existence of a fourth type of student engagement called
agentic engagement. A student initiated pathway to learning is exhibited when students
ask questions; express likes and dislikes; share interests, preferences, and opinions; and
offer suggestions (Reeve, 2013, p. 591). This mode of engagement is the result of self-
regulating behavior that is essential in personalized learning environments. Students that
show agentic engagement provoke an “ongoing series of dialectical transactions between
student and teacher” (Reeve, 2013, p. 580). Characteristics of agentic engagement
include students that are proactive, reflective, seek to personalize, and contribute to the
flow of classroom instruction (Reeve & Lee, 2014). When teachers offer a classroom that
24
promotes self-regulation, such as personalized learning environments, all four types of
engagement (behavioral, emotional, cognitive, and agentic) come together to increase
student academic growth and achievement (Reeve, 2013; Reeve & Lee, 2014).
Teacher Implementation
To increase student achievement and personalize learning, teachers must design
instruction to spark student curiosity, build confidence and provide learning opportunities
to share learning with peers (Tlhoaele et al., 2014, p. 1031). Personalized learning
requires teachers to become “designers or engineers of learning” and integrate unique
methods of instruction and assessment (Basham et al., 2016, p. 134). Personalized
learning focuses on the individual learner versus teaching to the average student (Basham
et al., 2016, p. 127). Basham et al. (2016) defined necessary characteristics of operational
personalized learning that must be implemented by teachers to generate student success
in the classroom. Essential characteristics include a highly self-regulated environment;
transparent, continual, and actionable data; continual feedback and weekly meetings;
integrating student voice; and multiple means of demonstrating mastery of learning
standards (Basham et al., 2016).
In a highly self-regulated environment, students are active participants in the
decision-making process and assume responsibility for their learning. The teacher’s role
is to design the learning environment by providing resources and scaffolds learning to
support self-regulation. Teachers also utilize student data along with student voice and
choice to design pathways for learning (Basham et al., 2016, p. 130). “Planning for
variability” is evident in the opportunities for multiple learning pathways available in this
25
environment (Basham et al., 2016). Instructional modeling is a strong proponent of a self-
regulated learning environment. Harbour et al. (2015) found that when teachers use
instructional modeling to demonstrate a skill or behavior it builds student confidence
engaging the learner influencing him or her to attempt higher order tasks (p. 6-8).
Instructional modeling produces the highest levels of student engagement when teachers
model their thought process by describing the decision-making process (Harbour et al.,
2015). Student self-regulation is supported through continual feedback from both
teachers and students (Basham et al., 2016; Harbour et al., 2015). The amount of
feedback correlates to the level of student behavior, academic performance, and time on
task; feedback is most effective when it is frequently given to students and promotes one-
on-one teaching opportunities with students (Harbour et al., 2015, p. 9-10). Student self-
reflection is also an effective component of self-regulating behavior. The use of exit
tickets is an example of integrating student reflection; students can use the exit ticket to
measure their progress and success on learning tasks (Basham et al., 2016, p. 133).
Transparent, continual, and actionable data is an essential component in a
successful personalized learning environment. Data can be extracted from national and
state standardized assessments, school district created performance tasks, teacher created
formative and summative classroom assessments, and digital programming (Abbott &
Wren, 2016; Lin et al., 2013; Shapiro & Wardrip, 2015). These data are used to analyze
the effectiveness of school-wide frameworks, spark conversations between school faculty
about areas for improvement, make instructional decisions based on learner progress, and
maximize opportunities for personalized learning (Abbott & Wren, 2016; Basham et al.,
26
2016; Lin et al., 2013). Shapiro and Wardrip (2015) described the purpose of data as a
means to “know what students know” (p. 128). Students and teachers are able to make
actionable decisions based on data from student progress and effort (Basham et al., 2016).
According to Abbott and Wren (2016) data driven decision-making has not been
universally successful since there is often lack of preparation of how to use the data by
administrators and teachers along with an unclear vision for its use (p. 38). For data
analysis in the instructional setting to contribute to student learning, a culture based on
clearly outlined learning standards, scoring consensus, and identifying strengths and
weaknesses for individual students must be embedded in the analysis (Abbott & Wren,
2016, p. 40). Shapiro and Wardrip (2015) stated in their research that teachers must
design conditions for data collection and then use the data for future instructional
planning (p. 146). Successful use of data requires expertise in data mining by all
stakeholders in the learning environment at the building and central office levels (Abbott
& Wren, 2016, p. 42-43).
When student voice is an integral part of classroom design, it influences the level
of student engagement. Basham et al. (2016) found that allowing students to demonstrate
mastery in multiple ways, especially if they have some choice, provides for higher levels
of engagement and more authentic and meaningful learning (p. 134). Utilizing the power
of student voice in the class changes lesson design. Students participate in writing goals
and choosing how evidence of content mastery will be shown while teachers become
learning coaches and find resources (technology, reading materials, experts) to provoke
student thought and curiosity (Basham et al., 2016, p. 130). In this classroom, teachers
27
must accept that they may no longer know more than the students in terms of learning
content as students are able to direct their learning; however, the benefit of this
environment is that teacher time is freed to support students that may need more one-on-
one support (Deed, et al., 2014a).
Learning Spaces
The design of the classroom can influence the learning that will take place.
Although whole class instruction is important when introducing new learning or when
addressing the entire class is necessary, classrooms that utilize an open flow concept and
include areas for small group work, individual work, and a variety of seating options
allows students to learn in their preferred environment (Basham et al., 2014; Deed et al.,
2014a; Deed et al., 2014b). Basham et al., (2014) emphasized the need for teachers to be
innovative in the design of learning spaces and test different designs to support increased
student achievement (p. 131). In a personalized environment, learning is active and
complex to support learner growth. Cooperative student grouping and skills based
groupings are complete the flexible learning design of classroom space (Basham et al.,
2016, p. 127).
Technology
Technology has changed the face of K-12 education and integrating technology
into instructional design provides opportunities for increased student engagement
throughout the learning process (Delgado, Wardlow, McKnight, & O’Malley, 2015;
Mitchell, Wohleb, & Skinner, 2016). In their research, Mitchell et al. (2016) studied
teacher perceptions on how technology can be utilized to improve instruction, including
28
the availability of web-based learning (p. 15). However, technology integration is not a
simple task and requires training in order to be effective. Technology is changing at such
a rapid pace that without training teachers are at a disadvantage when during the
implementation process (Yu & Okojie, 2017, p. 61-62). Mitchell et al. (2016) found that
teachers require diverse trainings matched to their level of teaching and technology
experience. As newer teachers are more aware of technology, they are able to utilize
technology more effectively in the classroom (p. 14). Not only does the amount of
teacher experience with technology influence effective classroom use, but teachers must
also be confident with how technology interacts with pedagogy. Yu and Okojie (2017)
stated in their research on the relationship between pedagogy and technology integration
that “the relationship between technology infusion and pedagogical knowledge represents
the foundation and a road map through which technology integration can be successfully
implemented” (p. 62). K-12 teacher training needs to include not only how to use
technology as a learning tool, but also how it is effectively used for meaningful learning
opportunities in specific content area (Mitchell et al., 2016, p. 17). School systems must
also play a supportive role in integrating technology, such as providing resources,
equipment, and training meeting the needs of individual teachers (Delgado et al., 2015;
Mitchell et al., 2016, p. 14).
The “Net Generation” student is internet literate and connected to the newest
technologies; she or he uses the internet as a tool for self-expression (Mitchell et al.,
2016, p. 14). The endless possibilities these tools have to influence student learning
makes technology an ideal pathway for personalized learning. Online learning has made
29
it possible for high school students to take courses not offered at their school, complete
coursework for credit recovery, and pursue college courses (Delgado et al., 2015, p. 398).
Computer based instruction has been used to attempt to personalize learning in the
classroom; however, it has been questioned if e-learning can facilitate a learning
environment that supports the needs of all learners (Delgado et al., 2015; Sahin & Kisla,
2016). Any web-based learning system must meet the needs of human participants
including gender; learning styles, characteristics, and needs; and individual preferences to
successfully personalize learning for students (Chen et al., 2016). Digital personalized
learning not only lacks hands-on problem solving, but also neglects the socio-emotional
development of learners (Basham et al., 2016, p. 128). If computer-based programs for
personalized learning cannot support the variety of needs of all learners, then potentially
a blended approach should be considered. A blended learning approach requires a
balance between teacher-direction and alternative learning resources; both modes of
instruction are needed to refine pedagogical practices (Deed et al., 2014b, p. 382).
However, teacher skillsets and beliefs about personalized learning instruction, along with
perceptions of student grouping, inhibits successful employment of the approach (Prain et
al., 2013, p. 658).
Challenges in Implementing Personalizing Learning
Personalized learning requires educators to attain a new instructional skillset if it
is to be successfully implemented (Bingham et al., 2016; Busher, 2012; Deschler, 2015;
Prain et al., 2013). The creation of a flexible curriculum centered on student interests,
academic data, available resources, and preferred learning styles that aims to meet desired
30
content standards is a difficult and time consuming process that is not easily attained. It is
essential that research-based guidance is utilized in the implementation of personalized
learning to prevent haphazard instruction that eventually becomes an “unrealistic fad in
education” (Basham et al., 2016, p. 127). Prain et al. (2013) stated that if teachers are
expected to provide personalized learning opportunities to students they need “expertise,
time, resources, and teamwork to develop a flexible curriculum that is adequately
structured in content, learning tasks, and adaptable classroom practices to engage all
learners and address contrasting learner needs” (p. 660). Even if teachers are given the
resources and support necessary, another challenge arises as students question the
“degree of control and choice” being offered and if the learning experience is indeed
personalized for individual students (Prain et al., 2013, p. 668).
The lack of availability of exemplar personalized learning models provides a
barrier in the implementation of this instructional mode by classroom teachers (Basham
et al., 2016; Bingham et al., 2016; Waldrip et al., 2014). There is a lack of understanding
of what it actually means to personalize. Similar to students, teachers need examples to
drawn from to aide and promote instructional design. Personalized learning requires
educators that are proficient in different perspectives of learning, data analysis, and
student grouping (Busher, 2012). The role of the teacher changes in a personalized
learning environment. Deschler’s (2015) research on personalized learning environments
for students with disabilities, found a need to re-define the roles of the classroom teacher
and the special education teacher to define learning for students with special needs (p.
75). Along with the potential strain in the instructional design process, time demands
31
throughout personalized learning design deters teachers from implementation. Pane et al.
(2015) stated in their research that one-half to two-thirds of teachers studied mentioned
time demands as problematic in personalized instruction; furthermore, the pressure to
meet learning objectives for assessments was reported as a minor or major obstacle by
40% of teachers in their study (p. 27). Complications also arise from the amount and type
of student data teachers receive to plan for all students, along with the management of
incoming data throughout the learning process (Bingham et al., 2016; Deschler, 2015).
Professional Development
Professional development is instrumental to the successful implementation of any
change in school culture if teachers are to operate and grow in a new learning
environment (Alison, 2013). Professional development may occur in different formats:
face-to-face learning opportunities (district sessions, peer observations, conversations
with building colleagues, conferences, Edcamps) and using technology such as social
media and blogging (Krutka, Carpenter, & Trust, 2017, p. 247). Krutka et al. (2017)
focused on frameworks that promoted teachers creating a personal learning network
(PLN) that allowed teachers to concentrate on personal and professional learning goals
(p. 247). Just as a personalized learning environment focuses on students’ interests and
academic needs, PLNs allow teachers to personalize their own learning endeavors. PLNs
engage teachers by allowing them to form their learning experiences based personal
needs and promote lifelong learning experiences for professional growth (Krutka,
Carpenter, & Trust, 2017, p. 248). This personalized engagement can also be directed
towards secondary teachers’ content area. Just as student engagement is vital to academic
32
achievement, teacher engagement in their content area is essential throughout
professional development sessions to benefit student learning (Alison, 2013).
The delivery of professional development has evolved along with the use of
technology to promote learning (Krutka et al., 2017; Seifert & Bar-Tal, 2017). Seifert and
Bar-Tal (2017) stated that “educators need settings for frequent sharing of knowledge,
construction of knowledge, continual professionalization, updating, regular advice and
support, and to introduce innovative pedagogical challenges” (p. 22). Their research
found that educators have a thirst for professional discourse and have multiple
motivations for participating in online professional networks such as meeting colleagues,
being part of a thinking team, research, fields of interest, discussing educational issues,
seeking professional information, and building social relationships (Seifert & Bar-Tal,
2017, p. 27). However, the recognition of online professional learning has yet to be
recognized as an effective means of professional growth. “Connected educators who are
innovative, inspired, or early adopters may find themselves working for institutions
whose professional development policies do not honor their dedication to growing into
their craft through social media and other relatively new interactions” (Krutka et al.,
2017, p. 251).
Personalized learning environments require teachers to multi-task throughout
instruction to promote student voice in learning. Rowan and Townend (2016) studied
teacher perceptions of working with diverse student populations including students with
disabilities and gifted students. Their findings stated that teacher behavior is connected to
teacher self-efficacy and that teachers feel they are underprepared to work with diverse
33
learners since there is not a one-size-fits-all instructional approach (Rowan & Townend,
2016). Their collected data serves as a reminder that professional development must
continually address instructional strategies for working in a diverse classroom so all
students, no matter their ability, can achieve (Rowan & Townend, 2016, p. 20). Just as
student learning does not happen in a one-size-fits-all classroom, teacher professional
development cannot be prescribed as a one-size-fits-all program for teacher growth.
Personalized Learning versus Content Standards
Personalized learning emphasizes student voice and choice throughout the
learning process. However, this becomes a concern when teachers have a dictated list of
standards that must be taught and assessed throughout the school year. Teachers are
concerned that personalized learning environments prohibit them from meeting
curriculum requirements and assessments standards. Questions also arise on if students
have the ability to make suitable content related decisions regarding their learning (Prain
et al., 2013, p. 668). Thus, where does the balance lie between personalized learning
philosophy and national and state testing on academic content standards? As states
continue to implement state assessments, teachers have to meet the expectation of
preparing students for standardized tests instead of allowing for student choice and self-
pacing (Johnsen, 2016, p. 73). Constraints due to testing expectations create conflict
between school measures of student success versus outside expectations (Bingham et al.,
2016; Johnsen, 2016). These constraints are not only due to mandated assessments, but
are also created by expectations of community stakeholders and post-secondary
institutions (Bingham et al., 2016, p. 26).
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Implications
Personalized learning has the potential to increase gains in student achievement
by implementing the student voice in curricular decisions, flexible groupings, data-based
decision-making, and technology integration (Basham et al., 2016; Busher, 2012;
Childress & Benson, 2014; Garn & Jolly, 2014; Gillard, Gillard, & Pratt, 2015; Hopkins,
2014). The five essential elements of personalized learning were the focus of this study to
target strengths and weaknesses within content areas to enable the creation of
personalized solutions to promote teacher growth with this mode of instruction and
engage students in their learning. Along with the essential elements, teachers’
expectancies and value of personalized learning in the instructional environment were
analyzed to determine measures that can be taken by school district leadership to support
teachers in implementing personalized learning and, in turn, increase overall student
achievement.
Summary
By utilizing innovative instructional methods such as personalized learning to
increase student engagement, teachers are better able to design learning opportunities for
students that not only emphasize current learning, but also solicit interests in college and
potential career goals (Nordgren, 2013; Reeve, 2013; Tlhoaele et al., 2014). However,
according to the research, teacher implementation of personalized learning brings about
challenges in effective curricular design: teachers lack of training in instructional
methods, in meaningful use of student data, in technology integration and in successful
personalized teaching models to reference (Abbott & Wren, 2016; Bingham et al., 2016;
35
Busher, 2012; Deschler, 2015; Prain, et al., 2013; Shapiro & Wardrip, 2015; Yu &
Okojie, 2017). These challenges support the need to investigate the challenges secondary
teachers have in implementing a personalized learning environment in their content area.
In this study, the research questions drove the investigation so that inconsistencies
amongst personalized learning implementation across content areas were discovered
using quantitative means and further analyzed using qualitative research.
A mixed-methods research study was conducted to define the challenges faced by
teachers while implementing a personalized learning environment in their content area.
Data collected from teacher interviews, classroom observation, and archival district
survey data on the essential elements of personalized learning (knowing your learners,
student voice and choice, flexible groupings and space, data-informed instruction, and
technology integration) were analyzed to gain insights into the strengths and weaknesses
of personalized learning implementation across content areas. Section 1 outlined the
benefits of a personalized learning environment and how that environment influences
student achievement. The problem, however, illustrated the lack of personalized learning
implementation across content areas.
The literature review examined how the implementation of a personalized
learning environment increases student engagement and achievement. Previous research
also illustrated challenges in successful implementation of personalized learning
including the balance between teacher and student responsibilities, lack of exemplary
models, available resources, and integrating content standards. The discussion of prior
36
research supports this study on the challenges of teacher implementation of personalized
learning across content areas.
The methodology presented in Section 2 will outline the mixed-methods approach
for this study. Archival survey data from the school district on the implementation of
each of the five personalized learning essential elements across content areas directed the
qualitative component of this research. Teacher interviews and classroom observations
provided an individual perspective on the challenges of implementing a personalized
learning environment. The data provided the overall picture of implementing
personalized learning: challenges, interventions, and successes. This information
provided the starting point for designing professional development to better support the
implementation of personalized learning in individual content areas in secondary
classrooms.
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Section 2: The Methodology
Research Design and Approach
A mixed-methods research study was conducted to better understand the
challenges secondary teachers have when implementing a personalized learning
environment in their content area. Creswell (2012) stated that mixed methods should be
used to build upon the strengths of both qualitative and quantitative research to gain a
better understanding of the phenomena being studied. An explanatory sequential mixed-
methods design allowed for the collection and analysis of the quantitative data to inform
the qualitative portion of the research, which elaborated upon the quantitative results
(Creswell, 2012; Creswell & Plano Clark, 2007). The quantitative component was
archival district survey data that described the implementation of each of the five
essential elements of personalized learning: knowing your learners, student voice and
choice, flexible groupings and space, data-informed instruction, and technology
integration. Two qualitative data components were used in this study: classroom
observation data and interview transcripts.
Participants
This research included teacher participants from a midwestern urban public
school district. I worked with the participating school district to recruit participants that
meet the criteria necessary for this study. The quantitative component of the research
included secondary teachers (Grades 7-12) who completed the school district’s 2017
strategic plan survey. This survey was completed by 475 K-12 staff members. Among
secondary teachers, there were 182 responses out of a sample size of 216. For this
38
research, data from departments with at least 10 staff members were used. Departments
meeting this criterion included the following: language arts (including world languages),
mathematics (including engineering and technology), performing and visual arts, science
(including health and physical education), and social sciences (including business).
For the qualitative component of this study, eight participants were selected, using
purposeful sampling techniques. Participants targeted through purposeful sampling
techniques can offer descriptive and detailed data (Creswell, 2012; Lodico, Spaulding, &
Voegtle, 2010; Merriam, 2009). The goal for purposeful sampling techniques was to
ensure the selection of secondary teachers (Grades 9-12) from each of the following
groups: language arts (including world languages), mathematics (including engineering
and technology), performing and visual arts, science (including health and physical
education), and social sciences (including business). These participants may or may not
have completed the 2017 strategic plan survey; however, their department was
represented in the quantitative data. Using a maximum variation sample provided a
diverse participant group that represented multiple content areas to provide the broadest
possible range of experiences (Lodico, Spaulding, & Voegtle, 2010; Merriam, 2009).
Researcher-Participant Relationship
It was essential that the researcher’s and participants’ roles in this research study
were clearly established. Each participant in the qualitative component of this study
received a clear and concise explanation of the purpose of the research (Creswell, 2012;
Merriam, 2009). By participating in this study, participants contributed their perspectives
on the challenges of implementing personalized learning in their content areas which
39
aided in the design of potential future professional development offerings. Participants
had the opportunity to partake in member checks to ensure internal validity (Merriam,
2009) after initial coding. I provided the participant with an interview transcript and a list
of emerging themes from qualitative data to examine to ensure that no misinterpretation
occurred throughout the data collection and analysis process.
Protection of Participants
It is the responsibility of the researcher to guarantee the protection of researcher
study participants (Lodico, Spaulding, & Voegtle, 2010). In the quantitative phase of this
study, all survey respondents’ names were removed from the data provided by the
research site. The data provided included overall building responses and responses by
secondary content area teachers. For the qualitative component of this study, an informed
consent form was provided to participants explaining the expectations of participants,
ensuring the confidentiality of data collected from the participant, and outlining the
security of their information (Lodico, Spaulding, & Voegtle, 2010; Merriam, 2009).
Participating teachers were given a pseudonym that reflected only their content area and
grade level.
Role of the Researcher
It is the responsibility of the researcher to provide a clear purpose of the research
to the participating school district and qualitative participants. I ensured confidentiality
for all parties involved throughout the data collection process. During the quantitative
component, I collaborated with school district leadership to collect all available survey
data. Throughout the qualitative research process, I conducted classroom observations.
40
During the observation process I used an observational protocol to record field notes
without becoming a distraction to the learning environment (Creswell, 2012; Lodico,
Spaulding, & Voegtle, 2010). Before interviews were conducted, I provided the teacher
participants with a copy of the interview questions. The interviews were recorded to aide
in the transcription of interview data. Since I, the researcher, am an employee of the
research site school district, it was essential that I did not have an evaluative professional
role over any participants to protect all rights of willing participants.
Data Collection
Quantitative Data Collection
The quantitative component consisted of archival data from a 2017 district
administered survey regarding the implementation of each of the five essential elements
of personalized learning. The Assistant Superintendent of Curriculum, Instruction, and
Assessment of the participating school district granted access to the 2017 strategic plan
data. The school district used an independent research firm to collect the strategic plan
survey data and organize the results by building, grade level, and secondary content
departments. This independent firm created a Likert-scale survey using questions written
by district stakeholders to collect strategic plan data. Although data was collected from
staff, students, and parents regarding the use of personalized learning, technology, and
literacy standards across the school district only the staff results were utilized for the
purpose of this study. The quantitative data was gathered from five questions focused on
the use of personalized learning in the classroom. The strategic plan survey questions
utilized for this research asks teachers to identity the number of times each of the five
41
essential elements of personalized learning (knowing your learners, student voice and
choice, flexible groupings and space, data-informed instruction, and technology
integration) was used in the classroom in a 10-day period. The survey questions that were
analyzed to answer the quantitative research question are provided in Appendix B. The
survey data provided the frequency that each of the five essential elements of
personalized learning was implemented in a 10-day period by teachers in each content
area department. Data from the various departments was analyzed to declare strengths
and weaknesses in overall personalized learning implementation.
Qualitative Data Collection
Classroom observations were scheduled to begin the qualitative research process.
Each observation was scheduled for a time where the participant planned to implement
personalized learning opportunities in the lesson design. An observational protocol
checklist, which can be found in Appendix D, was used to gather classroom data during
personalized learning opportunities from teacher participants. The use of an observational
protocol document including questions and space for field notes was used to focus the
observation on the needs of the study. (Lodico, Spaulding, & Voegtle, 2010, p. 116-119).
According to Merriam (2009) observations include the following elements: physical
setting, participants, activities and interactions, and conversations (p. 120-121). Data on
each of the five personalized learning essential elements (knowing your learners, student
voice and choice, flexible groupings and space, data-informed instruction, and
technology integration) was collected by recording the frequency that each element was
used in the lesson: never (not evident in the lesson), seldom (seen once or twice in the
42
lesson), often (used multiple times during the lesson), and consistently (fully integrated
into lesson). Space was available for additional notes on the integration of each
component. The observational protocol used for this research was adapted from an
observation tool created by the participating school district and the district’s 2016-2017
Strategic Plan. The data collected from the observational protocol built upon the
quantitative data from the teacher participant’s content area department.
A semi-structured interview was conducted after the classroom observation with
the participating teacher which lasted 15-30 minutes. The purpose of a semi-structured
interview was to allow for follow up questions throughout the interview process based on
the participants’ responses (Lodico, Spaulding, & Voegtle, 2010, p. 124). The interview
provided more in depth information regarding lesson design used in the observed class,
expectancies and values of personalized learning, along with teachers’ perspectives on
the challenges of implementing personalized learning in their classroom. The interview
protocol (Appendix E) for each participant initially consisted of the same questions and
was adjusted to attain more details based on activities and events seen during the
observation of the participant’s classroom. The quantitative data from the participant’s
content area department was included in the interview questions to gain a better
understanding of strengths and weaknesses in implementing personalized learning for
that specific content area. To ensure the validity of the interview data, participants were
given a transcript following the interview to review and ensure their responses have been
accurately recorded prior to the start of data analysis.
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Data Analysis
Quantitative Data Analysis
Statistical testing was conducted to analyze the use of personalized learning
elements by secondary teachers. In 2017, 216 secondary staff members were given the
strategic plan survey and 182 responses were collected. Percentages regarding the use of
each of the five essential elements were provided in the following categories: Grades K-
12, elementary (Grades K-6) teachers, middle school (Grades 7-8) teachers, and high
school (Grades 9-12). The 2017 data was further broken down by content area for
secondary teachers (Grades 7-12). Only the data for middle school and high school
teachers was utilized for the purposes of this study. Content areas in the data set include
language arts (including world languages), mathematics (including engineering and
technology), performing and visual arts, science (including health and physical
education), and social sciences (including business). The data was analyzed by content
area groupings using an analysis of variance test (ANOVA) which allowed for the
examination of the extent of implementation of personalized learning by content area
(language arts (including world languages), mathematics (including engineering and
technology), performing and visual arts, science (including health and physical
education), and social sciences (including business)) at the secondary level. G*Power
software was used to determine that the sample size was adequate for ANOVA testing.
ANOVA testing informed the researcher if any significant differences arose amongst
content area groups. If a significant difference was evident in the quantitative data, a
44
Tukey post hoc test was conducted to search for statistical significance between content
areas.
The analysis of the quantitative data informed the qualitative research
components. The quantitative data was used in the interview process to better understand
teachers’ expectancies on why specific elements of personalized learning are use the least
and the most for specific content areas. Expectancy–value theory states that an
individual’s choices are tied to their value of the task (Atkinson & Reitman, 1956). For
the purpose of this research, the task is the implementation of personalized learning.
Thus, teachers’ expectancies and values of personalized learning would influence their
survey responses. The quantitative data analysis provided a focus to both qualitative
components: classroom observations and interviews. Whereas the quantitative data
showed which personalized learning essential elements are most valued (more frequently
used) versus which essential elements are least valued (less frequently used), the
qualitative research component provided a better understanding of teachers’ expectancies
and values of each of the five personalized learning essential elements. The qualitative
research components provided a means to observe elements implemented into instruction
and an opportunity for participants to elaborate on their experiences with personalized
learning and state their point of view on their department’s quantitative data.
Two types of qualitative data were collected from each individual study
participant: a single classroom observation and a semi-structured interview taking place
shortly after the observation. The research of Basham et al. (2016) focused on the
development of an operational understanding of personalized learning, researchers first
45
conducted observations to develop initial themes for their research. After the
development of personalized learning themes, researchers then conducted interviews and
additional observations to support their initial findings (p. 129). Similar to the work of
Basham et al. (2016), the qualitative component of this research first consisted of a
classroom observation noting the use of each of the five personalized learning essential
elements.
Qualitative Data Analysis
Merriam (2009) stated that data analysis begins looking for data that is
“responsive to the purpose of the research” (p. 185). Research Question 2 was answered
through the observational data collection process. To prepare for the data analysis, the
observational protocol checklist was designed to separate each personalized element
(knowing your learners, student voice and choice, flexible groupings and space, data-
informed instruction, and technology integration). Using each personalized learning
element as category for qualitative data supported the need for categories to be mutually
exclusive, sensitizing, and conceptually congruent (Merriam, 2009). Themes were
generated from each category using the coded data from the observation. Lodico,
Spaulding, and Voegtle (2010) stated the development of themes will create an
organizational framework providing a more in depth understanding of the data (p. 185).
The observation data guided interview questions three and four which asked the
participant to elaborate on the planning and implementation of personalized learning
during the observed lesson. The developed themes instilled a focus for the direction of
the interview, where the participant was able to express their expectancies of the
46
effectiveness of personalized learning and the value of each component as it pertained to
student learning in their specific content area.
Interview transcripts from all participants were analyzed using an open coding
process to determine initial themes. Merriam (2009) described open coding as a process
where the researcher searches for qualitative data that may assist with the answers to
research questions. During the open coding process, I took notes regarding data that may
be useful in answering the research questions. Using an analytical coding procedure,
these initial codes were grouped into categories to look for emerging themes within the
qualitative data (Merriam, 2009, p. 180). Once the data analysis was completed and the
finding were prepared, participants had the opportunity to participate in member checks.
Merriam (2009) referred to member checks as means for internal validity (or credibility)
and called this process respondent validation (p. 217). This allowed for participants to
respond to the findings of the study and offer comments.
Throughout the qualitative data analysis process, triangulation of the multiple
sources of data was used to compare collected data. Triangulation of data increases
research credibility as multiple measures of data are compared (Lodico, Spaulding, &
Voegtle, 2010; Merriam, 2009). The quantitative and qualitative research data
materialized to provide results for the three research questions to better understand
teacher challenges in implementing personalized learning for their content area based on
teacher expectancies and values. Research Question 1 was addressed in the quantitative
data collection while Research Questions 2 and 3 were answered using qualitative
methods.
47
Results
This mixed-methods study on teachers’ challenges in implementing personalized
learning in content areas focuses on personalized learning practices of secondary teachers
(Grades 7-12). The qualitative portion of this study utilizes the 2017 strategic plan data
from the participating school district. Classroom observations and teacher interviews
comprise the qualitative component.
The data collected for this study was taken from a population of 165 secondary
teachers currently teaching in the selected content areas. The 2017 strategic plan data for
the qualitative component of this study included responses from the 138 survey
participants in the identified content areas: language arts (including world languages),
mathematics (including engineering and technology), performing and visual arts, science
(including health and physical education), and social sciences (including business). The
population and sample size is reported in Table 1.
Table 1
Population and Sample Size
Group name
Population
N
%
Language arts & world language
46
46
100
Mathematics & engineering
32
27
84.38
Performing & visual arts
17
10
58.82
Science & health
36
33
91.67
Social science & business
34
22
64.71
Total
165
138
83.64
Quantitative Data Analysis
The quantitative data was used to analyze the reported use of personalized
learning elements by secondary content teachers in each of the content area groupings:
48
language arts (including world languages), mathematics (including engineering and
technology), performing and visual arts, science (including health and physical
education), and social sciences (including business) which answers the quantitative
research question. A null hypothesis and non-directional alternate hypothesis was also
identified for this research.
RQ1 (Quantitative): How does the implementation of each of the personalized
learning essential elements differ between content area departments?
H0: There is no significant difference in the implementation of personalized
learning essential elements amongst secondary content area departments in a
midwestern urban school district.
HA: There is a difference in the implementation of personalized learning essential
elements amongst secondary content area departments in a midwestern urban
school district.
The quantitative research question was divided into five subquestions to allow analysis of
each personalized learning element: knowing your learners, student voice and choice,
flexible grouping and space, data-informed instruction, and technology integration.
Knowing Your Learners
The personalized learning element, knowing your learners, is defined as the
actions taken by teachers to gather information about each learner and how instruction is
planned to meet the needs of the learner. Examples of actions taken by teachers to know
their learners may include student inventories and formative assessments (Rutledge,
Cohen-Vogel, Osborne-Lampkin, and Roberts, 2015; The Institute for Personalized
49
Learning, 2015). The research question, null hypothesis, and non-directional alternate
hypothesis for the personalized learning element knowing your learners are as follows:
RQ1(a): How does the implementation of the personalized learning essential
element, knowing your learners, differ between content area departments?
H0(a): There is no significant difference in the implementation of the personalized
learning element, knowing your learners, amongst secondary content area
departments in a midwestern urban school district.
HA(a): There is a difference in the implementation of the personalized learning
element, knowing your learners, amongst secondary content area departments in a
midwestern urban school district.
The following 2017 strategic plan survey question focused on knowing your learners,
Thinking of the last 10 school days combined, how many days did you do an activity to
get to know your students, or intentionally make a classroom decision based on
information you have learned about your students? Table 2 summarizes data collected
from the responses to this question. The mean represents the average number of days (out
of a 10-day period) knowing your learners was implemented by each curricular group.
The table illustrates each content area grouping’s use of knowing your learners in
classroom instruction from largest to smallest mean. The percent that N is of the entire
sample is also provided.
50
Table 2
Personalized Learning Element: Knowing Your Learners
Mean
N
%
Standard
deviation
Social science & business
5.64
22
15.94
3.874
Language arts & world language
4.83
46
33.33
2.984
Mathematics & engineering
4.70
27
19.56
3.698
Performing & visual art
4.30
10
7.25
3.561
Science & health
2.94
33
29.91
2.783
Total
4.44
138
3.361
According to the mean, in 2017 the social science and business content area has the
highest reported days of implementation of the knowing your learners, whereas, the
science and health content area have the least number of days of implementation of the
same personalized learning essential element.
Table 3
One-Way Analysis of Variance of Knowing Your Learners by Content Area Departments
Knowing your Learners
Sum of
squares
df
Mean
square
F
Sig.
Between groups
114.728
4
28.682
2.661
.035
Within groups
1433.308
133
10.777
Total
1548.036
137
There exists a significant effect on the implementation of knowing your learners in
instruction according to teachers [F(4, 133) = 2.661, p = 0.035]. The significance value of
the F test is less than 0.05 (or 5%), which rejects the null hypothesis, H0(a), and accepts
the alternate hypothesis, HA(a). Thus, there is a significant difference between the days of
implementation of the personalized learning element of knowing your learners amongst
content areas.
51
Post hoc comparisons using the Tukey test was conducted to look for significance
between content area groupings. Table 4 summarizes the results of the Tukey test the
personalized learning element knowing your learners. Content area departments were
assigned the following codes: 1 – language arts (including world language), 2 –
mathematics (including engineering and technology, 3 – performing and visual arts, 4 –
science (including health and physical education, 5 – social science (including business).
The comparison of science (including health and physical education) and social science
(including business), groups 4 and 5 has a p-value less than 0.05 (p = 0.027) which notes
a significance in how these content areas implementation of knowing your learners. Thus,
the social science content area subgroup implements knowing your learners statistically
significantly more frequently in a 10-day period than the science content area subgroup.
Table 4
Multiple Comparisons Content Area Department Implementation of Knowing Your
Learners (Tukey Post Hoc)
Mean
95% Confidence level
Department
(I)
Department
(J)
difference
(I-J)
Std. error
Sig.
Lower
bound
Upper
bound
1
2
.122
.796
1.000
-2.08
2.32
3
.526
1.145
.991
-2.64
3.69
4
1.887
.749
.092
-.18
3.96
5
-.810
.851
.876
-3.16
1.54
2
3
.404
1.215
.997
-2.96
3.76
4
1.764
.852
.239
-.59
4.12
5
-.933
.943
.860
-3.54
1.67
3
4
1.361
1.185
.781
-1.92
4.64
5
-1.336
1.252
.823
-4.80
2.13
4
5
-2.697a
.904
.027
-5.20
-.20
aThe mean difference is significance at the 0.05 level.
52
Student Voice and Choice
Student voice and choice refers to the opportunities given to students to make
decisions regarding their personal learning paths. Examples of student voice and choice
includes allowing students to alter assignments to make them more meaningful, as well
as, providing encouragement for students to take learning risks (Basham et al., 2016;
Busher, 2012; Childress & Benson, 2014; Garn & Jolly, 2014; Gillard, Gillard, & Pratt,
2015; Hopkins, 2014; The Institute for Personalized Learning, 2015). The research
question, null hypothesis, and non-directional alternate hypothesis for the personalized
learning element student voice and choice are as follows:
RQ1(b): How does the implementation of the personalized learning essential
element, student voice and choice, differ between content area departments?
H0(b): There is no significant difference in the implementation of the personalized
learning element, student voice and choice, amongst secondary content area
departments in a midwestern urban school district.
HA(b): There is a difference in the implementation of the personalized learning
element, student voice and choice, amongst secondary content area departments in
a midwestern urban school district.
The following 2017 strategic plan survey question focused on student voice and choice,
Thinking of all your preps combined, in a typical school day period, how many days did
you implement voice and choice in student assignments/activities in your classroom?
Table 5 summarizes data collected from the responses to this question. The mean
represents the average number of days that student voice and choice was implemented by
53
each curricular group. The table illustrates each content area grouping’s use of student
voice and choice in classroom instruction from largest to smallest mean calculated out of
a 10-day period along with the percent that N is of the entire sample.
Table 5
Personalized Learning Element: Student Voice and Choice
Mean
N
%
Standard
deviation
Language arts & world language
5.37
46
33.33
2.969
Performing & visual art
5.00
10
7.25
4.028
Social science & business
4.68
22
15.94
3.138
Mathematics & engineering
3.19
27
19.56
2.760
Science & health
2.61
33
29.91
2.536
Total
4.14
138
3.126
According to the mean, in 2017 the language arts and world language content area has the
highest reported days of implementation of the student voice and choice, whereas, the
science and health content area has the least number of days of implementation of the
same personalized learning essential element.
Table 6
One-Way Analysis of Variance of Student Voice and Choice by Content Area
Departments
Student voice and choice
Sum of
squares
df
Mean
square
F
Sig.
Between groups
185.658
4
46.415
5.352
.001
Within groups
1153.443
133
8.673
Total
1339.101
137
There exists a significant effect on the implementation of student voice and choice in
instruction according to teachers [F(4, 133) = 5.352, p = 0.001]. The significance value of
the F test is less than 0.05 (or 5%), which rejects the null hypothesis, H0(a), and accepts
the alternate hypothesis, HA(a). Thus, there is a statistically significant difference between
54
the days of implementation of the personalized learning element of voice and choice
amongst content areas.
Post hoc comparisons using the Tukey test was conducted to look for significance
between content area groupings. Table 7 summarizes the results of the Tukey test for
student voice and choice. The comparison between language arts (including world
language) and mathematics (including engineering and technology), groups 1 and 2 has a
p-value less than 0.05 (p = 0.022) which notes a statistically significant difference in how
much the teachers in these content areas implement student voice and choice. In addition,
language arts (including world language) and science (including health and physical
education), groups 1 and 4 have a p-value less than 0.05 (p = 0.001) which also notes a
statistically significant difference in how much these two content areas implement
student voice and choice. Thus, the language arts content area subgroup implements
student voice and choice statistically significantly more frequently in a 10-day period
than the mathematics and science content area subgroups.
55
Table 7
Multiple Comparisons of Content Area Department Implementation of Student Voice and
Choice (Tukey Post Hoc)
Mean
95% Confidence level
Department
(I)
Department
(J)
difference
(I-J)
Std. error
Sig.
Lower
bound
Upper
bound
1
2
2.184a
.714
.022
.21
4.16
3
.370
1.028
.996
-2.47
3.21
4
2.764a
.672
.001
.91
4.62
5
.688
.763
.896
-1.42
2.80
2
3
-1.815
1.090
.459
-4.83
1.20
4
.579
.764
.942
-1.53
2.69
5
-1.497
.846
.396
-3.84
.84
3
4
2.394
1.063
.167
-.55
5.33
5
.318
1.123
.999
-2.79
3.42
4
5
-2.076
.811
.084
-4.32
.17
aThe mean difference is significance at the 0.05 level.
Flexible Groupings and Space
The personalized learning element, flexible groupings and space, focuses on the
use of classroom space and student groupings. This includes modifications teachers make
to the learning environment for instructional activities and decisions that are made when
assigning student groups for learning (Basham et al., 2014; Deed et al., 2014a; Deed et
al., 2014b; The Institute for Personalized Learning, 2015). The research question, null
hypothesis, and nondirectional alternate hypothesis for the personalized learning element
flexible groupings and space are as follows:
RQ1(c): How does the implementation of the personalized learning essential
element, flexible groupings and space, differ between content area departments?
56
H0(c): There is no significant difference in the implementation of the personalized
learning element, flexible grouping and space, amongst secondary content area
departments in a midwestern urban school district.
HA(c): There is a difference in the implementation of the personalized learning
element, flexible grouping and space, amongst secondary content area
departments in a midwestern urban school district.
The following 2017 strategic plan survey question focused on flexible groupings and
space, Thinking of all your preps combined, in a typical 10 school day period, how many
days did you implement activities with flexible student groupings or flexible classroom
space? Table 8 summarizes data collected from the responses to this question. The mean
represents the average number of days that flexible groupings and space was
implemented by each curricular group out of a 10-day period. The table illustrates each
content area grouping’s use of flexible groupings and space in classroom instruction from
largest to smallest mean along with the percent that N is of the entire sample.
Table 8
Personalized Learning Element: Flexible Groupings and Space
Mean
N
%
Standard
deviation
Language arts & world language
4.89
46
33.33
3.328
Social science & business
4.41
22
15.94
3.712
Performing & visual art
3.90
10
7.25
3.725
Mathematics & engineering
3.52
27
19.56
3.412
Science & health
3.36
33
29.91
3.131
Total
4.11
138
3.404
According to the mean, in 2017 the language arts and world language content area has the
highest reported days of implementation of the flexible groupings and space, whereas, the
57
science and health content area has the least number of days of implementation of the
same personalized learning essential element.
Table 9
One-Way Analysis of Variance of Flexible Groupings and Space by Content Area
Departments
Flexible groupings and space
Sum of
squares
df
Mean
square
F
Sig.
Between groups
58.318
4
14.579
1.268
.286
Within groups
1529.052
133
11.497
Total
1587.370
137
There does not exist a significant effect on the implementation of flexible groupings and
space in instruction according to teachers [F(4, 133) = 1.268, p = 0.286]. The significance
value of the F test is greater than 0.05 (or 5%), which accepts the null hypothesis, H0(a),
and rejects the alternate hypothesis, HA(a). Thus, there is no statistically significant
difference between the days of implementation of the personalized learning element of
flexible groupings and space amongst content areas.
Data-Informed Instruction
Data-informed instruction is defined at the ongoing actions by teachers to assess
student learning and adjust instruction based on the assessment. This element may be
implemented through formative assessment throughout a lesson or pre-assessments to
help determine student groupings by ability or interests (Abbott & Wren, 2016; Deschler,
2015; Lin et al., 2013; Shapiro & Wardrip, 2015; The Institute for Personalized Learning,
2015). The research question, null hypothesis, and non-directional alternate hypothesis
for the personalized learning element data-informed instruction are as follows:
58
RQ1(d): How does the implementation of the personalized learning essential
element, data-informed instruction, differ between content area departments?
H0(d): There is no significant difference in the implementation of the personalized
learning element, data-informed instruction, amongst secondary content area
departments in a midwestern urban school district.
HA(d): There is a difference in the implementation of the personalized learning
element, data-informed instruction, amongst secondary content area departments
in a midwestern urban school district.
The following 2017 strategic plan survey question focused on data-informed instruction,
Thinking of all your preps combined, in a typical 10 school day period, how many days
did you implement data informed activities? Table 10 summarizes data collected from the
responses to this question. The mean represents the average number of days that data-
informed instruction was implemented by each curricular group. The table illustrates each
content area grouping’s use of data-informed instruction in classroom instruction from
largest to smallest mean out of a total of 10 days. The percent that N is of the entire
sample is also provided.
Table 10
Personalized Learning Element: Data-Informed Instruction
Mean
N
%
Standard
deviation
Social science & business
4.91
22
15.94
3.741
Language arts & world language
3.65
46
33.33
2.643
Performing & visual art
3.60
10
7.25
2.989
Mathematics & engineering
3.48
27
19.56
3.203
Science & health
2.88
33
29.91
2.870
Total
3.63
138
3.050
59
According to the mean, in 2017 the social science and business content area has the
highest reported days of implementation of data informed activities, whereas, the science
and health content area has the least number of days of implementation of the same
personalized learning essential element.
Table 11
One-Way Analysis of Variance of Data-Informed Instruction by Content Area
Departments
Data Informed Activities
Sum of
squares
df
Mean
square
F
Sig.
Between groups
55.243
4
13.811
1.507
.204
Within groups
1218.909
133
9.165
Total
1274.152
137
There does not exist a significant effect on the implementation of data-informed
instruction according to teachers [F(4, 133) = 1.507, p = 0.204]. The significance value of
the F test is greater than 0.05 (or 5%), which accepts the null hypothesis, H0(a), and
rejects the alternate hypothesis, HA(a). Thus, there is no statistically significant difference
between the days of implementation of the personalized learning element of data
informed activities amongst content areas.
Technology Integration
The personalized learning element of technology integration refers to the
utilization of technology to make learning more meaningful to students. Technology
integration includes the type of technology used along with how the technology enhances
student learning. Examples of technology includes the incorporation of learning software
to practice specific content skills, using technology to model phenomena, or the use of
technology by students to create and present their learning (Delgado et al., 2015: Mitchell
60
et al.,2016; The Institute for Personalized Learning, 2015). The research question, null
hypothesis, and non-directional alternate hypothesis for the personalized learning element
technology integration are as follows:
RQ1(e): How does the implementation of the personalized learning essential
element, technology integration, differ between content area departments?
H0(e): There is no significant difference in the implementation of the personalized
learning element, technology integration, amongst secondary content area
departments in a midwestern urban school district.
HA(e): There is a difference in the implementation of the personalized learning
element, technology integration, amongst secondary content area departments in a
midwestern urban school district.
The 2017 strategic Plan survey question focused on knowing your learner, Thinking of all
your preps combined, in a typical 10 school day period, how many days did you integrate
technology into your lessons? Table 12 summarizes data collected from the responses to
this question along with the percent that N is of the entire sample is also provided. The
mean represents the average number of days out of 10 that technology integration was
implemented by each curricular group.
61
Table 12
Personalized Learning Element: Technology Integration
Mean
N
%
Standard
deviation
Social science & business
7.68
22
15.94
2.801
Language arts & world language
6.93
46
33.3
2.847
Mathematics & engineering
5.96
27
19.56
3.546
Performing & visual art
4.40
10
7.25
3.340
Science & health
4.09
33
29.91
2.754
Total
6.00
138
3.251
According to the mean, in 2017 social science and business content area has the highest
reported days of implementation of the technology integration, whereas, the science and
health content area has the least number of days of implementation of the same
personalized learning essential element. The table illustrates each content area grouping’s
use of student voice and choice in classroom instruction from largest to smallest mean.
Table 13
One-Way Analysis of Variance of Technology Integration by Content Area Departments
Technology integration
Sum of
Squares
df
Mean
Square
F
Sig.
Between groups
248.333
4
62.083
6.883
.000
Within groups
1199.667
133
9.020
Total
1448.000
137
There exists a significant effect on the implementation of technology integration in
instruction according to teachers [F(4, 133) = 6.883, p = 0.000]. The significance value of
the F test is less than 0.05 (or 5%), which rejects the null hypothesis, H0(a), and accepts
the alternate hypothesis, HA(a). Thus, there is a statistically significant difference between
the days of implementation of the personalized learning element of integrating
technology amongst content areas. Social science and business had the highest
62
implementation of technology in a 10-day period while science and health reported the
least number of days implementing the same element.
Post hoc comparisons using the Tukey test was conducted to look for statistical
significance between content area groupings. Table 14 summarizes the results of the
Tukey test for technology integration. For technology integration three content area
comparisons have a p-value less than 0.05. Thus, there exists a statistically significant
difference in how often technology is integrated into instruction in a 10-day period:
language arts and science (groups 1 and 4) has a p-value of 0.001, performing and visual
arts and social science (groups 3 and 5) has a p-value of 0.038, and science and social
science (groups 4 and 5) has a p-value of 0.000. Thus, the language arts content area
subgroup implements the personalized element of technology integration statistically
significantly more frequently in a 10-day period than the science content area subgroup.
The data also supports that the social science content area subgroup integrates technology
statistically significantly more frequently in a 10-day period than the science and arts
content area subgroup.
63
Table 14
Multiple Comparisons of Content Area Department Implementation of Technology
Integration (Tukey Post Hoc)
Mean
95% Confidence level
Department
(I)
Department
(J)
difference
(I-J)
Std. error
Sig.
Lower
bound
Upper
bound
1
2
.972
.728
.670
-1.04
2.99
3
2.535
1.048
.117
-.36
5.43
4
2.844a
.685
.001
.95
4.74
5
-.747
.779
.873
-2.90
1.41
2
3
1.563
1.112
.625
-1.51
4.64
4
1.872
.779
.121
-.28
4.03
5
-1.719
.863
.275
-4.10
.67
3
4
.309
1.084
.999
-2.69
3.31
5
-3.282a
1.145
.038
-6.45
-.11
4
5
-3.591a
.827
.000
-5.88
-1.30
a The mean difference is significance at the 0.05 level.
Qualitative Data Analysis
Two qualitative research questions were used to investigate teachers’
expectancies, values, and challenges as they pertain to the implementation of
personalized learning across content areas. The qualitative research questions are as
follows:
RQ2 (Qualitative): How do teachers’ expectancies and value of personalized
learning influence the extent to which they implement personalized learning
essential elements (knowing your learners, student voice and choice, flexible
groupings and space, data-informed instruction, and technology support) in their
content areas?
RQ3 (Qualitative): What do teachers perceive as challenges in implementing a
personalized learning environment in their content area?
64
Classroom observations and interviews were used in the qualitative research process.
Participants in the qualitative component of this study represent each of the
following content area groupings: language arts (including world languages),
mathematics (including engineering and technology), performing and visual arts, science
(including health and physical education), and social sciences (including business). I
worked with the cooperating school district to gain access to a list of potential study
participants. Twenty-one invitations to participate were sent to potential teacher
participants. Twelve teachers responded to the invitation and eight teachers volunteered
to participate in the qualitative portion of this study. There was at least one volunteer
participant for each content area grouping. Each participant allowed the researcher to
observe one class. The length of each class varied from 40-80 minutes depending on the
structure of the course. Within 48 hours after the classroom observation, the teacher
partook in a 10-question interview with the researcher.
After the initial coding, the observation and interview coded data was sorted into
each of the five personalized learning elements: knowing your learner, student voice and
choice, flexible groupings and space, data-informed instruction, and technology
integration. Within each element the data was further sorted to answer each of the two
qualitative research questions.
Knowing Your Learners
Knowing your learners represents the actions teachers take to understand the
needs of each learner. Essential to knowing your learners is building relationships with
student. In all eight observed lessons, teachers checked in on students during independent
65
or small group work time using the teacher-student relationship to promote learning.
During the interviews one teacher from each of the following categories: language arts
(including world languages), mathematics (including engineering and technology),
performing and visual arts, and science (including health and physical education)
mentioned that the amount of help that is offered to students is determined by the student
skill level on the lesson objectives. Two of the eight teachers also mentioned that in their
courses students are grouped by skill level; these teachers were in the science (including
health and physical education) and the mathematics (including engineering and
technology) content areas.
Focusing on RQ2, all eight teacher participants expressed high expectancies and
values on this personalized learning element and its power to influence student
achievement. During the interviews, four of the eight participants described knowing
your learner as the means that teachers identify where students are in terms of content
knowledge and to help students move forward. Both teacher participants in the
mathematics (including engineering and technology) subgroup explained that when
teachers tailor curriculum to individual students’ needs to meet how students learn best
increases student engagement and investment in learning. One teacher in this subgroup
further explained during the interview that teachers must have an awareness of which
students need additional help, time, and resources including knowledge of individualized
educational plans (IEPs) and accommodations for students. Other implementation efforts
mentioned in this interview include understanding class dynamics and planning
instruction accordingly, and assigning learning tasks based on students’ skills and
66
strengths. Table 15 summarizes teacher implementation efforts (RQ2) that were either
observed during the lesson or mentioned during the interview for the personalized
learning element knowing your learners.
Table 15
Content Area Implementation of Knowing Your Learners
Content area
Total
participants
Implementation of personalized learning
element (number of mentions)
Language arts
(including world language)
1
• Use of student skills from previous
learning (1)
• Assistance provided to students based on
skill level (1)
Mathematics
(including engineering and
technology)
2
• Course set by student skill level (1)
• Lesson design based on class dynamics (1)
• Awareness of student individualized
education plans (1)
• Assistance provided to students based on
skill level (1)
Performing and visual arts
2
• Teacher chooses performance task for
students (1)
• Assistance provided to students based on
skill level (1)
• Teacher-student conversations (1)
Science
(including health and
physical education)
2
• Course set by student skill level (1)
• Assistance provided to students based on
skill level (1)
• Teacher-student conversations (1)
Social science
(including business)
1
• Resources given to students to assist in
research efforts (1)
• Assistance provided to students based on
skill level (1)
RQ3 investigates challenges as perceived by teachers when implementing
personalized learning within their content area. During the observed lessons, little
evidence was collected where teachers gathered data on student learning needs for that
specific lesson. Only in one lesson from the science (including health and physical
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education subgroup) did the teacher ask students about their preferred learning mode for
the day’s learning. However, casual conversations between teachers and students during
lesson transitions along with learning checks-in throughout student work time were
observed in every lesson. In the interview, a participant from the performing and visual
arts subgroup stated that “knowing your learner requires a lot of human management.”
One participant in the science (including health and physical education) subgroup felt that
this element needed further clarification: does it focus on students’ interests or how
students learn best?
Time for adequate lesson preparation, grading, and data analysis was another
concern with knowing your learners. All eight teachers shared concerns of meeting the
needs of students that learn differently and have different needs. The same participant in
the science (including health and physical education), as well as, a teacher in the
language arts (including world language) subgroup voiced the concern that with high
student caseloads of 130 or more students, it takes time to get to know about all students.
This teacher found it difficult to address the needs of so many students while making sure
students do not “fly under the radar.” Some students demand more teacher attention
during the learning process while other students do not seek teacher guidance or ask
questions during class. With limited class time, it is a challenge to work with all students
individually and allocate time to help everyone grow as a learner.
Student Voice and Choice
Student voice and choice emphasizes student empowerment to take ownership on
their learning experiences. During the interviews, one participant stated that student voice
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and choice encourages “students take risks throughout the learning process.” Teachers
illustrated student voice and choice in multiples forms during the eight observed lessons
such as providing students different ways to learn the content, allowing students to pick
partners or topics to study, promoting student creativity, and how to show mastery of
content. During the observations, two of the eight teachers allowed students to choose
their mode of learning; these teachers were from the language arts (including world
languages) and science (including health and physical education) content areas. A teacher
from the science (including health and physical education) content area grouping
provided three options for learning during the observed lesson. Students were able to
choose a short lecture from the teacher, an online learning activity, or conduct an
experiment to meet the lesson objective. Both teachers in the performing and visual arts
content area shared in the interview that student voice and choice is the focus of
promoting student creativity. One of these teachers stated “voice and choice promotes
creative expression and decision making throughout learning” which encourages students
to interpret assigned and add a personal touch. During the observation of a teacher from
the social sciences subgroup, students were to choose from a given list of topics and
create a presentation on that topic. Students were also allowed to advocate for a topic of
their own choosing. A teacher in the mathematics subgroup mentioned during the
interview she offers choice by encouraging students to attempt challenging problems and
allowing students to investigate topics that would support multiple ways to solve the
same problem. Table 16 summarizes teacher implementation efforts (RQ2) that were
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either observed during the lesson or mentioned during the interview for the personalized
learning element student voice and choice.
Table 16
Content Area Implementation of Student Voice and Choice
Content area
Total
participants
Implementation of personalized learning
element (number of mentions)
Language arts
(including world language)
1
• Student choice in writing focus (1)
• Options for presentation of final work (1)
• Encourage students to take risks in a safe
environment (1)
• Student choice in how to record data (1)
Mathematics
(including engineering and
technology)
2
• Daily goal setting for project work (1)
• Students encouraged to take risks on
assigned problem (1)
• Students encouraged to attempt additional
problems as time allows (2)
• Resources available for student use (2)
Performing and visual arts
2
• Student input on tasks and projects (2)
• Student self-expression and creativity
encouraged (2)
• Student development of personal artistic
style (2)
• Student interpretation of performance
(voice, emotion, blocking) (1)
Science
(including health and
physical education)
2
• Students given choice of what skills to
practice (1)
• Students given options of learning mode
with options to change mode, if necessary
(1)
Social science
(including business)
1
• Students allowed to choose topic to
research (1)
• Students allowed to choose the mode of
project presentation (1)
Although teachers expressed value in incorporating voice and choice in their
classroom (RQ2), responses to teacher perceptions of challenges in implementing
personalized learning (RQ3) determined that this element is difficult to incorporate for
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multiple reasons. A teacher in the science (include health and physical education)
subgroup questioned how you get students to authentically make the choice that is best
for him or her without letting peer influence get in the way. The need to follow course
standards was a concern of five of the eight participants. How do you allow student
choice when there is a specific curriculum sequence that needs to be followed? Questions
similar to this were mentioned by teachers in the language arts (including world
languages), mathematics (including engineering and technology), science (including
health and physical education) subgroups. The same concern was mentioned pertaining to
advanced placement (AP) course standards along with dual enrollment coursework
standards written by local universities. In the mathematics and engineering group,
teachers stated that it is essential to build a foundation of basic content skills which is
why the allotment of student choice is difficult to implement. Seven of the eight
participants stated that time to create meaningful options for students, write multiple fair
assessments, and manage student progress is essential to effective implementation of
student voice and choice. A participant in the social science (including business)
subgroup mentioned the challenge concerning the assessment process and having a fair
assessment for students that have different tasks, roles, and responsibilities. In addition to
the creation of a fair assessment process, this participant stated that the management of
keeping track of students working on a variety of tasks was expressed to be as equally
challenging for teachers. These concerns are part of a larger challenge--time.
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Flexible Groupings and Space
Flexible groupings and space promotes manipulation of the classroom
environment that support student learning. A teacher in the language arts (including
world languages) subgroup described flexible groupings and space as “providing a safe
environment that allows students to take risks and have opportunities to engage in
authentic, real-world learning experiences.” Use of this personalized learning element
was observed in seven of the eight observed classrooms supporting high teacher
expectancies (RQ2) in the usefulness of flexible groupings and space for instructional
purposes; the classroom that did not utilize this element during the observation was in the
performing and visual arts content area grouping. One teacher participant from the
following content areas (five of the eight teachers in total): language arts (including
world languages), mathematics (including engineering and technology), science
(including health and physical education), and social sciences (including business)
allowed students to choose how to work (individually, with a partner, or small group) at
some point during the class period. Students in six of the observed classrooms (one
participant from language arts (including world languages), one participant from
mathematics (including engineering and technology), both participants from science
(including health and physical education), one participant from performing and visual
arts, and one participant from the social sciences (including business) content area
grouping) were permitted to choose the location that learning would take place. Locations
included different areas in the classroom, hallways, and visiting another classroom to
gather data. A teacher in the science (including health and physical education) subgroup
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had the classroom divided into areas for whole class lectures, small group work, and lab
activity space. This classroom showed the highest implementation of flexible groupings
and space out of all observed classrooms. The classroom that did not utilize flexible
groupings and space was in the performing and visual arts subgroup and the observed
lesson was an introduction to the next unit of study. However, during the interview this
teacher did report that flexible groupings and space would be utilized in future lessons.
Table 17 summarizes teacher implementation efforts (RQ2) that were either observed
during the lesson or mentioned during the interview for the personalized learning element
flexible groupings and space.
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Table 17
Content Area Implementation of Flexible Grouping and Space
Content area
Total
participants
Implementation of personalized learning
element (number of mentions)
Language arts
(including world language)
1
• Students allowed choice of where to sit in
classroom (1)
• Students allowed to select partners (1)
• Visited another classroom for in school
field trip (1)
Mathematics
(including engineering and
technology)
2
• Student may choose partners or small
groups for projects (1)
Performing and visual arts
2
• Students allowed choice of where to sit in
classroom (1)
• Students spread out in classroom and
hallway to prepare their performance (1)
• Students allowed to use classroom
throughout the day as it fits in their
schedule to complete work (1)
Science
(including health and
physical education)
2
• Students allowed choice of where to work
in classroom (2)
• Students may choose partners or small
groups for learning tasks (2)
• Large and small group learning
opportunities (2)
Social science
(including business)
1
• Students allowed choice of where to sit in
classroom (1)
• Students may choose partners or small
groups for projects (1)
Even though flexible groupings and space was utilized in most observed
classrooms, responses to RQ3 during teacher interviews provided multiple challenges
with this personalized learning element. When asked why his content area reported
flexible groupings and space to be the lowest implemented personalized learning element
in the quantitative data, a participant in the social studies (including business) subgroup
stated “Teachers may be scared to give up the level of control to allow for flexible
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groupings and space. It is easier to avoid the unknowns.” Five of the eight teachers
reported that available learning space and classroom size was a constraint in the
implementation of this element; these teachers were from the following content area
groupings: language arts (including world languages), mathematics (including
engineering and technology), science (including health and physical education), and
performing and visual arts. A participant in the social science (including business)
subgroup stated that in order to implement flexible groupings and space it would require
a redesign of the classroom environment to include a variety of student seating options
that would support group work. A teacher in the mathematics (including engineering and
technology) subgroup reported challenges due to the design of the class structure:
students were regrouped each day so group tasks could not be continued into the next
class period and short class periods (35 minute classes). Even though one teacher in
science (including health and physical education) showed high implementation of flexible
groupings and space, another teacher in the same subgroup shared a concern regarding
class size when it came to this element. This teacher stated that a large class size made it
difficult to monitor students working in different spaces or multiple small groups. A
teacher in the language arts (including world languages) subgroup explained that with
more funding field trip experiences would allow for student learning outside the confines
of a single classroom.
Data-Informed Instruction
Data-informed instruction provides teacher insight into student understanding of
content so instruction can be adjusted to increase achievement. In seven of the eight
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observed classrooms, teachers checked in with students throughout the lesson to ask and
answer questions, offer suggestions, redirect when necessary, and diagnose
misconceptions which show high teacher expectancies in the use of this personalized
learning element per RQ2. This was not observed in one classroom in the performing and
visual arts content area subgroup; however, this teacher stated in the interview that she
frequently checks in with students to monitor their progress. This teacher explained that
data-informed instruction looks different in different content areas, especially in
performing and visual arts. In this teacher’s content area, data is not quantitative but is
collected through observations of student behavior and work along with discussions
between student and teacher. Observational data collection was also mentioned by a
teacher in the science (including health and physical education) subgroup. During teacher
interviews, a teacher in the mathematics (including engineering and technology) and a
teacher in the language arts (including world languages) subgroups mentioned pre-testing
students to collect data to best meet the learning needs of students regarding appropriate
content and student groupings. Table 18 summarizes teacher implementation efforts
(RQ2) that were either observed during the lesson or mentioned during the interview for
the personalized learning element data-informed instruction.
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Table 18
Content Area Implementation of Data-Informed Instruction
Content area
Total
participants
Implementation of personalized learning
element (number of mentions)
Language arts
(including world language)
1
• Teacher meets with students throughout
learning process to answer questions and
provide suggestions (1)
• Individual student feedback (1)
Mathematics
(including engineering and
technology)
2
• Teacher asks questions throughout
learning process to promote in depth
inquiry (2)
• Teacher checks-in with students
throughout work time (2)
• Teacher analysis of student work (2)
Performing and visual arts
2
• Teacher checks-in with students
throughout work time (1)
• Conversational versus empirical (2)
Science
(including health and
physical education)
2
• Teacher checks-in with students
throughout work time (2)
Social science
(including business)
1
• Teacher checks-in with students
throughout work time (1)
Data-informed instruction may be considered by some as an essential part of
instructional planning. Responses to RQ3 found that there are challenges in effective
implementation of this personalized learning element. Time constraints was a common
thread in the concerns with data-informed instruction by six of the eight teachers
participants; these teachers were in the following content area groupings: language arts
(including world languages), mathematics (including engineering and technology),
performing and visual arts, and science (including health and physical education). Large
class size was another challenge expressed by four teachers in the language arts
(including world languages), mathematics (including engineering and technology), and
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science (including health and physical education) content area groupings. Even though
pretesting is a means of data-informed instruction, a teacher in the mathematics
(including engineering and technology) subgroup shared that pretesting is time
consuming. He stated “I am already behind in grading. How can I use this to make
decisions when I am behind in posting grades?” A high number of students makes it
difficult to keep up with formative assessment in class. There is not enough time to
collect, grade, and analyze student data along with other teacher responsibilities. Face to
face conversations with students takes much classroom time and takes away from
instructional time. A teacher in the language arts (including world languages) subgroup
asked how pre-testing can be an effective strategy when introducing foundational content.
The same teacher stated that pre-testing can be time consuming and “not a joyful
experience for students.”
Technology Integration
Teacher expectancies and the use of technology integration to influence
instructional planning, as noted in RQ2, seems to be not in the potential benefits of
technology in student learning, but in what does effective use of technology look like in
the classroom setting for that particular content area. Technology integration was evident
in five of the eight observed classrooms. It was not utilized in one classroom in the
science (including health and physical education) subgroup and both classrooms in the
performing and visual arts subgroup. However, the degree to which it was implemented
varied per content area. A teacher in the language arts (including world languages)
allowed students to use cell phones to record videos and take pictures to support their
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journalistic writing. A teacher in the mathematics (including engineering and technology)
subgroup had students using technology to present their findings and record notes on a
proposed problem to share with the rest of the class. Another teacher in the same
subgroup had students working with a variety of technological tools in their construction
task. A teacher in the science (including health and physical education) had students
recording data using spreadsheets and other students working with an online simulation.
A teacher in the social sciences (including business) subgroup had students using the
Internet to collect research on a chosen topic of study. Other meaningful uses of
technology mentioned during teacher interviews included using dynamic software for
simulations, creating products using 3-D printers and laser engravers, producing videos
for demonstration purposes, to publish writing, and using apps geared towards specific
content areas. Table 19 summarizes teacher implementation efforts (RQ2) that were
either observed during the lesson or mentioned during the interview for the personalized
learning element technology integration.
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Table 19
Content Area Implementation of Technology Integration
Content area
Total
participants
Implementation of personalized learning
element (number of mentions)
Language arts
(including world language)
1
• Use cell phones to take pictures and record
interviews (1)
• Use of Turnitin.com for student writing (1)
• Use of Google suite and iWork
applications (1)
Mathematics
(including engineering and
technology)
2
• A variety of technology tools available for
student use (2)
• Use of dynamic software for mathematical
modeling (1)
Performing and visual arts
2
• Teacher use of technology to show
examples of artistic work (1)
Science
(including health and
physical education)
2
• Use of recordings for demonstrations (1)
• Use of online learning modules and
simulations (1)
• Data collection (1)
• Use of cell phones to assist with lab
experiences (1)
• Use of Google Suite for assignments (1)
Social science
(including business)
1
• Research conducted online (1)
• Technology used for creation of final
products (1)
However, even with multiple examples of how technology integration influence
classroom practices the definition of technology integration was unclear to some
participants. Teachers in the social sciences (including business) and science (including
health and physical education) subgroups shared during their interviews that even though
technology has the potential to enrich student learning they feel the high numbers of
technology integration implementation in the quantitative data are skewed due to a
misunderstanding of what is expected in terms of technology integration. Both teachers
mentioned that some members of their department consider posting documents on an
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online learning platform as high use of technology whereas others may use technology to
provide more in depth study of content standards. Questions that arose in both interviews
included: Does technology integration represent only teacher use of technology during
instruction? Does posting documents including notes and assignments on an online
learning platform count as technology integration? Should the focus be on student use of
technology for collaborative learning experiences or for student created documents and
presentations? What if technology is only used for data collection, but not for other
aspects of learning? Does it include technology to be used a tool for substitution or does
it include technology being used for collaborative purposes?
Even though the participating school district provides a laptop for every student in
Grades 7-12, concerns were expressed that some technology that could enhance student
learning was not compatible with the school issued MacBooks. This concern was
reported by a teacher participant in the science (including health and physical education)
since available technology including heart rate monitors and pedometers were only IBM
compatible. Teachers in this subgroup and the performing and visual arts subgroup
reported that if students are working on the laptop then they are not necessarily physically
engaged in the learning for that content area.
Although it was evident in the qualitative data that teachers find value the
addition of personalized learning elements in classroom instruction, multiple challenges
have risen during implementation efforts. Table 20 summarizes the eight participants’
responses to RQ3 regarding content area implementation challenges for personalized
learning as presented in the previous discussion of each personalized learning element.
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Table 20
Teacher Perceptions of Personalized Learning Elements Implementation Challenges
Personalized learning element
Challenges
(number of mentions)
Knowing your learners
• Time for information management (8)
• High student caseloads (2)
• Definition (interests vs. learning styles) (1)
Student voice and choice
• Standards-based curriculum (5)
• Dual-enrollment & AP course guidelines (2)
• Time to plan for multiple learning pathways (7)
• Assessment practices for multiple learning pathways (1)
• Student management (7)
Flexible groupings & space
• Teacher comfort level (2)
• Class size (5)
• Available space (5)
• Funding for field trip experiences (1)
Data-informed instruction
• Class size (4)
• Time to analyze data and plan accordingly (6)
Technology integration
• Lack of understanding by teachers of expectations for
technology integration (i.e. document creation and data
collection vs. student collaboration) (3)
• Desired technology not compatible with school issued
laptops (1)
• Best practices do not always include technology (3)
Professional Development
During the interviews, participants were also asked questions regarding their
perceptions of professional development and how it has supported their personalized
learning endeavors. The questions were the following:
1. What professional development opportunities were most valuable in assisting
your implementation efforts for personalized learning?
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2. In your opinion, what professional development or resources would be most
valuable in helping you continue to integrate personalized learning in your
content area?
The purpose of these two questions was to inform the project in Section 3 by providing
next steps in assisting teachers to overcome the challenges of implementing personalized
learning in their content areas.
Content-Specific Professional Development
All eight participants stated in their interviews that there is a need for content-
specific professional development and examples of personalized learning in their content
areas. A teacher in the science (including health and physical education) subgroup stated
“there is a need for personalized learning philosophy and strong content knowledge to
make personalized learning an effective instructional tool.” He discussed that teachers
must be masters of their content in order to create different pathways for students that are
engaging and meet the learning standards for that course. Six of the eight teachers (all
participants except for teachers in the performing and visual arts subgroup) stated that
guidance from the district’s personalized learning collaborators assisted in their lesson
revisions to include personalized learning.
Professional Development Design
Three of eight participating teachers felt that an all-day personalized learning
immersion experience was beneficial, but that the follow up support by the coaches
afterwards was the most helpful. A teacher in the mathematics (including engineering and
technology) content area stated that professional development needs to be personalized
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for the teacher and “the teacher needs to feel what it is like to be a student in a
personalized learning environment.” This teacher also expressed that personalized
learning “requires creativity and resourcefulness” which takes time to do. Both teachers
in the performing and visual arts category expressed a need to meet with professionals in
their content areas outside of the school district to promote growth in their instructional
practice. Participants in the language arts and social sciences subgroups all expressed that
they looked towards social media and the Internet for professional learning experiences.
Discussion
The quantitative portion of this mixed methods study focused on how the
implementation of each of the five personalized learning essential elements (knowing
your learners, student voice and choice, flexible groupings and space, data-informed
instruction, and technology integration) differed between five content area groupings:
language arts (including world languages), mathematics (including engineering and
technology), performing and visual arts, science (including health and physical
education), and social sciences (including business). The quantitative data was collected
from the participating school district and organized in an Excel spreadsheet. The
Statistical Package for Social Scientists (SPSS) software was used to analyze the
district’s strategic plan data informing the frequency content area departments use
personalized learning elements in a 10-day period. A one-way ANOVA was used to test
for significance in implementation frequency and the Tukey post-hoc test was used to
examine significance between content area implementation of each element. The null
hypothesis was rejected and alternate hypothesis accepted for three of the five elements:
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knowing your learners, student voice and choice, and technology integration. The Tukey
test found significant differences between content areas for each of these three
personalized learning elements.
The qualitative portion of this study was two-fold: a classroom observation and
teacher interview. Eight teachers participated in the qualitative portion of this research
from the following content areas: one participant from language arts (including world
languages), two participants from mathematics (including engineering and technology),
two participants from performing and visual arts, two participants from science
(including health and physical education), and one participant from social sciences
(including business). An observational protocol was used in each classroom observation,
which allowed for consistency between observations. After the classroom observation
teachers partook in a 10-question interview with the researcher. During the interview
teachers were asked to define personalized learning, share how it was or was not used in
the observed lesson, express their opinions on the quantitative results for their content
area group, and discuss professional development opportunities. Each interview was
recorded and transcribed by the researcher. All participating teachers received an email
offering the opportunity to participate in a member check to review themes taken from
their interview. Merriam (2009) explained that member checks allowed research
participants to examine “preliminary analysis” for validity (p. 217). Using an Excel
spreadsheet, the qualitative data was organized by content area grouping, as well as,
organized by personalized learning element then coded for emerging themes. The
qualitative findings support that teacher expectancies and value of personalized learning
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do influence the extent to which they implement personalized learning essential elements
in their content areas (RQ2). The dynamics of instruction is dependent on content
knowledge, understanding of student needs, and individual teacher confidence in their
craft. The qualitative findings support that there is not a one-size-fits-all set of
instructional practices that meets the needs of all students and suits the skillset of all
teachers. Personalized learning elements are better suited in some content areas than
others and their use needs to be strategically implemented to be most effective in
promoting student growth. Even though content areas utilize personalized learning
elements differently, there exists common challenges perceived by teachers as they
implemented personalized learning in their content area (RQ3). Time and availability of
resources were common threads throughout discussions of how personalized learning
elements are used in the classroom. Teacher understanding was another common thread
throughout the interview data. Personalized learning philosophy must clearly be
explained with content specific examples in order for meaningful and effective
implementation.
Triangulation of the strategic plan, observation, and interview data was used in
this mixed methods research study. Merriam (2009) stated that triangulation using
multiple data points increases the credibility of research by cross checking data across
data sources. The data resources for this mixed-methods research were school district
strategic plan data, classroom observations, and participant interviews. The quantitative
data was used to inform the qualitative research component. An examination of
similarities and differences amongst content area implementation of personalized
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learning in the qualitative data was used to support or disconfirm quantitative data.
During the interview process, participants were asked to elaborate on why a certain
personalized learning element was rated as the highest implemented for their content area
and why another personalized learning element was rated as the lowest implemented for
their content area.
The quantitative data for knowing your learners showed a significant difference in
the number of days out of a 10-day period in how often content area groupings
implemented this personalized learning element. Social sciences (including business)
reported the highest implementation, whereas, science (including health and physical
education) reported the lowest implementation of this element and post hoc comparisons
reported a significant difference in how these two content area groups implemented this
element. As stated in the qualitative analysis, one teacher questioned the definition of
knowing your learners: does it mean knowing students’ interests or how students learn
best? Even though all eight participants expressed the importance of knowing your
learners and demonstrated some aspect of knowing your learners in the observed lessons,
participants expressed challenges with the implementation of this element including high
student caseloads and time.
Language arts (including world languages) reported the highest implementation of
student voice and choice out of a 10-day period, whereas, science (including health and
physical education) reported the lowest implementation. Post hoc comparisons reported a
significant difference between the implementation of student voice and choice in between
the language arts and science content area groupings, as well as, between language arts
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and mathematics content area groupings (mathematics including engineering and
technology report the second lowest implementation of student voice and choice). This
quantitative data is supported by the qualitative data for this personalized learning
element. A participant in the language arts (including world languages) group reported
that skills, such as critical thinking and questioning, allowed for student choice in their
content area. On the other hand, participants in both the science (including health and
physical education) and mathematics (including engineering and technology) stated that
student voice and choice was the least implement in their content areas due to the
standards-based nature of these content areas. One teacher in the mathematics content
area group addressed how content standards and the need to teach foundational skills
caused this content area to be more “rigid” than other content areas which impacted low
implementation of student voice and choice. A teacher in the science content area group
mentioned that course standards, advanced placement, and dual enrollment coursework
detracts from the implementation of student voice and choice since the curriculum can be
scripted with a specific delivery plan. “It is hard to fit content and implement voice and
choice in a predetermined timeline.”
There was no significance in the frequency of implementation of flexible
groupings and space across content areas in the quantitative data. Language arts
(including world languages) reported the highest implementation, whereas, science
(including health and physical education) reported the lowest implementation of this
element. During the observations, five of the eight teachers utilized some element of
flexible groupings and space in their class. However, during the interviews it was
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expressed that classroom space is often a challenge when implementing this personalized
learning element.
The quantitative data for data-informed instruction did not show a significant
difference in the number of days out of a 10-day period in how often content area
groupings implemented this element. Social science (including business) reported the
highest implementation, whereas, science (including health and physical education)
reported the lowest implementation of this element. Teachers in both these content areas
used data-informed instruction in a similar manner during the observed lesson. These
teachers checked on individual students or groups of students during independent work
time to monitor progress and provide feedback. A challenge that arose in multiple
interviews is that this personalized learning element requires a lot of time to meet with
students individually or groups of students to give them meaningful feedback.
Technology integration was the highest reported implemented element out of a
10-day period in four of the five content area groupings in this one-to-one Apple laptop
high school. This access to technology along with the school district’s expectations for
implementation may influence the quantitative results. Social sciences (including
business) reported the highest implementation, whereas, science (including health and
physical education) reported the lowest implementation of this element. Post hoc
comparisons did report a significant difference between the implementation of
technology integration between the social science and science content area groupings, the
social science and performing and visual arts content areas, as well as, the language arts
and science content areas. Through the qualitative data collection process it was found
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that some content area department leaders have encouraged teachers to explore
technology use in the classroom and teachers in different content areas implement
technology differently. It was mentioned during the interview process that the
quantitative data may be skewed due to misconceptions of what is expected in terms of
technology integration. Is it teacher use or student use of technology? If it is student use
of technology, to what degree should students be using technology: general software
applications, student collaboration, or real-world simulations? A challenge that was
addressed regarded best practices for individual content areas. For instance, a teacher in
the performing and visual arts content area stated that personalized learning “elements
used or not used depends on the class. For example, there is no technology in pottery, but
technology is used in graphic art.”
The quantitative data illustrated that personalized learning elements promote
student learning differently across content areas. However, the qualitative data shows that
implementation of each element depends on the teacher expectancies of the effectiveness
of each element in instruction just as heavily as it does on the content area it is being
implemented in. Together, the quantitative and qualitative data provided evidence of the
effectiveness of personalized learning in content areas, struggles in implementation, and
next steps to overcome challenges and barriers in the implementation of personalized
learning.!
Limitations
Limitations of this study are two-fold: the groupings of content areas and lack of
equal representation from content areas. The science and health and physical education
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subgroup would be best split into individual subgroups. As mentioned in an interview
with a teacher in this group, the high report of technology integration in the quantitative
data is most likely skewed by the science department since this participant did not feel
that technology was a strength in the health and physical education department. Another
limitation is that all content area groups had two participants with the exception of
language arts (including world languages) and social sciences (including business).
Although teachers from the world language and business departments were represented in
the quantitative data, they were not represented in the qualitative data collection.
Conclusion
The findings support that meaningful implementation of personalized learning
elements are unique to each content area. One participant described personalized learning
as an instructional method that requires “throw[ing] away what you think you know
about it and ... opening up to the possibilities.” This teacher shared that she was hesitant
to implement aspects of personalized learning until a student approached her regarding
this mode of instruction in her content area. Differences that influence implementation
includes the nature of the course (standards driven, advanced placement or dual
enrollment), time constraints in the course structure or teacher time to plan innovative
instruction, and available resources such as technology and professional development
support.
The analysis of the quantitative data taken from the participating school district’s
strategic plan showed that three personalized learning elements had statistically
significant differences in their implementation across content areas. Knowing your
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learners had a statistically significant difference between the social science and science
content area groupings, with social science implementing this element at a higher
frequency than science. During the observations and interviews, the lack of
understanding of what knowing your learners actually means was apparent. Does this
element mean knowing your learners on a personal level such as their interests or goals?
Or does it represent knowing what educational supports are necessary for this student to
succeed in a specific content area?
Two personalized learning elements had multiple statistically significant
differences between content areas according to the Tukey analysis: student voice and
choice and technology integration. Student voice and choice had a significance between
language arts and mathematics, as well as, language arts and science. Whereas
technology integration had a statistically significant difference between language arts and
science, social science and performing and visual arts, and social science and science.
The difference in the implementation of student voice and choice may be due to the
nature of a standards-based science curriculum that must meet the needs of advanced
placement and dual enrollment courses. Even though science was paired with health and
physical education for the content area groups, the participant from the health and
physical education department felt that health and physical education provided options
for student voice and choice while the science teacher participant voiced concerns over
having to align with a set curriculum due to standards. This was also a concern voiced by
both teachers in the mathematics (including engineering and technology) content area
group. Language arts and social science teacher participants discussed multiple ways that
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technology can be used to enhance student learning during the interview. However, the
teachers in science (including health and physical education) felt that either teachers were
not clear on the depth of implementation of technology that should be counted (data
collection or student collaboration) or that technology was not useful for their particular
course, “if students are on a device they are not moving.” In the performing and visual
arts curriculum, not all courses utilize technology as part of their best practices for
instruction. As one participant stated, “it really depends on the course.”
Looking at each content area separately, the strengths and weaknesses of
personalized learning implementation efforts are apparent. In language arts (including
world languages), the highest implemented element as reported in the quantitative data
was technology integration. The teacher participant in this content area stated that
teachers in this department frequently use turnitin.com to check student writing. During
the observation, this teacher allowed student to take pictures and make recordings as part
of their data collection for the assigned writing task. This content area reported data-
informed instruction as the least implemented element in a 10-day period which was
supported by comments during the interview: “If you consider pre-testing a way to
implement data-informed instruction, does it make sense to pretest when a student has
either read or not read the novel?”
Technology integration was also reported to be the highest implemented
personalized learning element in mathematics (including engineering and technology).
However, it ranked only third highest for this element of the five content area groupings
in the quantitative data. Both participating teachers for this content area mentioned the
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usefulness of dynamic software in teaching their content and the high use of
technological tools to help students innovate such as 3-D printers, laser engravers, and
tools for construction. Student voice and choice was reported as the lowest implemented
element for mathematics and engineering which was supported in the interview data.
Teachers in this area spoke of how the standards based nature of their courses influence
how much choice students can actually have. In this area, teachers felt that a foundation
must first be built before students can explore further content. The department’s structure
has also set up a course sequence that builds such a foundation one course at a time.
The performing and visual arts content area reported the student voice and choice
as their highest implemented element in the quantitative data, which was supported by
both participating teachers during the interview process. Teacher in this content area
defined creativity as student voice and choice since every artistic decision a student
makes stems from the creative process. The element implemented the least per the
quantitative data was data-informed instruction. During the interviews, these participants
stated that it isn’t that data-informed instruction does not happen in their content area, but
that it is observational data not empirical data that is collected.
The science (including health and physical education) content area group reported
the lowest implementation for all five personalized learning elements. As mentioned
earlier, the Tukey post hoc analysis reported significant differences in implementation
between this department and others. Elements of personalized learning were observed in
both participants’ classrooms during observations. Nonetheless, challenges in
implementation efforts of the elements of personalized learning were voiced during the
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interviews. Technology integration was reported as the highest implemented element in a
10-day period in the quantitative data for this content area. Yet, both teachers shared
concerns with this element. One participant mentioned that the technology for her courses
was not compatible with the school issued laptops. The other participant mentioned that
he felt that his department colleagues may be confused about what technology integration
is expected: data collection or using technology for more in depth student learning. Both
teachers felt that the quantitative data for this element may be skewed. As mentioned
earlier, teachers in this content area grouping placed different expectancies and values on
the element of student voice and choice, which was reported as the lowest implemented
element for this content area grouping. The health and physical education teacher
reported value in allowing students to design personal workout routines while the science
teacher felt constraints placed upon him due to the standards driven nature of his content
area.
The social science (including business) content area also reported technology
integration as its highest implemented element in the quantitative data. The participant in
this content area mentioned during the interview that department leadership has focused
on technology integration in curricular planning. Students are also highly encouraged to
conduct research and prepare presentations using their school provided laptop, which
may also influence this data trend. Flexible groupings and space was the element with the
least reported implementation in a 10-day period for this content area. During the
observation, the teacher allowed students to form their own groups and sit wherever they
wanted in the classroom. However, in his interview he stated that not all teachers in his
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content area can easily give up the teacher control required by this personalized learning
element.
Personalized learning is an innovative instructional technique that encourages
teachers to provide students with options that will make learning more meaningful for
that student. Yet, this instructional strategy looks different across content areas and
amongst teachers in that content area which is evident in the analysis of data in this
mixed methods study. The project in Section 3 will outline a professional development
action plan to support teachers as they implement a personalized learning environment
for their specific content area.
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Section 3: The Project
Introduction
In Section 3, I will introduce the project based on the findings of my research
study: a professional development program incorporating personalized learning elements
and content area training for teachers. Two goals define this project: to educate teachers
in personalized learning strategies and to support teachers in becoming masters in their
content area. Participants reported that, in order for personalized learning to be effective,
teachers must understand the philosophy behind this instructional strategy and have
opportunities to expand their content area knowledge. This yearlong professional
development program will consist of an initial training session on the five personalized
learning elements (knowing your learners, student voice and choice, flexible grouping
and space, data-informed instruction, and technology integration). Teachers will also
have additional opportunities to observe these elements being implemented in
classrooms, content area focused training with master teachers and community mentors,
and ongoing support from a district personalized learning expert. This experience will be
tied to teachers’ professional learning goal for the academic year in order to hold teachers
accountable for the opportunities offered in this program.
Rationale
The professional development program proposed for this project was designed
according to the needs identified by the eight participants during the interviews as part of
the qualitative data collection process. The professional development needs included the
following: full-day immersion experiences, a foundation of personalized learning
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elements, content area learning, time to design instruction, and ongoing support during
implementation. Participants stated that professional development should also be
personalized to meet the learning needs of individual teachers and should not be a one-
size-fits-all approach. If teachers are to incorporate personalized learning strategies in
their instruction, then professional development must use personalized learning concepts
and model such strategies for teachers. This project will outline a two-fold professional
development experience: content area training that is personalized to meet the needs of
each teacher and training sessions focused on individual personalized learning elements.
This professional development experience will also include ongoing support from a
district expert in personalized learning strategies to help teachers in the design and
successful implementation of personalized learning in the classroom.
Review of the Literature
To aid in the design of an effective professional development program to address
teachers’ challenges when implementing personalized learning in their content areas, a
review of the literature was conducted using multiple databases: Education Source,
ERIC, Google Scholar, ProQuest Central, and SAGE Journals. The following search
terms were used: content-specific professional development, instructional coach,
pedagogical content knowledge, personalized professional development, professional
development models, teacher collaboration, and teacher learning. Each content area
studied for the qualitative component of this research was also included in the search
process: engineering, health and physical education, language arts, mathematics,
performing arts, science, social science, and visual arts. The searches for literature on
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these content areas were in combination with professional development and pedagogical
content knowledge.
Teacher Efficacy
Teachers orchestrate the dynamics that take place during instruction in the
classroom. In order for a teacher to grow in his or her ability to implement a personalized
learning environment for students, it is vital that professional development opportunities
build teacher efficacy in delivering this mode of instruction. It vital that teachers are
given support as they challenge their uncertainties regarding change in their practice
(Dixon, Yssel, McConnell, & Harden, 2014). According to Dixon et al. (2014), the
reason that teachers my take part in professional learning experiences and yet return to
their classroom without application of this knowledge may be due to a lack of teacher
efficacy. Furthermore, Dixon, et al. (2014) explained in their research that teachers who
are uncomfortable with their own content knowledge are lesson likely to be flexible with
diversifying instruction for students. Franklin, Jarvis, and Bell (2017) stated that if
teachers will not successfully implement instructional practices that are mandated without
adequate resources and training. Professional development is vital to educators
throughout the entire span of their career. Lowrie (2014) stated a needs exists to build
efficacy in new graduates to help them overcome feelings of inadequacies, stress,
workplace challenges which can be done through workplace professional development.
The development of a teachers’ capacity for change will only occur if professional
learning is designed to “support teachers to understand how the interdependent elements
of curriculum, assessment, teaching, learning and classroom management can work
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together in an effectively differentiated classroom” (Frankling et al., 2017). A teacher’s
path towards a fundamental change in instructional practices, including personalized
learning strategies, is a complex process that is comprised of teachers taking charge of
such modifications along with constant reflection in how these changes fit with current
instructional practices and classroom dynamics (Dixon et al., 2014; Frankling et al. 2017;
Van Den Bergh, Ros, & Beijaard, 2015).
Personalized Professional Development
Classroom teachers understand that students may have a diverse learning needs.
The same could be said of teachers when it comes to professional development
opportunities. Gynther (2016) stated “personalization is education, where participants
have different learning objectives, depending on their learning needs” (p. 17). When
professional learning is customized to the needs of each teacher more possibilities exist
for differentiation and individualization which has the potential to influence academic
gains for students (Gynther, 2016). So why is it that professional trainings are not
personalized? Lowrie (2014) explained the frustration of the “limited capacity for
personalized professional learning” and stated that the personal theories of teachers must
be taken into consideration if changes are mandated in the educational setting; therefore,
a “bottom-up approach to professional learning” must be implemented (p. 40). Change
occurs through the empowerment of all individuals involved. If district leadership,
administrators, and teachers collectively work together to design professional
development practices that are personalized for individuals, professional autonomy will
increase teacher efficacy as teachers take responsibility for their own professional growth
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and change within their educational environment (Clarke, 2016; Lowrie, 2014;
Matherson & Windle, 2017).
Just as students learn differently, teachers learn differently. Thus, professional
development cannot be designed as a one-size-fits-all program (Burbank, Bates, & Gupta,
2016; Lowrie, 2014; Van Den Bergh et al., 2015). If teachers are to implement
personalized learning in their classrooms they need to have experienced personalized
learning themselves. Pasatta, Hamilton and DeDoes (2017) described a personalized
professional development program for teachers that encompassed 120 hours of teacher
learning for selected participants. This ongoing professional development program
provided teachers with opportunities to experience what students do in a personalized
environment along with support to design personalized activities for students. The
premise behind the design of this professional learning program was the “need to provide
teachers with experiences that mirror the type of personalized learning we - and they -
hope to see in their classrooms” (Pasatta et al., 2017, p. 67). As part of this professional
development program, small groups of teachers were presented with a real-world
scenario to study to allow teachers to experience road blocks and triumphs in the learning
process similar to what students may experience in the classroom setting (Pasatta et al.,
2017, p. 65). Time for reflections was also embedded into teacher learning experiences to
allow teachers to consider how they felt, what they learned, and next steps. Reflections
took multiple forms: journals, groups discussions, and writing prompts (Pasatta et al.,
2017, p. 67).
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In addition to personalizing professional development for teachers, Burbank et al.
(2016) stated a “multilevel focus” approach to teacher support emphasizing content and
pedagogy is necessary for outcomes resulting in effective teaching and learning practices
(p. 57-58). This supports the need for all teachers to be given opportunities to grow as an
expert in their content area along with growing in pedagogy. Jao and McDougall (2016)
stated that professional development initiatives including “job embedded learning,
collaborative (peer) inquiry, attention to student performance, institutional and
administrative support, provision of time and other resources, and commitment to
continuous long-term engagement” are most effective if they are effectively woven
together into a single cohesive program for teacher learning (p. 557).
Ongoing professional development is essential to provide teachers with continued
support as instructional strategies are learned, practiced, and reflected upon. Dixon et al.
(2014) stated that single presentation professional development offerings provide only a
snapshot of knowledge to teachers which is not adequate for implementing change in the
classroom, whereas more experience and support with new knowledge allows a teacher to
more effectively implement change. Professional development must lay a foundation
from which teachers are able to build upon to enhance student achievement in their
classroom. Teachers must have opportunities to practice new skills and reflect on such
practice so new instructional strategies can be effectively implemented (Pasatta et al.,
2017). Matherson and Windle (2017) stated “professional growth should be a steady
progression over the course of a semester, a year, or more if it is to have lasting impacts
in the classroom and on student achievement” (p. 31).
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Content Area Expertise
Without content area expertise, any educator will be unable to design a vibrant
curriculum that increases student achievement. Griffin and Brownell (2018) stated
content focused professional development has the greatest influence on student learning
when compared to other aspects of professional development including active learning,
coherence, duration, and collective participation. Research studies across multiple content
areas support the need for teachers to continue to build their content area knowledge. In
their research on how professional development experiences influence instruction in
physical education, Iserbyt, Ward, and Martens (2016) found evidence that student
learning is influenced by the strength of the teacher’s content-knowledge along with the
teacher’s ability to give quality feedback to students. Singh-Pillay and Sotsaka (2017)
stated in their research on teachers’ content knowledge in the engineering classroom that
the teacher’s understanding of their content influences instructional decisions. According
to Thomas-Brown, Shaffer, and Werner (2016), when social studies teachers were
surveyed regarding professional development needs that more opportunities for
disciplinary knowledge and skills was desired by the majority of participants.
Professional development based on a teachers’ content area creates learning experiences
that are relevant to teachers, increases teacher engagement, and effects the overall quality
of his or her teaching (Singh-Pillay & Sotsaka, 2017; Sutherland, Granger, Hughes,
Enderle, Ke, Saka, & Tekkemru-Kisa, 2016).
Content area growth can occur in a variety of forms: time to take on the role of a
student manipulating content area tools, observing colleagues’ classrooms, participating
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discussions with colleagues focused on content area phenomena or research literature,
mentoring programs with colleges and universities, and community internships which
together can build a content-focused professional learning network (Burbank et al.,2016;
Glover, Harrison, & Shallcross, 2018; Herro & Quigley, 2017; Sutherland et al., 2016;
Wongsopawiro, Zwart, & van Driel, 2017; Yee, 2015). In their research on secondary
chemistry teachers collaborating with university professors, Glover et al. (2018) found
that content area collaboration “has changed the way they teach, giving them greater
confidence, new skills, knowledge and the ‘patter’ associated with teaching their subject,
as well as demonstration skills, giving these and other practical work greater focus in
their teaching (p. 124). Herro and Quigley (2017) stated similar results in their research
on professional development in STEAM coursework adding that collaborative
experiences can also be enabled through the use of technology if meetings with local
content area experts are not possible. Jao and McDougall (2016) stated in their work on
the Collaborative Teacher Inquiry Project for a ninth-grade mathematics program that
content area collaboration is an effective means of professional development since
teacher improvement is not successful in an isolated environment.
Teacher effectiveness is dependent on the strength of the teacher’s content-
knowledge (Iserbyt et al., 2016; Singh-Pillay & Sotsaka, 2017). Content area expertise
can be gained through a variety of endeavors including work with school colleagues,
members of the community, and global experts, all of which will influence classroom
instruction. Without strong content knowledge, teachers will not be successful in the
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implementation of new instructional strategies, such as personalized learning which
requires teachers to be open to multiple pathways of student learning.
Pedagogical Content Knowledge
Being an expert in a content area does is not sufficient when designing classroom
instruction. Pedagogical content-knowledge is the “fusion of both content knowledge and
pedagogical knowledge” (Singh-Pillay & Sotsaka, 2017, p. 1215). Saderholm, Ronau,
Rakes, Bush, and Mohr-Schroeder (2017) stated in their research on professional
development for math and science teachers that it is necessary to connect content-
knowledge to classroom practice to increase student achievement:
a goal for the design of professional development experiences should be to give
explicit attention to the meaning and application of these practices so that teachers
are able to deconstruct their own cognitive structures and reconstruct them in a
more robust form. (p. 815-816)
Teachers want engaging and relevant professional development opportunities that focus
pedagogical content knowledge and address students’ needs (Matherson & Windle, 2017,
p. 30).
According to Thomas-Brown et al. (2016) the greatest needs in professional
development experiences for teachers includes “incorporating real-world applications
into lessons; learning how to integrate other curricular topics into lessons; learning how
to align curriculum, instruction, and assessment with state education standards; and
challenging students to accept and share responsibility for their own learning” (p. 64).
These examples of pedagogical content knowledge demand a need to personalize teacher
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professional development based on content area to maximize the effect it will have on
classroom achievement. Pedagogical content knowledge can be built through consistent
teacher reflection on instructional practices and student work, reading content area
literature, and peer discussions with colleagues and experts in their content areas (Caddle,
Bautista, Brizuela, & Sharpe, 2016; Sutherland et al., 2016; Singh-Pillay & Sotsaka,
2017; Wongsopawiro et al., 2017).
Teachers must be exposed to innovative instructional strategies and ways to meet
the needs of diverse learners along with engaging in their content area in order to grow as
a professional and be an effective classroom practitioner (Iserbyt et al., 2016; Pasatta et
al., 2017; Singh-Pillay & Sotsaka, 2017). In their research on professional development
for social studies teachers, Thomas-Brown et al. (2016) found that teachers with strong
content-knowledge and pedagogy in their content area are more ambitious in their
teaching, make better instructional decisions, and are more effective at arousing students’
critical thinking skills (p. 69).
Content-Specific Professional Development
A one-size-fits-all professional development model is not responsive to the needs
of teachers with diverse strengths and weaknesses across multiple content areas (Caddle
et al. 2016; Herro & Quigley, 2017; Sutherland et al, 2016). According to research,
teachers felt that their top two professional development needs were content knowledge
and pedagogical content knowledge (Caddle et al., 2016; Thomas-Brown et al., 2016). In
their research of on professional development for mathematics educators, Caddle et al.
(2016) stated
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The vast differences in teachers’ mathematical backgrounds and experience, and
in their motivations and needs, indicate that in order to support teachers better, we
need to meet them where they are. That is, we need to be able to find the right fit
in PD programs in order to complement existing strengths and facilitate
improvement in other areas. (p. 129)
The implementation of a content area focus professional development program supports
teachers in gaining an in depth understanding of their content area and providing students
with more enriched learning opportunities in that content area (Griffin, & Brownell,
2018; Jao & McDougall, 2016).
Professional growth for secondary teachers must be two-fold: a focus on effective
instructional strategies and continuous development as a master teacher in their content
area. Burbank et al. (2016) stated “content area knowledge depth provides both a
foundation for flexibility as well as breadth in instruction” (p. 56). Flexibility is essential
to plan multiple opportunities for student learning that is key for personalized learning
experiences for students. Professional development must include opportunities for
teachers to grow in their content area, as well as, in pedagogical content knowledge.
Garet, Heppen, Walters, Smith, and Yang (2016) stated professional development can
produce significant gains in teacher knowledge if learning opportunities include content
knowledge along with content-specific pedagogy.
Instructional Coaches
Traditional professional development sessions are often based on a sage on the
stage mentality. However, Yee’s (2015) research on learner centered instruction can
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influence professional development design by considering the learning needs of each
teacher especially when teachers have “varied levels of experience and thus different
attitudes toward prescriptive approaches” (p. 100). Learner centered instruction aligns
with the need to personalized learning for teachers to maximize professional growth
which, in turn, supports gains in student achievement.
To truly personalize professional learning for teachers, the support of an
instructional coach is essential to the structure of a professional development program
Dixon et al., 2014; Frankling et al., 2017; Herro & Quigley, 2017). An instructional
coach must be available for teachers to seek ongoing support and guidance when needed
(Yee, 2015). Teachers must be allowed to practice new instructional strategies if they are
to make a difference. Teacher practice along with the support of a professional
development coach that assists with new lesson and assessment development will support
greater classroom success (Dixon et al., 2014; Frankling et al., 2017; Van Den Bergh et
al., 2015). Frankling et al. (2017) stated the benefits of embedded professional learning:
the need for teachers to ‘try out’ the principles of differentiation [personalized
learning] and see the benefits with their own students in their own classrooms,
and then share their experiences and seek feedback from colleagues and mentors,
leading to increasing ‘buy in’. The data also appear to affirm why the ‘one shot’
external professional development model with no on-site follow up is less
effective in changing teachers’ practices. (p. 80)
Continued teacher support, including the assistance of instructional coaches, must be
integrated into the design of a professional learning program aid in the implementation of
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any new instructional strategy if it is to build teacher efficacy. Thus, professional
development must be meaningfully integrated into the structure of each teacher’s school
environment (Lowrie, 2014).
In their research on teacher learning and professional development programs, Van
Den Bergh et al. (2015) found that without structured support, feedback, and reflection
teachers would not effectively integrate instructional changes into practice. However, if
teachers had opportunities to discuss problematic lesson components with an instructional
coach extensive feedback could be given to the classroom teacher to promote further
teacher learning and build tools to overcome challenges in classroom instruction (Van
Den Bergh et al., 2015). Even though the support of an instructional coach promoted
teacher learning, Van Den Bergh et al. (2015) stated that a significant finding in their
study was “how rarely designers built in opportunities for feedback and coaching in the
workplace” (p. 149).
Professional Collaboration
Ongoing professional learning experiences can be supported through
opportunities for teacher collaboration. If professional development is learner centered
with a shared leadership model, all participants including facilitators will continue to
grow professionally. Yee (2014) stated that a learner-centered philosophy emphasizes
engagement, exploration, an in-depth investigation along with reflection of the chosen
topic which promotes a shared understanding of possibilities and outcomes. As a result of
their research on learning circles, Frankling et al. (2017) found that professional
collaboration supports the alignment and integration of students, teaching and the
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curriculum, and the distributive nature of the model whereby leaders were cultivated at
multiple levels. The construct of learning circles appeared to assist in professional
development being delivered by a group of leaders who were supported by researchers,
and teachers who were in turn supported by leaders as they experimented with the
application of differentiation in their classrooms. This meant that professional
development was not only collaborative, it was decentralized and its distributive nature
allowed knowledge to be shared and accessed much more efficiently (p. 83).
Collaborative working environments provides personal and professional development
opportunities for all participants that are supportive and reciprocal in nature while
refining teachers’ expertise (Clarke, 2016; Herro & Quigley, 2017; Jao & McDougall,
2016; Messiou & Ainscow, 2015; Morris, 2017). In addition, an emphasis on engagement
during professional development increases teachers’ capacity as leaders in the school
culture creates an environment based on shared leadership (Parker, Patton, & O'Sullivan,
2016).
Engagement in collaborative practices not only empowers teachers to be more
reflective, but assists them toward effective teacher practices that are aligned and explicit
(Clarke, 2016; Ma, Xin, & Du, 2018; Morris, 2017). Collaboration can occur with
colleagues in a teacher’s environment or through interschool collaborative experiences.
In their research, Parker et al. (2016) found three professional development practices that
were most successful for teacher learning: critical dialogue, public sharing of work, and
engagement in a community of learners. In a teacher’s school environment, such
collaborative experiences may include peer observations and lesson reflections or small
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groups that meet focused on a common goal. From their research on learner diversity and
professional development, Messiou and Ainscow (2015) concluded that the following
were essential for teacher learning: engagement in professional discussions, collaboration
and mutual support amongst colleagues, and acceptance of challenging the status quo in
the school’s culture (p. 253). Given shared experiences and common backgrounds, a
more productive learning environment will be created where teachers can exchange ideas,
participate in discussions focused on effective instructional practices, and encourage the
creation of action-oriented solutions to challenges in teaching (Ma et al., 2018; Morris,
2017; Parker et al., 2016; Wongsopawiro et al., 2017). As “learners construct knowledge
in relation to their prior knowledge and experiences and to be useful, knowledge is
situated in a relevant or ‘authentic’ context” (Parker et al., 2016, p. 140-141). However, if
professional collaboration is to be effective, it is vital that time is scheduled for teachers
to engage in such collaborative activities (Parker et al., 2016).
According to the research of Frankling et al. (2017), highly collaborative
professional development programs repeatedly resulted in cross-curricular professional
conversations that allowed teachers to learn from the insights of their colleagues by
providing opportunities for “teachers to take risks without fear of failure and for
collaborative problem solving to flourish” (p. 84). As environments that promote risk-
taking are created in collaborative learning communities, excitement and engagement in
professional development will develop teacher ownership over their learning experiences
and motivate teachers to value and protect time for their own professional growth
(Messiou & Ainscow, 2015; Parker et al., 2016). Collaborative dialogue has the power to
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change a teachers’ practices by enriching their current knowledge, challenging their
professional beliefs and values, encouraging their evolvement as a continuous teacher
learner, and building supportive and strong relationships with colleagues (Messiou &
Ainscow, 2015; Parker et al., 2016).
Project Description
Potential Resources and Existing Supports
The participating school district had incorporated resources into the current
professional development plan that will be utilized for the purposes of this project. At the
start of this research, the school district had created three personalized learning cohorts,
over a period of three years. Each cohort included approximately 30 to 35 Grades K-12
teachers. Secondary participants were from a variety of content areas with most content
areas included during the three years of cohorts. These cohorts were the initial teachers to
be trained on personalized learning philosophy and were encouraged to implement the
elements of personalized learning into classroom instruction. With the addition of grant
funding three personalized learning collaborators have also been added to the district’s
staff. The personalized learning collaborators have at least five years of teaching
experience, various content and grade level backgrounds, and have incorporated
personalized learning opportunities for students into their former classrooms. These
personalized learning collaborators lead one-day professional development sessions
providing an understanding of personalized learning elements and examples of how it
may be incorporated in a classroom setting. Currently, the personalized learning
collaborators are available throughout the school year to provide teachers with one-to-one
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support or assist small groups of teachers working on the same instructional task. A
district technology team also exists to help teachers integrate technology into instruction.
Over the past two years, the school district has created eMerge teams consisting of
teachers with expertise in integrating technology into instruction to serve as technology
leaders in their buildings. These current components of teacher support will be utilized
during the proposed professional development program.
According to the qualitative data needed resources include time, support specific
to each content area, and a greater understanding of personalized learning elements. It is
to be noted that participants in the qualitative portion of this research study had a variety
of experiences when it came to personalized training. Some participants had no formal
personalized learning training and other attend sessions led by district leadership and
personalized learning collaborators. Financial support is essential for success of the
proposed professional development program. Summer writing hourly pay and guest
teacher funding for during the academic year would be needed to provide teachers with
ample time to design personalized learning options for students. Meeting time would also
need to be available more frequently to follow-up with individual teachers and provide
more opportunities for collaboration; this could be done during department meetings,
professional learning community meetings (which are held on a weekly basis), or other
times where the teacher may be available during the work day. Current content area
training is done in department meetings with little opportunity to attend national content
area conferences. However according to research, there are multiple possibilities for
building teachers’ content-knowledge and pedagogical content knowledge. One option
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could be working with community partners for pairing content area teachers with a
mentor in their content area or assisting professional development participants in finding
content area learning networks outside of the school district (Garet et al., 2016; Glover et
al., 2018; Herro & Quigley et al., 2017; Jao & McDougall, 2016; Saderholm et al., 2017;
Wongsopawiro et al., 2017; Singh-Pillay & Sotsaka, 2017). The best way for teachers to
be empowered is to expect them to act as professionals who are responsible for their own
professional learning needs. (Lowrie, 2014).
Proposal for Implementation and Timetable
The proposed professional development program for this project has three
components: personalized learning training, building content area expertise, and
professional collaboration focused on reflection and designing instruction. As stated by
Dixon et al. (2014), professional development is more effective when it is not completed
in a single session, but is ongoing and part of the school culture. Thus, this project has
been designed to incorporate ongoing professional development support and maximize
teacher learning.
For the purposes of the project proposal, three full-day sessions of professional
development incorporating personalized learning strategies, content area work, and
teacher instructional work will be presented. The first session focuses on personalized
learning theory and includes an overview of all five personalized learning elements
(knowing your learner, student voice and choice, flexible groupings, data-informed
instruction, and technology integration) and will be presented during the first quarter of
the school year. Participants in this session can represent diverse content areas. During
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this session examples for all five personalized learning elements will be presented from a
variety of content areas. It is important that participants see examples from their content
area. Lowrie (2014) stated empowered teachers will take responsibility for their
professional endeavors. Thus, this session will conclude with time for teachers to reflect
upon how personalized learning can enhance their instruction and brainstorm ways it
could be incorporated in their classroom.
The second session supports teachers in the same content area as they work on
how personalized learning elements promote student learning in their content area.
Multiple content area sessions should occur throughout the school year. Since this session
focuses on content area development, sessions will be held individually by content area
groups with the assistance of curriculum leaders. The content area agenda provided in
Appendix A will focus on mathematics. To start this session, there will be review of the
five personalized learning elements. Curriculum leaders will present professional
development based on district learning standards, which will have been previously
selected by the department. In the agenda provided in Appendix A factoring polynomials
is the standard of focus for this piece of the professional development. Teachers that
engage in professional activities within their content area will have increased success in
delivering classroom instruction as they receive support built into their school culture and
are provided with time for practice (Burbank et al., 2016; Singh-Pillay & Sotsaka, 2017).
Flexibility will be given to the curriculum leader on the design of the presentation:
inviting a local business to present, connecting with a university professor, working with
the state department of education curriculum specialist, or creating the presentation on his
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or her own. The remainder of the session will provide time for teachers to work as teams
on planning personalized learning instruction. The personalized learning collaborators
will be present to provide assistance as needed.
The third session allows for teachers to share their personalized learning efforts
will colleagues amongst all content areas, provides time for reflection and future
planning, and will be occur during the fourth quarter of the academic year. This session
will start with a celebration of personalized learning efforts through a public sharing of
work. Parker et al. (2016) stated that public sharing of work and engagement in a
community of learners were successful components of teacher professional development.
This show and tell will allow teachers from multiple content areas to see examples that
could be modified to fit their classroom instructional needs and ask those teachers
questions as needed. Time for reflection will take place so teachers can process what
went well and what could be improved in their personalized learning project. At the end
of the session, teachers will be asked to make plans for future projects.
In addition to the three professional learning sessions, participants will also have
ongoing support from department leaders, personalized learning collaborators, and
colleagues between the professional development experiences. Participants will be
encouraged to schedule times with instructional leaders to aide in their instructional
planning. All teachers currently participate in weekly professional learning communities
which provides time for professional dialogue to discuss instruction specific to their
course load. Participants will also have opportunities for personal content area growth,
which may occur by attending a content area conference or collaborating with a
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university professor or teachers in surrounding school districts. It will be an expectation
that teachers share their content area learning at department meetings. Each participant
will also be expected to meet at least once with a personalized learning collaborator after
the initial session to discuss potential instructional projects and necessary supports. Since
the district implements professional learning community meetings (PLCs) once a week
during the academic year on Wednesday, PLC teams may choose a personalized learning
focus for their yearly goal. Professional development will more effectively build teacher
capacity and influence student academic achievement if it is spread throughout the
academic year with ongoing support available to teachers (Matherson & Windle, 2017).
Roles and Responsibilities of Others
The success of this professional development program is based on collaborative
experiences of district leadership, building administrators, and teachers. District
leadership must be willing to provide financial support for guest teachers and summer
writing hours, which will provide teachers with workday embedded time to design
personalized learning opportunities in their content area. According to Van Den Bergh et
al. (2015), a teacher’s willingness to learn is a vital factor in the effectiveness of any
professional development experience. For this reason, the professional development
offering will be made available to all teachers, but participation will not be forced upon
all teachers. The district’s personalized learning collaborators will be utilized to provide
participants with ongoing support as personalized learning is implemented. Building
administrators will need to allow for flexible professional development schedules for
teachers that may include: full-day or half-day sessions, department meetings, and
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professional learning community meetings. Teachers would need to be willing
participants in this professional development program. This program requires a year-long
commitment to professional growth and may include time outside of the work day for
community content area collaboration.
Project Evaluation Plan
There will be formative and summative evaluations to measure the goals that
define this professional development plan, which are to build teacher understanding of
personalized elements and how each element can be incorporated into classroom
instruction, as well as, to support teacher growth in their content area and to develop
effective instructional practices for their content area. The purpose of multiple
evaluations will provide meaningful feedback so improvements can be made for future
professional development opportunities. At the end of the first two sessions, participants
will be asked to complete a formative evaluation asking for feedback regarding meeting
the day’s learning goals and requesting suggestions for improvement. These formative
assessments will provide valuable information that will assist professional development
leaders to modify the next session to better meet the needs of the participants. At the end
of the third session, participants will complete a summative evaluation so district
leadership can assess program effectiveness and design future professional development
sessions based on participant feedback.
The goals for the first session are that participants should be able to summarize
personalized learning philosophy and name at least one benefit for students, as well as,
describe the five personalized learning elements and provide at least one example of how
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each element could be used in their content area. Upon the conclusion of the second
session participants should be able to meet the following goals: summarize instructional
strategies for teaching a chosen foundation skill for their content area and describe the
personalized learning project chosen by their content team. In each formative assessment
questions are included to measure if participants’ have met the desired learning goals. If a
goal has not been met, then curriculum leaders and personalized learning collaborators
will provide additional support to help participants meet each goal. When participants
meet with the curriculum leaders and personalized learning collaborators a brief survey
will be sent electronically to seek additional guidance for future support for each teacher
participant. These surveys will ask the participants what they learned during the meeting
and what additional support and resources are needed to assist in personalized learning
implementation efforts. These surveys will provide the curriculum leaders and
personalized learning collaborators with information so they can better support teachers
implementing personalized learning in their content area.
The summative evaluation given after the final session will assess the overall
effectiveness of the proposed professional development series. The objectives for the
third session includes summaries of the implemented personalized learning projects,
analysis and reflection of successes and areas for growth as they continue implementing
personalized learning in their content area, and initial planning for a personalized
learning project that will be implemented the following academic year. The summative
evaluation is designed to assist the participants in reflecting upon the overall experience,
allow participants to provide suggestions for improvement, and ask each participant to
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share next steps. The information gathered from these assessments will allow district
leaders to design professional development opportunities that will continue to support the
participants in future instructional endeavors.
The goal for the project evaluation is to allow future sessions to be better geared
towards participants’ learning needs. District leaders participating in the delivery of this
professional development program will be able to use the formative and summative
feedback to continue to fine tune and improve future professional development
experiences for teachers; thus, building teacher capacity in the school district.
Participants will also benefit from the evaluation process. Through purposeful reflection
and continuous goal setting, each participant will be able to celebrate small successes,
seek help when needed, and be more successful in implementing new classroom
instructional practices to increase student achievement in the participant’s content area.
Project Implications
The qualitative data in this study focused on needs and desires of teachers
regarding professional development offered by the participating school district.
Personalized learning is part of the school district’s strategic plan. However, teachers feel
they need professional development support to learn more about personalized learning,
strengthen their content knowledge and instructional practices, and time to complete the
necessary work in plan personalized learning experiences for students. The proposed
three-day professional development program with ongoing support between sessions
meets the needs voiced by participating teachers during data collection.
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Local Stakeholders
A school district consists of a community of professionals with a common goal:
helping students achieve to their fullest potential. This professional community will only
be as strong as the inner support that occurs amongst colleagues: district leadership,
professional development specialists, building administrations, curriculum leaders, and
teachers. Thus, the designed professional development project integrates all components
of the district’s community. District leadership provides financial support for guest
teachers for teachers participating in full day workshops during the school year and for
summer writing pay for participants to write curriculum. Planning and organizational
skills from professional development leaders will be utilized to carry out the full day
learning sessions and one-to-one assistance for participating teachers. Building
administrators will allow teachers to attend the one-day workshops and conferences
focused on the teacher’s content area. Curriculum leaders will continually look for ways
to build content area capacity amongst the teachers in the department. All of these
professionals work to support the teachers attending professional development with the
goal of improving classroom instruction and student learning.
Larger Context
Student learning is the focus of any school district. If there are professional
development experiences that support teachers in increasing overall student learning,
school districts will be able to better meet their goals. However, teachers must willingly
engage in professional learning if it is to make a difference in student learning.
Professional development must be personalized to teachers’ individual needs in order to
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be effective and engage teacher learners. As previously mentioned, a one-size-fits-all
professional development experience is not the way towards school improvement
(Burbank et al., 2016; Caddle et al. 2016; Herro & Quigley, 2017; Lowrie, 2014; Rowan
& Townend, 2016; Sutherland et al, 2016; Van Den Bergh et al., 2015). Just as students
have individual learning needs and are support by teachers, teachers have individual
professional learning needs and must be supported by the school district.
Conclusion
The proposed professional development plan addresses the needs to assist
teachers in overcoming the challenges of implementing personalized learning in their
content areas. The plan includes three full day sessions along with ongoing support from
personalized learning collaborators. The goal of the proposed project is to personalized
professional development for teachers to meet their individual learning needs.
In Section 4, I will discuss the project’s strengths and limitations, and alternate
approaches for this research. I will also reflect upon my experiences as a scholar at
Walden University, my growth as an educational leader, and my progress as a researcher.
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Section 4: Reflections and Conclusions
Introduction
The purpose of the study was to investigate the challenges that arise when
secondary teachers implement a personalized learning environment in their content area.
As a result of this research, a project was designed to provide teachers with a content-
specific professional development program that is personalized to meet the needs of
individual teachers. This program will support positive teacher growth and thus increase
student achievement. In Section 4, I will state the strengths and limitations of the project
outlined in Section 3; provide recommendations for alternate approaches; discuss
scholarship, project development and evaluation, leadership and change; reflect on this
work; offer insights on future research; and explain the study’s implications for social
change.
Project Strengths and Limitations
Strengths
Data collected from this research study provided the means to create a
professional development plan that met the needs of teachers in different content areas
and provide ongoing support to teachers for planning personalized instruction. The
strengths of the project met the needs outlined in the data analysis through the creation of
a professional development plan including full-day immersion experiences, a training on
the personalized learning elements, content area learning, time to design instructional
materials, and ongoing support from district leaders. The professional development
program created for this project is personalized in order to meet the needs of individual
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teachers, to provide teachers with opportunities for content-specific professional learning
experiences, and to offer ongoing support for teachers through the access to instructional
coaches and collaboration opportunities with colleagues. This project permits teachers to
partake in the design of their professional learning and empowers teachers to grow in
pedagogy and their content area.
Limitations
The limitations of this project are time and potential costs. After the initial session
on personalized learning philosophy, teachers will need to commit to content area
professional learning experiences and meetings with an instructional coach. Although this
provides ongoing support for teachers as learners it may require multiple meetings that
could take teachers outside of the classroom. Even though all efforts will be made to
work with teachers’ schedules before and after school along with planning periods for
instructional coaching appointments and content area training it is not guaranteed that it
will happen. Thus, there would be a cost for guest teachers for the days where teachers
were out of the classroom. Additional costs are dependent on the content area training as
well. If a speaker is brought in or teachers are sent out for content area experiences, fees
may add up which could deplete the district’s professional development budget.
Recommendations for Alternative Approaches
The problem addressed in this study was the perceived challenges of teachers
when implementing personalized learning strategies into their content areas at the
secondary level. The local problem allowed for multiple approaches. A single school
district participated in this research study. If this study had multiple school districts, a
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larger sample size would have led to more generalizable results. With a larger sample
size, this study could have compared perceived challenges of teachers at the elementary
level and the secondary level to see if the perceived challenges were consistent across K-
12 content areas.
Alternatively, a program evaluation for the implementation a personalized
learning program for Grades K-12 could have been conducted to provide greater insights
to the professional development program in its current standing across the entire school
district. Another approached would be to focus on student perspectives of how
personalized learning influences them as learners across content areas. This research
would provide information on personalized learning strategies that may or may not be
more effective to how students view their personal learning strengths.
Scholarship, Project Development, and Leadership and Change
Scholarship
As a student at Walden University, I have been challenged about my beliefs of
current educational practices, who I am as a learner, and my future endeavors as an
educator. During the coursework for my educational doctorate I have been greatly
supported by my professors and colleagues. Class discussions made me question my
thinking about current educational practices and how the future of curriculum,
instruction, and assessment should look in the field of education. My coursework has
prepared me to successfully define problems based on evidence, write both qualitative
and quantitative research questions to focus on the problem at hand, propose a framework
to guide further examination, collect and analyze essential data, and strategically derive a
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solution based on data analysis and past research. I have learned that even though the
research process is tedious, it is necessary for a solutions-focused approach on improving
educational practices.
Throughout my doctoral journey I have remained a full-time educator and
curriculum leader in my school district, as well as, worked with a state university to
design a graduate program to build leadership capacity in teachers. At my school, I have
shared my doctoral endeavors with my current high school students hoping to instill in
them the power of being a lifelong learner. I feel that being a student has provided
valuable insights into how my current students multi-task to handle their daily
commitments and grow as individuals. My classroom grew as an environment where we
struggled together and celebrated together. As a leader, I shared scholarly articles and
posed questions to promote scholarly dialogue with my colleagues. My work as a
curriculum leader was also influenced as I took a more strategic outlook to how I
approached my current position so I could assist others to push their professional growth.
Project Development
During the data analysis process, common themes emerged that provided a focus
for the chosen project. Teachers voiced in the qualitative data collection that they wanted
professional development that was personalized to their professional needs and content
area. Teachers also desired sufficient time to plan personalized learning instructional
activities with continued support from the school district. Thus, the goal of the project
was to provide a professional development experience focused on how to implement
personalized learning elements (knowing your learner, student voice and choice, flexible
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groupings, data-informed instruction, and technology integration) into teachers’ content
areas. The review literature supported a design of collaborative professional development
experiences that integrate pedagogical content knowledge with personalized learning
elements. As a result of the designed professional development experience, teachers will
have created and implemented personalized learning lessons for their students focused on
best practices for their content-area.
Through my experiences as a leader in my school and what I have learned through
my coursework I feel confident in my ability to design professional development that will
engage teachers and be relevant to the needs of each individual teacher. My research
along with the review of literature guided my decision throughout the design process. As
a means to continuously improve my design, time for participants to provide feedback
was integrated into each session so adjustments could be made based on teachers’
responses.
Leadership and Change
During my 19 years in education, I have had many opportunities to growth as an
educational leader. I have participated on school improvement committees, worked at the
district and state level writing standards, and held leadership positions in the state’s
teachers of mathematics association. In my current role as a curriculum leader, I work
with teachers and administrators to promote high student achievement in our school. My
four years as a student at Walden University has been most beneficial in my growth as an
educational leader. My coursework has helped me develop a solid foundation in
curriculum, instruction, and assessment and continues to support my growth as a leader in
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education. The theoretical framework for this research, expectancy-value theory, enforces
the needs to work collectively with teachers when implementing new initiatives. If
teachers do not see the value or hold high expectations of an initiative, such change will
not effectively be implemented and student growth will not occur. As a result of this
mixed-methods research study, I have gained confidence in my ability to conduct
educational research that could positively impact student achievement. This work has
allowed me to define problems in education and seek results to promote change in the
learning environment.
Reflection on Importance of the Work
As an educator, I always look for the most effective instructional strategies that
will help students grow academically. As a leader, I feel that it is my duty to search for
methods that not only improve my craft as an educator, but help my colleagues to also
grow as educators. Personalized learning is an instructional mode that empowers students
as learners. It offers a meaningful educational experience that allows for choice, focuses
on students’ strengths, and offers multiple paths towards mastery. If teachers are well
versed in their content area and in personalized learning strategies, the resulting
instructional practices has the potential to increase student achievement in that content
area.
The doctoral process taught me that it is essential to continue my growth as a
learner in the field of education. I must always look for more effective strategies to meet
the needs of the learners in my community. To do this, I cannot work in isolation.
Success builds from continued academic studies, collaboration with colleagues, research,
