How to use data to drive science instruction in K-8 public schools?
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ANNOTATED BIBLIOGRAPHY.
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Problem/Solution
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Despite the presence of new reform ideals and standards, K‐12 science instruction remains largely teacher‐driven
and focused on content memorization (Banilower et al., 2018).
The No Child Left Behind (NCLB) mandate to demonstrate adequate yearly progress (AYP), are
their assessment regimens and technology systems to use outcome data to drive instructional
decisions. How educators use data is what matters most in closing the achievement gap.
Integrating data into instructional decisions is the key to help improve student achievement. The
benefits are heavily weighted with internal assessments and state testing results for data to guide
and inform classroom instruction (Peterson, 2007).
Neugebauer, S. R., Morrison, D., Karahalios, V., Harper, E., Jones, H., Lenihan, S., Oosterbaan,
F., & Tindall, C. (2021). A Collaborative Model to Support K-12 Pre-Service Teachers’ Data-
Based Decision Making in Schools: Integrating Data Discussions across Stakeholders, Spaces,
and Subjects. Action in Teacher Education, 43(1), 85–101.
Teacher preparation programs have failed to support teachers in developing knowledge of data
literacy. A collaborative approach to increasing new teachers’ experiences with using data
literacy should be addressed more in college prep courses. As a result of using data literacy
research, teacher preparation programs should focus more on utilizing the data to inform
curriculum and instruction. The practice of using data, is indicative that novice teachers will have
increased beliefs, understandings, and value of data with a growth perspective in confidence and
understanding school-wide data and an approach to problem-solving.
Data based decisions are identified as a core skill for K-12 educators across the country.
Therefore, the rationale for promoting responsive instruction lies within the teachers ability to
interpret, analyze and increase student achievement in school reform. Research is indicative that
using data to support instruction has teachers labeled teachers as data organizers rather than the
purpose of informed instruction. Current research indicates that teachers’ knowledge of how to
effectively use data in schools is limited (Datnow & Hubbard, 2015; Mandinach & Gummer,
2016), anecdotal information is relied upon or some informed intuition in making curriculum and
instructional decisions. Having been scrutinized about their inability to support teachers in
valuing data and data literacy, teacher preparation programs rely on teachers ability to transform
this into more actionable knowledge and practice. Furthermore, successful preparation is
considered the best route for changing teachers beliefs and habits around data literacy when
compared to ineffective inservice professional development for teachers. Innovative approaches
to support teacher data literacy requires going beyond the more common teacher preparation to
solely address assessment literacy (Neugebauer, et al 2020). Further identifies most teacher
preparation programs offer standalone data courses which focus on assessment literacy and not
data literacy. Teachers receive increased pressure from federal and state officials to collect and
use data to promote student achievement. Although many teachers feel they do not have
sufficient understanding or competence to use data for decision making to support student
learning outcomes. But if teacher preparation program curriculum and be transferred tot he
Boesdorfer, S. B., Del Carlo, D. I., & Wayson, J. (2022). Secondary Science Teachers’ Definition
and Use of Data in Their Teaching Practice. Research in Science Education, 52(1), 159–171.
https://doi.org/10.1007/s11165-020-09936-8
Teachers use a limited number of sources for student data to make informed instructional
decisions and address student deficiencies in learning despite the advocacy of data-driven
instructional practices. As for Science teachers, seemingly have a vigorous background in data
collection and analysis in lieu of beliefs to teach data practices to students, and may even be
more comfortable in implementing data driven instructional practices. Because science teachers
are expected to teacher students how to collect, analyze and form conclusions from data, it could
be assumed that these teachers have an advantage over teachers in other disciplines, like social
studies. Boesdorfer (2022) found that secondary science teacher might have data skills because
of their science background and the NGSS, they do not seem to be any different than the general
population of educators in terms of understanding and use of data in their teaching practice.
Moreover, secondary science teachers limit their definition of data to student assessments and
use that information to inform their daily instructional practice somewhat than improve their
overall teaching. However, there is some hope of improvement in teachers’ data practice.
Boesdorfer (2022) found that teachers used the data to identify gaps in student knowledge but
failed to use the data to analyze their instructional methods to determine what worked and what
did not work for their students. In addition, there is a small, but noticeable, group of secondary
science teachers who appeared to be satiated with or to misunderstand the effective teaching skill
exuded by the term data-driven instruction.
Like all teachers, science teachers are expected to improve instruction using the data and to
support student academic success. Data driven practices are teaching methods educators
specifically implement based on data they gathered from their students and effectively educate
student learning outcomes. However, the main reason teachers’ use of data in their teaching
practice is lacking because they were not taught how to approach this data and use it in their
instructional practices (Boesdorfer, 2022). Furthermore, not all science teachers are comfortable
with implementing data driven instructional practices, because they are apprehensive about
reading the data to drive the instruction. In past practices, novice science teachers and math
teachers have asked how do you use the data to drive instruction? Subsequently, they appear to
lack the knowledge of how to pull up their data and decipher it. There is no one size fits all
approach to tracking student data. Once you have been trained to interpret the data, then you
need to unravel how you will record this information. It is imperative that you not only inform
your administrators, but parents and students need to be made aware of the data. Once you can
