Incorporating the principles of learning science into educational practices by integrating
psychological research into the curriculum
Dasha Bunks
Liberty University
PSYC 775 - Teaching of Psychology
Dr. Winn
2022
What is the Science of Learning?
The field of specialization known as the science of learning is not, in fact, one field.
Science of learning is a term that serves as an umbrella for many lines of research, theory, and
application. A term with an even wider reach is Learning Sciences (Sawyer, 2006). The present
book represents a sliver, albeit a substantial one, of the scholarship on the science of learning
and its application in educational settings (Science of Instruction, Mayer 2011).
Although much, but not all, of what is presented in this book is focused on learning in
college and university settings, teachers of all academic levels may find the recommendations
made by chapter authors of service. The overarching theme of this book is on the interplay
between the science of learning, the science of instruction, and the science of assessment
(Mayer, 2011). The science of learning is a systematic and empirical approach to understanding
how people learn. More formally, Mayer (2011) defined the science of learning as the “scientific
study of how people learn” (p. 3). The science of instruction (Mayer 2011), informed in part by
the science of learning, is also on display throughout the book. Mayer defined the science of
instruction as the “scientific study of how to help people learn” (p. 3). Finally, the assessment of
student learning (e.g., learning, remembering, transferring knowledge) during and after
instruction helps us determine the effectiveness of our instructional methods. Mayer defined
the science of assessment as the “scientific study of how to determine what people know”
(p.3).
Most of the research and applications presented in this book are completed within a
science of learning framework. Researchers first conducted research to understand how people
learn in certain controlled contexts (i.e., in the laboratory) and then they, or others, began to
consider how these understandings could be applied in educational settings. Work on the
cognitive load theory of learning, which is discussed in depth in several chapters of this book
(e.g., Chew; Lee and Kalyuga; Mayer; Renkl), provides an excellent example that documents
how science of learning has led to valuable work on the science of instruction. Most of the work
described in this book is based on theory and research in cognitive psychology. We might have
selected other topics (and, thus, other authors) that have their research base in behavior
analysis, computational modeling and computer science, neuroscience, etc. We made the
selections we did because the work of our authors ties together nicely and seemed to us to
have direct applicability in academic settings.
Anyone who has ever taught a course knows that students are not a homogeneous
group. They come into our courses with differing levels of knowledge about subject matter
content, broad ranges of intellectual and metacognitive skills, and a variety of beliefs and
attitudes toward the topic and toward learning. In fact, prior knowledge is one of the most
influential factors in student learning because new information is processed through the lens of
what one already knows, believes, and can do. In this chapter we define prior knowledge
broadly, to include content, skills, and beliefs, because all are “knowledge” in that they result
from past experiences and impact subsequent learning and performance. When prior content
knowledge is accurate, sufficient, active and appropriate, students can build on that foundation,
connecting new content knowledge to already established content knowledge in a framework
that will enable them to learn, retrieve, and use new knowledge when they need it (Ambrose,
Bridges, DiPietro, Lovett & Norman, 2010).
When prior skills – both domain-specific and more general, intellectual skills – are
honed, accessed appropriately, and used fluently, they help students to learn more complex
skills. And when prior beliefs support behaviors that lead to learning, students’ performance is
enhanced (Aronson, Fried, & Good, 2002; Henderson & Dweck, 1990). However, students will
not have a stable base on which to build new knowledge if their existing content knowledge is
distorted, if their skills are inadequate, and/or if their beliefs lead to behavior that impedes
learning. This expanded definition of prior knowledge raises two questions: how can faculty
members determine what students know, can do, and believe, and then how can faculty
members adapt their teaching to address prior knowledge issues and promote student
learning? In this chapter, we focus on helping faculty to better understand (1) different kinds of
prior knowledge with which students enter courses; (2) how prior knowledge, skills and beliefs
can help or hinder learning; and (3) what strategies faculty members might use to address prior
knowledge, skills, and beliefs that are harmful and leverage those that are potentially helpful.
The accuracy of students’ prior content knowledge is critical to teaching and learning as
it is the foundation on which new knowledge is built (e.g., Bransford & Johnson, 1972; Resnick,
1983). If students’ prior knowledge is faulty (e.g., inaccurate facts, ideas, models, or theories),
subsequent learning tends to be hindered because they ignore, discount, or resist important
new evidence that conflicts with existing knowledge (Dunbar, Fugelsang, & Stein, 2007; Chinn &
Malhotra, 2002). For example, if first-year Physics student Toby mistakenly believes “heavier
objects fall faster,” he is likely to see what he expects when shown a demonstration of
Newton’s Second Law.
In general, students can significantly benefit from lessons that directly challenge their
misconceptions or that leverage accurate conceptions as a bridge to dislodge misconceptions
(e.g., Clement, 1993; Hunt & Minstrell, 1994). So, 9 instructors who are aware of their students’
inaccurate prior content knowledge can design instruction to target and correct
misconceptions. Even accurate prior content knowledge has important effects on students’
capacity to learn new related material. For example, when college students were taught new
facts about familiar individuals (i.e., high prior knowledge), they retained twice as much as
students who were taught the same number of facts about unfamiliar individuals (i.e., low prior
knowledge). In addition, research has shown that students can maximize the potential benefits
of accurate prior knowledge if they are prompted to “activate” that information (e.g., Peeck,
VanDenBosch, & Kruepeling, 1982; Garfield, delMas, & Chance, 2007). For example, imagine a
student, Donna, starting her second semester of statistics, and her instructor asks the class to
generate daily-life examples of variability before studying the concept more deeply.
By calling these examples to mind, Donna is better able to connect her existing
knowledge with new knowledge and hence learn the new material better. In other words,
instructors who are aware of their students’ accurate prior knowledge can design instruction to
connect new information more effectively with what students already know.
Intellectual Skills
intellectual skills to delineate skills that can be applied across a wide range of different content
areas. Pascarella and Terrizini (2005) described these skills as those that help students “process and
utilize new information, communicate effectively, reason objectively and draw objective conclusions
from various types of data, evaluate new ideas and techniques efficiently, become more objective about
beliefs, attitudes and values; evaluate arguments and claims critically; and make reasonable decisions in
the face of imperfect information (p. 155).”
These skills are important because they are inextricably tied to the content we teach. To absorb
and digest the information they are given in classrooms, students must be able to express, apply,
demonstrate, and use their content knowledge through these intellectual skills. Moreover, assessment
of students’ knowledge is often a blend of domain-specific and domain-general knowledge and skills. For
example, students need to be able to write well in order for teachers to clearly assess their knowledge
of the reasons behind an historical event. Similarly, students need strong skills in analyzing and
interpreting data to define and support a public policy decision.
Intellectual skills form the foundation of the ability to learn throughout life, particularly in
today’s work environment where professionals will likely change jobs and perhaps professions several
times over the course of their careers. Employers across a variety of industries already recognize the
importance of these intellectual skills for their companies’ continued/future health and success (It takes
more than a major, 2013; Job outlook 2013, 2012; The role of higher education in career development,
2012). In our experience, faculty members often neglect or minimize the development of intellectual
skills in students. Some faculty do not believe it is their responsibility to teach these skills, others worry
that they do not have the expertise to do this kind of teaching, and still others make an attempt, but do
it poorly. We argue here that it is critical for instructors to take on the responsibility of developing
intellectual skills in their students, as disciplinary variations embedded within intellectual skills are
critical and core to learning and developing expertise.
Faculty attempting to teach intellectual skills will be able to do so more effectively if they teach
about explicit components of intellectual capacity to help students become aware of these separate
aspects of intellectual skills. This is often a challenging task for faculty because research has shown that
experts tend to skip and/or combine steps in a process once it has become second nature to them
(Anderson, 1992; Koedinger & Anderson, 1990). But being able to grasp the component parts of
intellectual skills is akin to being able to grasp and manipulate the trajectory of each ball when
performing a complex juggling act. If students do not have the “micro skill” of fluently grasping each ball,
the fluid activity of juggling will fail. Similarly, creating an argument requires a set of specific steps that
we engage in but often do not explicitly acknowledge: stating a position, examining assumptions, using
evidence, refuting other arguments, drawing conclusions, and many other steps. And each of these
steps has sub-steps. Learning to quickly and fluidly maneuver among these intellectual moves comes
only after years of practice building understanding of individual steps.
Although some teachers believe that intellectual skills cannot be taught, we believe not only that they
can be taught but that such skills should be taught explicitly within respective domains. We look, for
example, to Harskamp and Suhre (2007) who found that they could improve students’ problem-solving
skills by intervening through formal and informal hints in a computer program, particularly the aspects
of problem-solving that focus on the solution approach (planning) and feedback on the correct solution
(verifying). This research was based on Schoenfeld’s (1992) work in which he helped students improve
their problem-solving skills by asking them both to recall the steps they had taken in solving a problem
and to reflect on the next steps, reinforced by class discussion. Research by Garfield, delMas and Zieffler
(2012) showed an improvement in statistical thinking – particularly around the modeling and simulation
processes – when teachers “scaffolded” instructions (i.e., provided detailed directives at the beginning
and then gradually reduced them by the end of the course), provided an illustrated summary and
synthesis of both processes, and discussed the ideas in class.
Research by Nadolski, Kirschner and van Merriënbuer (2005) also validated how worksheets helped
students learn the steps necessary to prepare and complete a law plea. Again, explicit explanations of
individual steps in complex tasks is what made instruction valuable for learning in the above studies.
Information about students’ intellectual skill set should impact the activities and assessments we design
for them. As students move through our respective courses and curriculum, they should apply these
skills in progressively more complex and challenging assignments, projects, problems, etc., eventually
transferring them to novel/new contexts. Because we need to build on the skills students enter our
courses with, and address the gaps when they have not acquired those skills at the level they need to
use them, we should be ready with strategies to promote learning and expansion of those skills, such as:
• Give students a performance task related to the skill(s) they will need for the course to gauge
their actual level and identify where you need to provide support.
• Deconstruct expert skills for students by creating and teaching the key subcomponents of the
skills you want students to learn and use.
• Model the skills you want students to learn by explaining out loud your process for
understanding, solving, and reflecting on an activity or assignment.
• Provide guided practice on the skills that are missing or weak.
• Use rubrics in the course to reinforce the intellectual skills and their component parts.
• Ask probing questions to get students to reflect on their learning and the intellectual process
they engaged in during their work.
References
Ambrose, S. A., Bridges, M. W., DiPietro, M., Lovett, M. C., & Norman, M. K. (2010). How
learning works: 7 research-based principles for smart teaching (1st ed.). San Francisco,
CA: Jossey-Bass.
Anderson, J. R. (1992). Automaticity and the ACT theory. American Journal of Psychology, 105,
165–180.
Aronson, J., Fried, C. B., Good, C. (2002). Reducing the effects of stereotype threat on African
American college students by shaping theories of intelligence. Journal of Experimental
Social Psychology,38(2), 113-125.
Baxter Magolda, M. B. (1992). Knowing and reasoning in college: Gender-related patterns in
students’ intellectual development. San Francisco: Jossey Bass.
Belenky, M. F., Clinchy, B. M., Goldberger, N. R., & Tarule, J. M. (1986). Women’s ways of
knowing: The development of self, voice and mind. New York: Basic Books.
Billett, S. (2009). Personal epistemologies, work and learning. Educational Research Review,
4(3), 210– 219. Blackwell, L. S., Trzesniewski, K. H., & Dweck, C. S. (2007). Implicit
theories of intelligence predict achievement across an adolescent transition: A
longitudinal study and an intervention. Child Development, 78(1), 246–263.
Bransford, J. D., & Johnson, M. K. (1972). Contextual prerequisites for understanding: Some
investigations of comprehension and recall. Journal of Verbal Learning and Verbal
Behavior, 11, 717-726. Butler, D. (1997). The roles of goal setting and self-monitoring in
students’ self-regulated engagement of tasks. Paper presented at the annual meeting of
the American Educational Research Association, Chicago, IL.
Butler, D. L., & Winne, P. H. (1995). Feedback and Self-Regulated Learning: A Theoretical
Synthesis. Review of Educational Research, 65(3), 245 -281.
Carey, L. J., Flower, L., Hayes, J., Shriver, K.A., & Haas, C. (1989). Differences in writers’ initial
task representations (Technical Report No. 34). Center for the Study of Writing at
University of California at Berkeley and Carnegie Mellon University.
