Integrating Cognitive Learning Strategies into Physics Instruction : Developing students’ approaches to physics and learning

Introductory physics courses are obligatory for many disciplines outside of physics. As experienced by many students, they are notoriously difficult, often with high failure rates. Many students, whether they passed or failed a physics course, fail to acquire the required conceptual knowledge and skill to become able to model complex situations with physics principles. In some cases, this can be attributed to a lack of study time; in many cases, it can be attributed to inefficient learning strategies. The aim of this thesis was to find ways to create self-regulated physics students who use effective learning strategies, achieve a deep understanding of physics principles, and, ultimately, become able to solve conceptually challenging physics problems through the use of physics modeling. In this research project, we have identified and tried to fill some of the gaps in students’ knowledge that hinder them from becoming able to practice physics modeling. Research within cognitive science, educational psychology, and physics education has informed us about the structure of the knowledge students fail to learn. We matched proven, effective learning strategies to each aspect of this cognitive knowledge structure and we developed tools for scaffolding the process. In the first phase of the first paper, we investigated students’ memory for physics principles and basic facts shortly before the exam and experimentally tested the efficacy of retrieval practice of a novel hierarchical principle structure for improving their declarative memory. The results showed that many of the control group students had a severe lack in their memory for basic facts and principles and that seventy minutes of retrieval practice resulted in large gains for the experimental group. In the second phase, we implemented structured retrieval practice in lectures throughout the semester. The multiple regression model indicated that retrieval practice improved students’ results on the final exam, especially for the weaker students. In the second paper, we quasi-experimentally (study 1) and experimentally (study 2) tested the effects of doing retrieval practice before self-explanation on posttest problem-solving and conceptual scores. In sum, results indicated a medium-sized effect of doing retrieval practice on the problem-solving score. The results were inconclusive for the score on conceptual tests. We also investigated the knowledge students should seek to acquire when self-explaining worked examples in physics. The results from the two studies indicated that when explaining the physics model, students should seek to explicate the principles and their conditions of application, how the principle is set up, and how the physics model can lead to the goal of the problem; and when explaining the mathematical procedures, students should seek to explicate what is done in the particular procedural action, the goal of that action, and the conditions for its application. In the third paper, we built on the results and experiences from the first two papers and tried to integrate three learning strategies and three scaffolding tools into an introductory mechanics course. The three learning strategies were elaborative encoding for acquiring associative links within and between physics principles; retrieval practice for building strong memories of physics principles; and self-explanations for building effective declarative rules for problem-solving. The three tools were: A set of elaborative encoding-questions as a scaffold for elaborative encoding; the Hierarchical Principle Structure for Mechanics, which together with retrieval practice was meant for scaffolding students’ construction of a meaningful and hierarchical cognitive knowledge structure; and a problem-solution structure with emphasis on physics modeling for scaffolding self-explanation and for developing knowledge and skills in physics modeling. Using thematic analysis, we found that the two main encoding strategies—elaborative encoding and self-explanation—require substantial work for overcoming the existing barriers to student adoption and achieving effective implementation. We had more success with the integration of retrieval practice, the hierarchical principle structure, and the practice of physics modeling during problem-solving. The paper provided multiple suggestions for how to overcome barriers and better integrate these learning strategies and tools into the structure of physics courses. Together, these three papers contribute to the physics education research literature with increased knowledge of how we can support students’ conceptual learning, from simple cognitive learning processes like elaborative encoding to the complex practice of physics modeling; with new tools for scaffolding students’ conceptual learning in introductory physics, especially the Hierarchical Principle Structure for Mechanics and the problem-solution structure; and with insights into barriers to students’ adoption of effective learning strategies.Doktorgradsavhandlin

When does Provision of Instruction Promote Learning?

Contradictory evidence has been reported on the effects of discovery learning approach and the role of instructional explanations. By manipulating the presence of instruction (verbal explanation) and transparency of problem structures, we investigated how effects of instructional explanations differed depending on the transparency of problem structure. We developed an auxiliary representational task that made certain aspects of the problem structure more transparent. Instruction proved irrelevant to those aspects of the problem made transparent by the representation but facilitated learning of those aspects that were left obscure. We suggest that the critical role of instruction is not specifying the steps in solving a problem, but rather making salient the features that are critical to student’s ability to infer the steps from examples