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

Learning with worked-out problems in Manufacturing Technology: The effects of instructional explanations and self-explanation prompts on acquired knowledge acquisition, near and far transfer performance

In the present research, two different explanatory approaches – namely, instructional explanation and self-explanation prompts – were applied in worked-out-problem-based learning (learning with worked-out problems) in a computer-assisted instructional environment in the domain of manufacturing technology. This research aims at comparing the effects of both explanatory approaches on topic knowledge acquisition, near transfer performance, and far transfer performance. Additionally, this research also attempts to examine the impact of topic interest on the aforementioned variables, in addition to the relationships between topic interest, mental effort, and learning outcomes. A total of 76 second-year students were randomly assigned to experimental and control groups. The pre- and post-tests were used to measure topic knowledge acquisition, near-transfer performance, and far-transfer performance, whereas topic interest and mental effort were measured by means of Topic Interest Questionnaire and NASA Task Load Index (NASA-TLX) respectively. The analysis outcomes revealed that the self-explanation prompts approach was significantly superior to the instructional-explanation approach in terms of topic knowledge acquisition and near transfer performance. In addition, the results demonstrated that the impact of topic interest was significantly noticeable on far transfer tasks, but not on topic knowledge acquisition and near transfer tasks. On the other hand, the relationship between mental effort investment and test performance was not statistically significant. Finally, an equivocal relationship, which varied depending on the treatment conditions, was discovered between topic interest, mental effort, and test performance. (DIPF/orig.)In der vorliegenden Untersuchung wurden zwei unterschiedliche Lehrmethoden – instruktionale Erklärung und Aufforderung zur Selbsterklärung – angewandt auf das Lernen mit Lösungsbeispielen in einer computergestützten Lernumgebung, die thematisch im Bereich der Fertigungstechnik angesiedelt ist. Die computergestützte Lernumgebung bestand aus einer vom Autor erstellten Lernsoftware, die mit Macromedia Authorware entworfen und entwickelt wurde. Hauptziel der Studie war ein Vergleich der Effekte beider Lehrmethoden auf die Aneignung von Sachwissen sowie die Leistung beim nahen und weiten Transfer. Außerdem wurden die Auswirkungen von Gegenstandsinteresse auf die zuvor genannten Kriterien untersucht und die Beziehungen zwischen Gegenstandsinteresse, mentaler Anstrengung und Lernergebnissen. Insgesamt wurden 76 Studierende im zweiten Jahr ihres Studiums an der Fakultät für Technische Bildung, Universität Tun Hussein Onn Malaysia (UTHM), nach dem Zufallsprinzip in drei Gruppen aufgeteilt: Selbsterklärungsaufforderung (SE: n = 25), instruktionale Erklärung (IE: n = 25) und Kontrollgruppe (n = 26). Mit Pre- und Post-Tests wurden die Aneignung von Sachwissen sowie die nahe und weite Transferleistung erhoben. Gegenstandsinteresse und mentale Anstrengung wurden mit dem Topic Interest–Fragebogen und dem NASA-TLX gemessen. Das Statistik-Paket für die Sozialwissenschaften (SPSS) wurde verwendet, um die Hypothesen an den gesammelten Daten zu prüfen. Die Hypothesenprüfung erfolgte mittels quantitativ statistischer Auswertungsverfahren (Korrelation, Varianzanalyse). (DIPF/Orig.

Alternative Modes for Teaching Mathematical Problem Solving: An Overview

Various modes are proffered as alternatives for teaching mathematical problem solving. Each mode is described briefly, along with general purposes, advantages and disadvantages. Combinations of modes are suggested; general issues identified; recommendations offered; and feedback from teachers summarized

Basic calculation proficiency and mathematics achievement in elementary school children

The relation between skill in simple addition and subtraction and more general math achievement in elementary school is well established but not understood. Both the intrinsic importance of skill in simple calculation for math and the influence of conceptual knowledge and cognitive factors (working memory, processing speed, oral language) on simple calculation and math are plausible. The authors investigated the development of basic calculation fluency and its relations to math achievement and other factors by tracking a group of 259 United Kingdom English children from second to third grade. In both grades the group did not retrieve the solutions to most problems, but their math achievement was typical. Improvement in basic calculation proficiency was partially predicted by conceptual knowledge and cognitive factors. These factors only partially mediated the relation between basic calculation and math achievement. The relation between reading and math was wholly mediated by number measures and cognitive factors

The implementation of a multimedia learning environment for graduate civil engineers

This paper examines the underpinning theory, design and implementation of a computer‐based Multimedia Learning Environment (MLE) for graduate civil engineers. The MLE brings together multimedia technology and Intelligent Tutoring Systems techniques for the purpose of developing in the graduate engineer the skills and understanding needed to produce initial design proposals for real‐world dam spillway design problems

Applying science of learning in education: Infusing psychological science into the curriculum

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

Four approaches to teaching programming

Based on a survey of literature, four different approaches to teaching introductory programming are identified and described. Examples of the practice of each approach are identified representing procedural, visual, and object-oriented programming language paradigms. Each approach is then further analysed, identifying advantages and disadvantages for the student and the teacher. The first approach, code analysis, is analogous to reading before writing, that is, recognising the parts and what they mean. It requires learners to analyse and understand existing code prior to producing their own. An alternative is the building blocks approach, analogous to learning vocabulary, nouns and verbs, before constructing sentences. A third approach is identified as simple units in which learners master solutions to small problems before applying the learned logic to more complex problems. The final approach, full systems, is analogous to learning a foreign language by immersion whereby learners design a solution to a non-trivial problem and the programming concepts and language constructs are introduced only when the solution to the problem requires their application. The conclusion asserts that competency in programming cannot be achieved without mastering each of the approaches, at least to some extent. Use of the approaches in combination could provide novice programmers with the opportunities to acquire a full range of knowledge, understanding, and skills. Several orders for presenting the approaches in the classroom are proposed and analysed reflecting the needs of the learners and teachers. Further research is needed to better understand these and other approaches to teaching programming, not in terms of learner outcomes, but in terms of teachers’ actions and techniques employed to facilitate the construction of new knowledge by the learners. Effective classroom teaching practices could be informed by further investigations into the effect on progression of different toolset choices and combinations of teaching approache

An architecture for rule based system explanation

A system architecture is presented which incorporate both graphics and text into explanations provided by rule based expert systems. This architecture facilitates explanation of the knowledge base content, the control strategies employed by the system, and the conclusions made by the system. The suggested approach combines hypermedia and inference engine capabilities. Advantages include: closer integration of user interface, explanation system, and knowledge base; the ability to embed links to deeper knowledge underlying the compiled knowledge used in the knowledge base; and allowing for more direct control of explanation depth and duration by the user. User models are suggested to control the type, amount, and order of information presented

Engaging the 'Xbox generation of learners' in Higher Education

The research project identifies examples of technology used to empower learning of Secondary school pupils that could be used to inform students’ engagement in learning with technology in the Higher Education sector. Research was carried out in five partnership Secondary schools and one associate Secondary school to investigate how pupils learn with technology in lessons and to identify the pedagogy underpinning such learning. Data was collected through individual interviews with pupils, group interviews with members of the schools’ councils, lesson observations, interviews with teachers, pupil surveys, teacher surveys, and a case study of a learning event. In addition, data was collected on students’ learning with technology at the university through group interviews with students and student surveys in the School of Education and Professional Development, and through surveys completed by students across various university departments. University tutors, researchers, academic staff, learning technology advisers, and cross sector partners from the local authority participated in focus group interviews on the challenges facing Higher Education in engaging new generations of students, who have grown up in the digital age, in successful scholarly learning