Revisiting Clickers: In-Class Questions Followed by At-Home Reflections Are Associated with Higher Student Performance on Related Exam Questions

Clicker questions are a commonly used active learning technique that stimulates student interactions to help advance understanding of key concepts. Clicker questions are often administered with an initial vote, peer discussion, and a second vote, followed by broader classroom explanation. While clickers can promote learning, some studies have questioned whether students maintain this performance on later exams, highlighting the need to further understand how student answer patterns relate to their understanding of the material and to identify ways for clickers to benefit a broader range of students. Systematic requizzing of concepts during at-home assignments represents a promising mechanism to improve student learning. Thus, we paired clicker questions with at-home follow-up reflections to help students articulate and synthesize their understandings. This pairing of clickers with homework allowed us to decipher how student answer patterns related to their underlying conceptions and to determine if revisiting concepts provided additional benefits. We found that students answering both clicker votes correctly performed better on isomorphic exam questions and that students who corrected their answers after the first vote did not show better homework or exam performance than students who maintained an incorrect answer across both votes. Furthermore, completing the followup homework assignment modestly boosted exam question performance. Our data suggest that longer-term benefits of clickers and associated homework may stem from students having repeated opportunities to retrieve, refine, and reinforce emerging conceptions

The Use of Multiple Representations in Undergraduate Physics Education: What Do we Know and Where Do we Go from Here?

Using multiple representations (MR) such as graphs, symbols, diagrams, and text, is central to teaching and learning in physics classrooms. While different studies have provided evidence of the positive impact of the use of MR on physics learning, a comprehensive overview of existing literature on the use of MR in physics education, especially at the undergraduate level, is missing. This manuscript addresses this gap in the literature by reporting on the outcomes of a systematic review study that aimed to provide an overview of the existing knowledge base, to identify gaps in the knowledge base, and to propose future research about the use of MR in the context of undergraduate physics education. For the purpose of this study, we reviewed 24 empirical studies published between 2002 and 2019 in scientific, peer-reviewed journals in the context of undergraduate physics education. The outcomes of this review study are discussed under these themes (a) In what ways does the use of MR in instruction support student learning? (b) What kinds of representations do students use? (c) What difficulties do students face in using MR? (d) What is the relation between students’ use of MR and students’ problem-solving skills? and, (e) What is the added value of technology integration in teaching with MR? We identify gaps in the existing knowledge base, and we propose future research directions in these three areas: (a) Exploring the use of MR in university physics textbooks; (b) Blending of different kinds of MR; and, (c) The use of virtual reality applications

Teacher Quality and Student Inequality (Revised 2014)

This paper examines the extent to which the allocation of teachers within and across public high schools is contributing to inequality in student test score performance. Using ten years of administrative data from North Carolina public high schools, I estimate a flexible education production function in which student achievement reflects student inputs, teacher quality, school quality, and a school-specific scaling factor that allows the impact of teaching quality to vary across schools. The existence of nearly 3,000 teacher transfers, combined with a testable exogenous mobility assumption, allows separate identification of each teacher’s quality from both school quality and school sensitivity to teacher quality. I find that teaching quality is surprisingly equitably distributed both within and across high schools. Schools predominantly serving underprivileged students employ teachers who are only slightly below average, and most students receive a mix of their school’s good and bad teachers. Overall, I find that the allocation of teacher and school inputs at the high school level contributes only 4% to the achievement gap between the top and bottom deciles of an index of student background. Finally, I find that schools that disproportionately serve disadvantaged students tend to be more sensitive to teacher quality

Exploring Indonesian preservice physics teachers’ development of physics identity and physics teacher identity

This study explores preservice physics teachers’ physics identity and physics teacher identity development following a course that incorporated the use of multiple representations (MR) based instructional approach. The study is grounded within research evidence showing that specific instructional practices might influence the development of identity. The study began with a systematic literature review about the role of the MR-based instructional approach in the physics education field, especially for undergraduate students. Following that, an introductory university physics course was redesigned to incorporate the use of MR. Next, a research exploration through a quasi-experiment design study was performed to examine the development of the physics identity of 61 preservice physics teachers. Lastly, a qualitative case study was conducted to explore 21 preservice physics teachers’ identity development. The results of the study revealed that the development of physics identity and physics teacher identity is directly related to “competence” which refers to conceptual understanding and problem-solving abilities. In addition, the development of identity has been found to be directly related to “self-view” and “interest”. These findings have implications for future research aimed at supporting preservice physics teachers’ identity development

New Methodologies for Examining and Supporting Student Reasoning in Physics

Learning how to reason productively is an essential goal of an undergraduate education in any STEM-related discipline. Many non-physics STEM majors are required to take introductory physics as part of their undergraduate programs. While certain physics concepts and principles may be of use to these students in their future academic careers and beyond, many will not. Rather, it is often expected that the most valuable and longlasting learning outcomes from a physics course will be a repertoire of problem-solving strategies, a familiarity with mathematizing real-world situations, and the development of a strong set of qualitative inferential reasoning skills. For more than 40 years, the physics education research community has produced many research-based instructional materials that have been shown to improve student conceptual understanding and other targeted learning outcomes (e.g., problem solving). It is often tacitly assumed that such materials also improve students’ qualitative reasoning skills, but there is no documented evidence of this, to date, in the literature. Furthermore, a growing body of research has revealed that a focus on conceptual understanding does not always result in the anticipated performance outcomes. Indeed, students may demonstrate solid conceptual understanding on one physics question but fail to demonstrate that same understanding on a closely related question. This body of research suggests that reasoning processes general to all humans (i.e., domain-general processes) may impact how students understand and reason with physics concepts. Methodologies that separate (to the degree possible) the reasoning involved in a physics problem from the conceptual understanding necessary to correctly answer that problem are necessary for gaining insight into how conceptual understanding and domain-general reasoning processes interact. In order to explore such research questions, new research tools and analysis methodologies are required. Physics education researchers pursuing these questions have begun to embrace data-collection methodologies outside of the written free-response questions and think-aloud interviews that are ubiquitous in discipline-based education research. Some of these researchers have also begun to utilize dual-process theories of reasoning (DPToR) as an analysis framework. Dual-process theories arise from findings in cognitive science, social psychology, and the psychology of reasoning. These theories tend to be mechanistic in nature; as such, they provide a framework that can be prescriptive rather than solely descriptive, thereby providing a theoretical basis for examining the interplay of domain-general and domain-specific reasoning. In the work described in this thesis, we sought to gain greater insight into the nature of student reasoning in physics and the extent to which it is impacted by the domain-general phenomena explored by cognitive science. This was accomplished by developing and implementing new methodologies to examine qualitative inferential reasoning that separate reasoning skills from understanding of a particular physics concept. In this work we present two such methodologies: reasoning chain construction tasks, in which students are provided with correct reasoning elements (i.e., true statements about the physical situation as well as correct concepts and mathematical relationships) and are asked to assemble them into an argument in order to answer a physics question; and possibility exploration tasks, which are designed to measure student ability to consider multiple possibilities when answering a physics problem. The overarching goal of these novel tasks is to explore mechanistic processes related to the generation of qualitative inferential reasoning chains and to uncover insight into the nature of student reasoning more generally. The work reported in this dissertation has yielded a variety of important results. In concert with reasoning-chain construction tasks, the dual-process framework has been leveraged to provide testable hypotheses about student reasoning and to inform the design of an instructional intervention to support student reasoning. By applying network analysis approaches to data produced by reasoning chain construction tasks with network analysis, insights were uncovered regarding the structure of student reasoning in different contexts, and the development of a coherent reasoning structure over the course of a two-semester physics course was documented. Finally, students’ tendency to explore possibilities has been, both in the literature and in this dissertation, found to impact performance on physics questions. This tendency is examined and a possible mechanism controlling this tendency has been proposed. Taken together, these investigations and findings constitute substantive advances in how student reasoning is studied and serve to open new doors for future research

Testing students' ability to use derivatives, integrals, and vectors in a purely mathematical context and in a physical context

In this article, we discuss the development and the administration of a multiple-choice test, which we named Test of Calculus and Vectors in Mathematics and Physics (TCV-MP), aimed at comparing students' ability to answer questions on derivatives, integrals, and vectors in a purely mathematical context and in the context of physics. The comparison between the two contexts was achieved by using parallel (isomorphic) questions in mathematics and physics. The final version of the test contains 34 items (17 in a purely mathematical context and 17 in the context of physics) involving different representations (graphs, words, numbers, and formal expressions) of the concepts covered by the test. The test was administered in Spring 2018 to 1252 first-year students enrolled in 23 different degree programs of the School of Science and the School of Engineering of the University of Padua. We assessed the validity, reliability, and discriminatory power of the test both as a whole and at the single-item level, obtaining values within the desired ranges. The analysis of students' answers to individual items and the comparison between parallel mathematics and physics items provides insights into the factors that affect students' ability to use derivatives, integrals, and vectors in the context of introductory physics. We believe that the instrument we have developed can be useful not only for research purposes, but also for instructors and for students

RiPPLE: A crowdsourced adaptive platform for recommendation of learning activities

© 2019, UTS ePRESS. All rights reserved. This paper presents a platform called RiPPLE (Recommendation in Personalised Peer-Learning Environments) that recommends personalized learning activities to students based on their knowledge state from a pool of crowdsourced learning activities that are generated by educators and the students themselves. RiPPLE integrates insights from crowdsourcing, learning sciences, and adaptive learning, aiming to narrow the gap between these large bodies of research while providing a practical platform-based implementation that instructors can easily use in their courses. This paper provides a design overview of RiPPLE, which can be employed as a standalone tool or embedded into any learning management system (LMS) or online platform that supports the Learning Tools Interoperability (LTI) standard. The platform has been evaluated based on a pilot in an introductory course with 453 students at The University of Queensland. Initial results suggest that the use of the RiPPLE platform led to measurable learning gains and that students perceived the platform as beneficially supporting their learning

Ready Student One: Exploring the predictors of student learning in virtual reality

Immersive virtual reality (VR) has enormous potential for education, but classroom resources are limited. Thus, it is important to identify whether and when VR provides sufficient advantages over other modes of learning to justify its deployment. In a between-subjects experiment, we compared three methods of teaching Moon phases (a hands-on activity, VR, and a desktop simulation) and measured student improvement on existing learning and attitudinal measures. While a substantial majority of students preferred the VR experience, we found no significant differences in learning between conditions. However, we found differences between conditions based on gender, which was highly correlated with experience with video games. These differences may indicate certain groups have an advantage in the VR setting.Comment: 28 pages, 7 figures, 4 tables. Published in PLOS ONE March 25, 202

Technology-Based Personalization: Instructional Reform in Five Public Schools

This dissertation addresses the question: How does an attempt to redesign instructional delivery using technology-based personalization affect the technical core of teaching, learning, and student outcomes? In recent years, many prominent educators, business leaders, and philanthropists have suggested that schools be redesigned to personalize students’ learning experiences using technology. However, the justification for these reforms remains largely theoretical. Empirical research on technology-based personalization is sparse, and what little research does exist focuses predominantly on macro effects rather than the specific school-level, class-level, student-level, and lesson-level mechanisms that contribute to overall student achievement. The absence of research that pushes inside the “black box” of implementation is particularly problematic given a century of failed attempts to reform the technical core of instructional delivery, with symbolic reforms typically withering in the face of institutional resistance. This study attempts to address that gap by examining the implementation of an innovative model for using technology-based personalization to deliver middle school math instruction. I draw upon theoretical tools from institutional theory, instructional improvement, and the history of educational reform to deepen our understanding of how technology-based personalization affects the role of students and teachers, the logistics of content delivery, and students’ learning outcomes. Unlike previous studies in K-12 settings, which typically use summative assessments and virtual control groups to estimate aggregate effects on student learning, this study examines the relationships among a diverse set of lesson-level variables, including instructional modality, instructional content, group size and composition, teacher characteristics, student characteristics, and learning outcomes. In doing so, this study contributes to our understanding of the on-the-ground processes and mechanisms by which technology- based personalization affects (or does not affect) student learning. Although the instructional model documented in this case study will remain anonymous, it is well known and respected among educators and philanthropists, and regarded as one of the most prominent and archetypical examples of technology-based personalization currently active in American schools. Using multiple methods, including novel applications of hierarchical linear modeling, cluster analysis, and heatmap data visualization, I explore: (a) the degree to which ground-level implementation of technology-based personalization represents an authentic departure from the traditional technology of schooling, and (b) the relationships among various elements of the model and student learning outcomes. I draw on longitudinal data from a full year of implementation in five schools, including the daily lesson assignments and assessment scores of 1,238 unique students supervised by 48 teachers. This study supports four main findings: (a) the program succeeds in altering the technical core of instruction in several fundamental ways; (b) policy and logistical constraints limit the program’s ability to reform the technical core of instruction to the degree that it aspires; (c) students who enter the program as already higher-performing are more successful on daily exit slips than students who enter the program with lower performance; and (d) the quantitative methods used in this paper represent useful and replicable tools for exploring the data produced by technology-based and personalized models

A Review of Student Difficulties in Upper-Level Quantum Mechanics

Learning advanced physics, in general, is challenging not only due to the increased mathematical sophistication but also because one must continue to build on all of the prior knowledge acquired at the introductory and intermediate levels. In addition, learning quantum mechanics can be especially challenging because the paradigms of classical mechanics and quantum mechanics are very different. Here, we review research on student reasoning difficulties in learning upper-level quantum mechanics and research on students' problem-solving and metacognitive skills in these courses. Some of these studies were multi-university investigations. The investigations suggest that there is large diversity in student performance in upper-level quantum mechanics regardless of the university, textbook, or instructor and many students in these courses have not acquired a functional understanding of the fundamental concepts. The nature of reasoning difficulties in learning quantum mechanics is analogous to reasoning difficulties found via research in introductory physics courses. The reasoning difficulties were often due to over-generalizations of concepts learned in one context to another context where they are not directly applicable. Reasoning difficulties in distinguishing between closely related concepts and in making sense of the formalism of quantum mechanics were common. We conclude with a brief summary of the research-based approached that take advantage of research on student difficulties in order to improve teaching and learning of quantum mechanics