Students’ difficulties regarding vector representations in free-body system

We discuss a case study of the influence of epistemology on learning for a student in an introductory college physics course. An analysis of videotaped class work, written work, and interviews indicates that many of the student’s difficulties were epistemological in nature. Our primary goals are to show instructors and curriculum developers that a student’s personal epistemological stance—her ideas about knowledge and learning—can have a direct, causal influence on her learning of physics, and to describe a mechanism for this interaction. This influence exists even when research-based curriculum materials provide implicit epistemological support. For this reason, curriculum materials and teaching techniques could become more effective by explicitly attending to students’ epistemologies.

This dissertation presents a series of neuroimaging investigations and achievements that strive to deepen and broaden our understanding of human problem solving and physics learning. Neuroscience conceives of dynamic relationships between behavior, experience, and brain structure and function, but how neural changes enable human learning across classroom instruction remains an open question. At the same time, physics is a challenging area of study in which introductory students regularly struggle to achieve success across university instruction. Research and initiatives in neuroeducation promise a new understanding into the interactions between biology and education, including the neural mechanisms of learning and development. These insights may be particularly useful in understanding how students learn, which is crucial for helping them succeed. Towards this end, we utilize methods in functional magnetic resonance imaging (fMRI), as informed by education theory, research, and practice, to investigate the neural mechanisms of problem solving and learning in students across semester-long University-level introductory physics learning environments. In the first study, we review and synthesize the neuroimaging problem solving literature and perform quantitative coordinate-based meta-analysis on 280 problem solving experiments to characterize the common and dissociable brain networks that underlie human problem solving across different representational contexts. Then, we describe the Understanding the Neural Mechanisms of Physics Learning project, which was designed to study functional brain changes associated with learning and problem solving in undergraduate physics students before and after a semester of introductory physics instruction. We present the development, facilitation, and data acquisition for this longitudinal data collection project. We then perform a sequence of fMRI analyses of these data and characterize the first-time observations of brain networks underlying physics problem solving in students after university physics instruction. We measure sustained and sequential brain activity and functional connectivity during physics problem solving, test brain-behavior relationships between accuracy, difficulty, strategy, and conceptualization of physics ideas, and describe differences in student physics-related brain function linked with dissociations in conceptual approach. The implications of these results to inform effective instructional practices are discussed. Then, we consider how classroom learning impacts the development of student brain function by examining changes in physics problem solving-related brain activity in students before and after they completed a semester-long Modeling Instruction physics course. Our results provide the first neurobiological evidence that physics learning environments drive the functional reorganization of large-scale brain networks in physics students. Through this collection of work, we demonstrate how neuroscience studies of learning can

At large research universities, physics graduate teaching assistants (TAs) are often responsible for grading in courses at all levels. However, few studies have focused on TAs’ grading practices in introductory and advanced physics courses. This study was designed to investigate whether physics graduate TAs grade students in introductory physics and quantum mechanics using different criteria and if so, why they may be inclined to do so. To investigate possible discrepancies in TAs’ grading approaches in courses at different levels, we implemented a sequence of instructional activities in a TA professional development course that asked TAs to grade student solutions of introductory physics and upper-level quantum mechanics problems and explain why, if at all, their grading approaches were different or similar in the two contexts. We analyzed the differences in TAs’ grading approaches in the two contexts and discuss the reasons they provided for the differences in their grading approaches in introductory physics and quantum mechanics in individual interviews, class discussions, and written responses. We find that a majority of the TAs graded solutions to quantum mechanics problems differently than solutions to introductory physics problems. In quantum mechanics, the TAs focused more on physics concepts and reasoning and penalized students for not showing evidence of understanding. The findings of the study have implications for TA professional development programs, e.g., the importance of helping TAs think about the difficulty of a problem from an introductory students’ perspective and reflecting on the benefits of formative assessment.

This study aims to design and develop an interactive software for teaching and learning physics about motion and vectors analysis. This study also assesses its effectiveness in classroom and assesses the learning motivation of SMA Pekanbaru’s students. The software is developed using ADDIE Model design and Life Cycle Model and built using the program Delphi Seventh version by considering design, technical, pedagogical, and the content of its learning material. The research was conducted through quasi-experiment upon XI grade students of SMA Pekanbaru. The evaluation of the software was done by the experts of design, pedagogic, technical, and learning material. After the evaluation, this software was also evaluated by expert users consisting of 31 experienced physics teachers from senior high schools in Pekanbaru. The population consisted of six classes XI grade students of SMA Pekanbaru, where two of them are randomly selected. One of the two classes, then, was determined as experimental group while the other as control group. The experimental group consisted of 38 students, with 19 boys and 19 girls whereas the control group consisted of 37 students with 17 boys and 19 girls. The sampling was based on homogeneity and normality test. The finding shows that the reliability based on Cronbach Alpha is ?=0.740. Learning physics using interactive software “Motion and Vector Analysis” has affected students’ conceptual understanding by 81.16 compared to students learning using traditional method, which was only 35.86. Through t-test, it was found that students’ conceptual understanding was different from value t= -22.725, p= 0.00 with ?= 0.05. Students’ motivation to learn in the post-test was better for students who learn using the software than those who learned using traditional method. Finding based on gender found that the conceptual understanding of male students and female students were similar. Learning motivation for both male and female students has also increased. Therefore, learning physics with “Motion and Vectors Analysis” topics using interactive software is more effective than learning using traditional method.

The application of multiple representation approach in physics learning has been adopted by several researchers, especially to improve the ability and achieve effective learning. The use of multiple representation approach in physics learning over the past few years has provided good results on student learning outcomes, including being able to improve and develop abstract physics concepts, scientific complex skills, metacognition skills, and problem solving. This research is the result of a review based on bibliometric analysis of multiple representations in physics learning. Data was obtained based on Scopus sources from 2013 to 2023, then obtained data as many as 121 articles which then the data was processed using RStudio and VOSViewer. Based on the results of the analysis using Rstudio and VOSViewer, it also displays other information such as year, country, author, institution, journal, co-word analysis, co-occurrence network, and visual density on occurrence. The results of this study provide a framework of the current trends of multiple representations in physics learning both in the present and some indicators that can be used as opportunities for future research.

The study of student misconceptions about physics concepts has long been an important area of inquiry in physics education research (PER). The research discussed in this dissertation builds upon the developments in PER by exploring the prevalence of consistently held undergraduate student misconceptions in introductory calculus-based physics. This thesis explores the nature of student misconceptions, mistakes, and naive answering patterns in both introductory undergraduate Newtonian mechanics and electromagnetism by applying a network analytic technique called module analysis to student responses to different concept inventories from institutions of various levels of incoming physics preparation. Each study applying these methods also demonstrates how they can also be used to inform future inventory development. Network analysis was also used to study the growth and evolution of the First2 Network, a project with the goal of doubling the retention rate of STEM students in West Virginia, with a particular emphasis on rural and first-generation students. The final part of this thesis compares students’ performance and attendance in an introductory electricity and magnetism course before and after the COVID-19 pandemic.

The learning achievement of high school students of Malang city in physics was still low, thus the appropriate learning strategies was necessaryto optimize the physics learning achievement. V diagram procedural scaffoldingin Group Investigation was designed with the aim to optimize student learning achievement. The purpose of this study was to examine differences in learning achievement of groups of students who learn through Scaffolding Procedural strategies in Group Investigation and groups of students who are studying with Group Investigation, to examine differences in student achievement between thosewith high and low prior knowledge levels, and to examine the interaction between learning strategies (procedural scaffolding in GI and GI) and initial knowledge of students towardlearning achievement. The method used in this study wasquasi-experimental.The design used in this study was Two Factorial Design consisted of two experimental classes and two control classes each. The results indicated that learning achievement group of students who learn through proceduralscaffolding in Group Investigation was higher than the group of students who are studying with Group Investigation. The learning achievements of students with high prior knowledge surpassed those with low prior knowledge. Last, there was an interaction between the learning strategies and prior knowledge of students toward learning achievement.

While emphasis is often placed on assessing students' conceptual knowledge, less has been placed on investigating affective aspects of student biology learning. In this paper, we explore self-efficacy, sense of belonging, and science identity, as well as emerging assessment tools to monitor these dimensions of students' learning.

[This paper is part of the Focused Collection on Upper Division Physics Courses.] Compared with introductory physics, relatively little is known about the development of expertise in advanced physics courses, especially in the case of quantum mechanics. Here, we describe a framework for understanding the patterns of student reasoning difficulties and how students develop expertise in quantum mechanics. The framework posits that the challenges many students face in developing expertise in quantum mechanics are analogous to the challenges introductory students face in developing expertise in introductory classical mechanics. This framework incorporates both the effects of diversity in upper-level students’ prior preparation, goals, and motivation in general (i.e., the facts that even in upper-level courses, students may be inadequately prepared, have unclear goals, and have insufficient motivation to excel) as well as the “paradigm shift” from classical mechanics to quantum mechanics. The framework is based on empirical investigations demonstrating that the patterns of reasoning, problem-solving, and self-monitoring difficulties in quantum mechanics bear a striking resemblance to those found in introductory classical mechanics. Examples from research in quantum mechanics and introductory classical mechanics are discussed to illustrate how the patterns of difficulties are analogous as students learn to unpack the respective principles and grasp the formalism in each knowledge domain during the development of expertise. Embracing such a framework and contemplating the parallels between the difficulties in these two knowledge domains can enable researchers to leverage the extensive literature for introductory physics education research to guide the design of teaching and learning tools for helping students develop expertise in quantum mechanics.Received 29 September 2014DOI:https://doi.org/10.1103/PhysRevSTPER.11.020119This article is available under the terms of the Creative Commons Attribution 3.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical Society

Solving problems presented in multiple representations is an important skill for future physicists and engineers. However, such a task is not easy for most students taking introductory physics courses. We conducted teaching/learning interviews with 20 students in a first‐semester calculus‐based physics course on several topics in introductory mechanics. These interviews helped identify the common difficulties students encountered when solving physics problems posed in multiple representations as well as the hints that help students overcome those difficulties. We found that most representational difficulties arise due to the lack of students’ ability to associate physics knowledge with corresponding mathematical knowledge. Based on those findings, we developed, tested and refined a set of problem‐solving exercises to help students learn to solve problems in graphical and equational representations. We present our findings on students’ common difficulties with graphical and equational representations, the problem‐solving exercises and their impact on students’ problem solving abilities.

In constructivist literature, there are three main accounts of the role that children's prior knowledge plays in learning Physics and in developing mature scientific views: the knowledge-as-theory, the knowledge-as-elements and the knowledge-as-system accounts. The first one defines children's prior knowledge in terms of naive theories which are incorrect but display enough coherence to resist restructuring. In this view, the purpose of Physics education is to find ways to dislodge children's naive theories and replace them effectively with scientific concepts. The second account interprets prior knowledge as a scattered map of knowledge elements that children develop on their own based on previous day-to-day experience. Contrary to the first account, the second one stresses that such knowledge elements are mostly correct, but what they miss is a coherent system of relations to link them properly. In this view, Physics education is expected to develop children's existing knowledge elements into consistent sets of scientific beliefs. The third account defines prior knowledge in a more complex manner. The knowledge-as-system account highlights a series of dynamic and heterogeneous properties that most children's concepts about nature seem to possess. In this view, children's naive ideas display both knowledge-as-theory and knowledge-as-elements characteristics, so the task of Physics education is to address each characteristic in a differentiated manner and with adequate instruments. Keeping this background in mind, the main aim of the present paper is to evaluate constructivist accounts of children's prior knowledge from the point of view of a pragmatic evidence-based approach to learning scientific concepts - i.e. which of these views is convincingly supported by empirical research in Physics education and what are we to understand about children's knowledge from empirical investigations of scientific practices in the classroom.

Nursing practise plays an important role in transferring nursing knowledge to nursing students. From the related literature review, prior knowledge will affect how learners gain new knowledge. There has been no direct examination of the prior knowledge interaction effect on students' performance and its influence on nursing students when evaluating the knowledge transfer success factors. This study explores (1) the critical success factors in transferring nursing knowledge, (2) the impact of prior knowledge when evaluating the success factors for transferring nursing knowledge. This research utilizes in-depth interviews to probe the initial success factor phase. A total of 422 valid questionnaires were conducted by the authors. The data were analysed by comparing the mean score and t-test between two groups. Seventeen critical success factors were identified by the two groups of students. Twelve items were selected to examine the diversity in the two groups. Students with prior knowledge were more independent than the other group. They also preferred self-directed learning over students without prior knowledge. Students who did not have prior knowledge were eager to take every opportunity to gain experience and more readily adopted new knowledge.

The paper aims to fulfil three main functions: (1) to serve as an introduction for the physics education community to the functioning of large language models (LLMs), (2) to present a series of illustrative examples demonstrating how prompt-engineering techniques can impact LLMs performance on conceptual physics tasks and (3) to discuss potential implications of the understanding of LLMs and prompt engineering for physics teaching and learning. We first summarise existing research on the performance of a popular LLM-based chatbot (ChatGPT) on physics tasks. We then give a basic account of how LLMs work, illustrate essential features of their functioning, and discuss their strengths and limitations. Equipped with this knowledge, we discuss some challenges with generating useful output with ChatGPT-4 in the context of introductory physics, paying special attention to conceptual questions and problems. We then provide a condensed overview of relevant literature on prompt engineering and demonstrate through illustrative examples how selected prompt-engineering techniques can be employed to improve ChatGPT-4’s output on conceptual introductory physics problems. Qualitatively studying these examples provides additional insights into ChatGPT’s functioning and its utility in physics problem-solving. Finally, we consider how insights from the paper can inform the use of LLMs in the teaching and learning of physics.

Global land cover (GLC) products can be utilized to provide geographical supervision for remote sensing representation learning, which has significantly improved downstream tasks’ performance and decreased the demand of manual annotations. However, the time differences between remote sensing images and GLC products may introduce deviations in geographical supervision. In this paper, we propose a Geographical supervision Correction method (GeCo) for remote sensing representation learning. Deviated geographical supervision generated by GLC products can be corrected adaptively using the correction matrix during network pre-training and joint optimization process is designed to simultaneously update the correction matrix and network parameters. Additionally, we identify prior knowledge on geographical supervision to guide representation learning and restrict the correction process. The prior knowledge named “minor changes” implies that the geographical supervision may not change significantly, whereas the prior knowledge named “spatial aggregation” implies that land covers are aggregated in their spatial distribution. According to the prior knowledge, corresponding regularization terms are proposed to prevent abrupt changes in geographical supervision correction process and excessive smoothing of network outputs, thereby ensuring the adaptive correction process’s correctness. Experimental results demonstrate that our proposed method outperforms random initialization, ImageNet pre-training, and other representation learning methods on a variety of downstream tasks. In particular, when compared to the method that learns representations directly from deviated geographical supervision, it is proved that our method can eliminate the influence of deviations and further improve the effect of representation learning.

The lack of diversity and the under-performance of underrepresented students in STEM courses have been the focus of researchers in the last decade. In particular, many hypotheses have been put forth for the reasons for the under-representation and under-performance of women in physics. Here, we present a framework for helping all students learn in science courses that takes into account four factors: (1) the characteristics of instruction and learning tools, (2) student characteristics, (3) implementation of instruction and learning tools, and (4) the students’ environments. While there has been much research on factor 1 (characteristics of instruction and learning tools), there has been less focus on factor 2 (students’ characteristics, and in particular, motivational factors). Here, we focus on the baseline characteristics of introductory physics students obtained from survey data to inform factor 2 of the framework. A longitudinal analysis of students’ motivational characteristics in two-semester introductory physics courses was performed by administering pre- and post-surveys that evaluated students’ self-efficacy, grit, fascination with physics, value associated with physics, intelligence mindset, and physics epistemology. We found that female students reported lower levels of self-efficacy, fascination, and value associated with physics, and held a more “fixed” view of intelligence in the context of physics compared to male students. Female students’ fascination and value associated with physics decreased significantly more than males’ after an introductory physics course sequence. In addition, females’ view of physics intelligence became more “fixed” compared to males’ by the end of an introductory physics course sequence. Grit was the only factor on which females reported averages that were equal to or higher than males throughout introductory physics courses. The findings inform the framework and have implications for the development and implementation of effective pedagogies and learning tools to help all students learn.