The neurocognitive gains of diagnostic reasoning training using simulated interactive veterinary cases

The present longitudinal study ascertained training-associated transformations in the neural underpinnings of diagnostic reasoning, using a simulation game named “Equine Virtual Farm” (EVF). Twenty participants underwent structural, EVF/task-based and resting-state MRI and diffusion tensor imaging (DTI) before and after completing their training on diagnosing simulated veterinary cases. Comparing playing veterinarian versus seeing a colorful image across training sessions revealed the transition of brain activity from scientific creativity regions pre-training (left middle frontal and temporal gyrus) to insight problem-solving regions post-training (right cerebellum, middle cingulate and medial superior gyrus and left postcentral gyrus). Further, applying linear mixed-effects modelling on graph centrality metrics revealed the central roles of the creative semantic (inferior frontal, middle frontal and angular gyrus and parahippocampus) and reward systems (orbital gyrus, nucleus accumbens and putamen) in driving pre-training diagnostic reasoning; whereas, regions implicated in inductive reasoning (superior temporal and medial postcentral gyrus and parahippocampus) were the main post-training hubs. Lastly, resting-state and DTI analysis revealed post-training effects within the occipitotemporal semantic processing region. Altogether, these results suggest that simulation-based training transforms diagnostic reasoning in novices from regions implicated in creative semantic processing to regions implicated in improvised rule-based problem-solving

Modelling instruction effect with different reasoning ability on physics conceptual understanding by controlling the prior knowledge

Modelling instruction is systematic instructional activity for constructing and applying scientific knowledge in Physics lesson. The purpose of this research is to determine the effect of Modelling instruction with different reasoning abilities on understanding physical concepts by controlling students’ prior knowledge. This research used experimental method with 2x2 factorial design with two Modelling instruction classes and two conventional classes with a total of 176 students. The instrument used was reasoning ability test, prior knowledge test, and physics concept test. It used LCTSR (Lawson’s Classroom Test of Scientific Reasoning) instrument. Prior knowledge test instruments consisted of 25 problems to identify how deep the students understand the topic before they undergo the learning process and physics concept test consisted of 25 problems. Based on the statistical test using two factor Ancova, it proved that there was a significant difference in students’ ability to master the physics concept between using Modelling instruction learning model and using conventional learning model. The result showed that the Modelling instruction increasing conceptual understanding better than conventional learning. There are two important parts in the Modelling instruction that are model development and model deployment. This study also confirms that there are significant differences in understanding the concepts between students of high reasoning ability and low reasoning ability. Students with high reasoning abilities have a better understanding of concepts than students with low reasoning abilities

Exploring the Neural Mechanisms of Physics Learning

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 be grounded in educational theory and pedagogy, and provide deep insights into the neural mechanisms by which students learn physics

Toward a Neurobiological Basis for Understanding Learning in University Modeling Instruction Physics Courses

Modeling Instruction (MI) for University Physics is a curricular and pedagogical approach to active learning in introductory physics. A basic tenet of science is that it is a model-driven endeavor that involves building models, then validating, deploying, and ultimately revising them in an iterative fashion. MI was developed to provide students a facsimile in the university classroom of this foundational scientific practice. As a curriculum, MI employs conceptual scientific models as the basis for the course content, and thus learning in a MI classroom involves students appropriating scientific models for their own use. Over the last 10 years, substantial evidence has accumulated supporting MI's efficacy, including gains in conceptual understanding, odds of success, attitudes toward learning, self-efficacy, and social networks centered around physics learning. However, we still do not fully understand the mechanisms of how students learn physics and develop mental models of physical phenomena. Herein, we explore the hypothesis that the MI curriculum and pedagogy promotes student engagement via conceptual model building. This emphasis on conceptual model building, in turn, leads to improved knowledge organization and problem solving abilities that manifest as quantifiable functional brain changes that can be assessed with functional magnetic resonance imaging (fMRI). We conducted a neuroeducation study wherein students completed a physics reasoning task while undergoing fMRI scanning before (pre) and after (post) completing a MI introductory physics course. Preliminary results indicated that performance of the physics reasoning task was linked with increased brain activity notably in lateral prefrontal and parietal cortices that previously have been associated with attention, working memory, and problem solving, and are collectively referred to as the central executive network. Critically, assessment of changes in brain activity during the physics reasoning task from pre- vs. post-instruction identified increased activity after the course notably in the posterior cingulate cortex (a brain region previously linked with episodic memory and self-referential thought) and in the frontal poles (regions linked with learning). These preliminary outcomes highlight brain regions linked with physics reasoning and, critically, suggest that brain activity during physics reasoning is modifiable by thoughtfully designed curriculum and pedagogy

Using Student-Generated Problems (SGP) As An Instructional Strategy To Enhance Undergraduate Engineering Students’ Knowledge Application Ability And Problem-Solving Skills

The Student-Generated Problems (SGP) instructional strategy represents an exclusive area of real-world practice used by some educators to give powerful support and responsibility to college students for their learning experience (Mestre, 2002; Zurcher, Coppola, & McNeil, 2016). Undergraduate Engineering students often have difficulty applying gained knowledge in real-world settings and are reportedly underprepared for workplace challenges (Luo et al., 2015; Negro et al. 2019). This study examined the effects of the SGP instructional strategy used in an undergraduate Electrical Engineering course to determine students’ abilities to apply conceptual knowledge and problem-solving skills in real problem lab activities. The need for this study was to prepare students to be able to function well in the workplace environment in the future. The study also investigated whether there were relationships between students’ skills in SGP and their problem-solving skills, conceptual, and application knowledge of Electrical Engineering concepts under study. This investigation employed a quantitative approach, using a within-subject design with pre-post testing. A single group of participants experienced both the regular and SGP instructional strategy. This study’s independent variables were the type of instructional strategy–traditional class instruction and the SGP approach. The dependent variables are the students’ learning outcomes. This quantitative study used knowledge test (pre and post) to test the students’ conceptual knowledge, a Problem-Solving Inventory (PSI) survey to assess the students’ self-perception of problem-solving skills, and a problem identification rubric to assess students’ knowledge application in the SGP activity. Limited differences were revealed in the control and experimental group participants’ responses of their conceptual knowledge, knowledge application abilities, and self- perception of problem-solving skills. Test scores in the knowledge areas did not have a statistically significant overall relationship with the most of study variables. However, the test revealed that the difference in the test scores for the approach avoidance style construct was statistically significant. Further investigation on the connections between these study variables and the SGP instructional strategy is needed to provide a more insightful depiction of the effects of the Student-Generated Problems approach on students’ development of conceptual knowledge, knowledge application, and problem-solving skills in electrical engineering concepts. Although this study did not report a significant difference between the SGP and the traditional group, there appears to be a difference between the mean scores among the two groups. Hence, it can be implied that SGP has the potential to promote knowledge utilization and problem-solving skills among engineering students. This is because SGP enables students to connect and relate classroom concepts to real-world problems, and as a result contextualizing their learning. The findings of this study are significant for engineering instructors who intend to promote knowledge application and problem-solving skills in their teaching. Also, SGP is a constructivist learning approach and the results from this study suggest that it may offer alternative instructions to the traditional teacher-centered approach, thereby helping instructors better prepare their students for their future workplace challenges

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