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Register a new userThe impact of incoming preparation and demographics on performance in Physics I: a multi-institution comparison
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We have studied the impact of incoming preparation and demographic variables on student performance on the final exam in physics 1, the standard introductory, calculus-based mechanics course This was done at three different institutions using multivariable regression analysis to determine the extent to which exam scores can be predicted by a variety of variables that are available to most faculty and departments. We have found that the results are surprisingly consistent across the institutions, with the only two variables that have predictive power being math SAT/ACT scores and concept inventory pre-scores. The importance of both variables is comparable and fairly similar across the institutions. They explain 20 - 30 percent of the variation in students performance on the final exam. Most notably, the demographic variables (gender, under-represented minority, first generation to attend college) are not significant. In all cases, although there appear to be gaps in exam performance if one considers only the demographic variable, once these two proxies of incoming preparation are included in the model, there is no longer a demographic gap. There is only a preparation gap that applies equally across the entire student population. This work shows that to properly understand differences in student performance across a diverse population, and hence to design more effective instruction, it is important to do statistical analyses that take multiple variables into account. It also illustrates the importance of having measures that are sensitive to both subject specific and more general preparation. The results suggest that better matching of the course design and teaching to the incoming student preparation will likely be the most effective way to eliminate observed performance gaps across demographic groups while also improving the success of all students.
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A recent paper by Salehi et al. claims that the differences found between major demographic groups on scores in introductory college physics tests are due to differences in pre-college preparation. No evidence is produced, however, to show that preparation differences are more causally important than any other differences. In one case, the male/female difference, the paper actually provides evidence that preparation gaps are unimportant.
Graduate Teaching Assistants (GTAs) are key partners in the education of undergraduates. Given the potentially large impact GTAs can have on undergraduate student learning, it is important to provide them with appropriate preparation for teaching. But GTAs are students themselves, and not all of them desire to pursue an academic career. Fully integrating GTA preparation into the professional development of graduate students lowers the barrier to engagement so that all graduate students may benefit from the opportunity to explore teaching and its applications to many potential career paths. In this paper we describe the design and implementation of a GTA Preparation course for first-year Ph.D. students at the Georgia Tech School of Physics. Through a yearly cycle of implementation and revision, guided by the 3P Framework we developed (Pedagogy, Physics, Professional Development), the course has evolved into a robust and comprehensive professional development program that is well-received by physics graduate students.
This paper investigates the impact of institutes and papers over time based on the heterogeneous institution-citation network. A new model, IPRank, is introduced to measure the impact of institution and paper simultaneously. This model utilises the heterogeneous structural measure method to unveil the impact of institution and paper, reflecting the effects of citation, institution, and structural measure. To evaluate the performance, the model first constructs a heterogeneous institution-citation network based on the American Physical Society (APS) dataset. Subsequently, PageRank is used to quantify the impact of institution and paper. Finally, impacts of same institution are merged, and the ranking of institutions and papers is calculated. Experimental results show that the IPRank model better identifies universities that host Nobel Prize laureates, demonstrating that the proposed technique well reflects impactful research.
The physics that underpins modern technology is based on Einsteins theories of relativity and quantum mechanics. Most school students complete their compulsory science education without being taught any of these Einsteinian concepts. Only those who take a specialised physics course have the opportunity to learn modern physics. In 2011, the first study of a modern physics teaching intervention with an Australian upper primary (aged 10{11) class was conducted. The initial intervention was the first step of the Einstein-First collaboration towards challenging the current paradigm of Newtonian teaching in schools. It was found that modern physics concepts could be taught to these students. In 2020, 11 participants of the initial study (out of a total of 26) were contacted for a follow-up questionnaire and interview to investigate any long-term impact. The results of the follow-up indicate that the intervention maintained a positive impression on participants. The models and analogies used during the six-week intervention were highly memorable. The participants indicated that they found the intervention to be beneficial to their future learning. Even close to ten years after the intervention, the participants remembered several key concepts (such as curved space-time). The long-term follow-up indicates that Einsteinian physics can be taught at the upper primary level and be recalled several years later.
A goal of Introductory Physics for Life Sciences (IPLS) curricula is to prepare students to effectively use physical models and quantitative reasoning in biological and medical settings. To assess whether this goal is being met, we conducted a longitudinal study of the impact of IPLS on student work in later biology and chemistry courses. We report here on one part of that study, a comparison of written responses by students with different physics backgrounds on a diffusion task administered in a senior biology capstone course. We observed differences in student reasoning that were associated with prior or concurrent enrollment in IPLS. In particular, we found that IPLS students were more likely than non-IPLS students to reason quantitatively and mechanistically about diffusive phenomena, and to successfully coordinate between multiple representations of diffusive processes, even up to two years after taking the IPLS course. Finally, we describe methodological challenges encountered in both this task and other tasks used in our longitudinal study.
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