Modeling student pathways in a physics bachelor's degree program

Physics education research has used quantitative modeling techniques to explore learning, affect, and other aspects of physics education. However, these studies have rarely examined the predictive output of the models, instead focusing on the inferences or causal relationships observed in various data sets. This research introduces a modern predictive modeling approach to the PER community using transcript data for students declaring physics majors at Michigan State University (MSU). Using a machine learning model, this analysis demonstrates that students who switch from a physics degree program to an engineering degree program do not take the third semester course in thermodynamics and modern physics, and may take engineering courses while registered as a physics major. Performance in introductory physics and calculus courses, measured by grade as well as a students' declared gender and ethnicity play a much smaller role relative to the other features included the model. These results are used to compare traditional statistical analysis to a more modern modeling approach.Comment: submitted to Physical Review Physics Education Researc

Examining the relationship between student performance and video interactions

In this work, we attempted to predict student performance on a suite of laboratory assessments using students' interactions with associated instructional videos. The students' performance is measured by a graded presentation for each of four laboratory presentations in an introductory mechanics course. Each lab assessment was associated with between one and three videos of instructional content. Using video clickstream data, we define summary features (number of pauses, seeks) and contextual information (fraction of time played, in-semester order). These features serve as inputs to a logistic regression (LR) model that aims to predict student performance on the laboratory assessments. Our findings show that LR models are unable to predict student performance. Adding contextual information did not change the model performance. We compare our findings to findings from other studies and explore caveats to the null-result such as representation of the features, the possibility of underfitting, and the complexity of the assessment.Comment: 4 pages, 1 figure, submitted to the PERC 2018 proceeding

Identifying features predictive of faculty integrating computation into physics courses

Computation is a central aspect of 21st century physics practice; it is used to model complicated systems, to simulate impossible experiments, and to analyze mountains of data. Physics departments and their faculty are increasingly recognizing the importance of teaching computation to their students. We recently completed a national survey of faculty in physics departments to understand the state of computational instruction and the factors that underlie that instruction. The data collected from the faculty responding to the survey included a variety of scales, binary questions, and numerical responses. We then used Random Forest, a supervised learning technique, to explore the factors that are most predictive of whether a faculty member decides to include computation in their physics courses. We find that experience using computation with students in their research, or lack thereof and various personal beliefs to be most predictive of a faculty member having experience teaching computation. Interestingly, we find demographic and departmental factors to be less useful factors in our model. The results of this study inform future efforts to promote greater integration of computation into the physics curriculum as well as comment on the current state of computational instruction across the United States

Struggling to a monumental triumph : Re-assessing the final stages of the smallpox eradication program in India, 1960-1980

The global smallpox program is generally presented as the brainchild of a handful of actors from the WHO headquarters in Geneva and at the agency's regional offices. This article attempts to present a more complex description of the drive to eradicate smallpox. Based on the example of India, a major focus of the campaign, it is argued that historians and public health officials should recognize the varying roles played by a much wider range of participants. Highlighting the significance of both Indian and international field officials, the author shows how bureaucrats and politicians at different levels of administration and society managed to strengthen—yet sometimes weaken—important program components. Centrally dictated strategies developed at WHO offices in Geneva and New Delhi, often in association with Indian federal authorities, were reinterpreted by many actors and sometimes changed beyond recognition

Nonresonance impulsive higher order functional nonconvex-valued differential inclusions

In this paper, the authors investigate the existence of solutions for nonresonance impulsive higher order functional differential inclusions in Banach spaces with nonconvex valued right hand side. They present two results. In the first one, they rely on a fixed point theorem for contraction multivalued maps due to Covitz and Nadler, and for the second one, they use Schaefer's fixed point theorem combined with lower semi-continuous multivalued operators with decomposable values

General Lidstone Problems: Multiplicity and Symmetry of Solutions

AbstractFor the 2mth order Lidstone boundary value problem,y(2m)t=fyt,y″t,…,y(2i)t,…,y(2(m−1))t,t∈0,1, y(2i)0=y(2i)1=0,0≤i≤m−1, where (−1)mf: Rm→[0,$thinsp;∞) is continuous, growth conditions are imposed on f which yield the existence of at least three symmetric positive solutions. This generalizes earlier papers which have applied Avery's generalization of the Leggett–Williams theorem to Lidstone problems. We then prove the analogous result for difference equations