Identifying features predictive of faculty integrating computation into physics courses

Abstract

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.

Comparison of student and instructor reasons for using computation

“…These students described professionally and personally important reasons for using computation. Collectively, the students identified many reasons for using computation consistent with those expressed by the physics community [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]: accuracy, aiding learning, conceptual and mathematical accessibility, data collection and analysis, efficiency, experimental control, manipulation of variables, personal applications, sense-making, simulation, and visualization.…”

“…The use of computers to study, solve, and visualize physics problems is valuable in undergraduate physics education. Computation helps prepare students for careers in STEM research and industry [1][2][3][4][5] and enables students to pursue creative solutions [6,7], engage in sense-making [8][9][10], and test model-based predictions in analytically intractable problems [2,7,[11][12][13][14][15][16]. Although computation is widely adopted in university physics programs [17] and there is a consensus that computational experience is beneficial for students, research is needed to address challenges that arise when integrating computation, such as student motivation [18,19] and "making room" in the curriculum [2,14,19].…”

Using machine learning to understand physics graduate school admissions

“…We will refer to the features determined to be important via this process as meaningful features. For more information about random forest models, biases, and feature importance measures, see Young et al [21].…”

Physics computational literacy: An exploratory case study using computational essays

“…Despite this progress, computation is not yet widely integrated into physics teaching. Apart from a few wellestablished curricula like Matter & Interactions and certain mathematical tools (e.g., Mathematica and MATLAB), most university-level physics courses have relatively little engagement with computation [15][16][17]. There are numerous systemic factors related to this lack of adoption [18], but we contend that these factors alone should not deter the physics education research community from exploring ways in which computation can positively influence physics teaching, and vice versa.…”