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

Pre-college Characteristics and Online Homework Learning: Factors Associated with First Year Engineering Students’ Academic Success

The purpose of the study was to develop a working model to predict at risk students in an Introduction to Engineering course. The model considers both students’ pre-college characteristics, psychological traits, and online homework learning behavior. The study assisted the course instructor in the creation of an early warning system and the development of targeted interventions for students at risk. A reliable and valid instrument to measure engineering students’ pre-college characteristics was initially developed. The study also applied data mining to analyze the student online homework logs in order to observe engineering students’ homework learning process. A decision tree model containing all of the pre-college characteristics and online homework learning features was also developed, and it identified four key factors related to students’ risk to fail the first module exam: Correctness, Preparedness, Self-efficacy, and percentage of homework attempts after deadline (Plate). The results of the decision tree model helped identify students-at-risk at early stage of the course. Students at risk were grouped into multiple groups. The author also proposed customized interventions to help students in different at risk groups. The findings of the study helped engineering students and educators to build up a comprehensive student profile to better understand students’ academic status and learning needs in the course. Thus this study suggests ways for both the engineering educators and students to improve the learning process in a more efficient manner

Using Machine Learning to Predict Physics Course Outcomes

The use of machine learning and data mining techniques across many disciplines has exploded in recent years with the field of educational data mining growing significantly in the past 15 years. In this study, random forest and logistic regression models were used to construct early warning models of student success in introductory calculus-based mechanics (Physics 1) and electricity and magnetism (Physics 2) courses at a large eastern land-grant university. By combining in-class variables such as homework grades with institutional variables such as cumulative GPA, we can predict if a student will receive less than a “B” in the course with 73% accuracy in Physics 1 and 81% accuracy in Physics 2 with only data available in the first week of class using logistic regression models. The institutional variables were critical for high accuracy in the first four weeks of the semester. In-class variables became more important only after the first in-semester examination was administered. The student’s cumulative college GPA was consistently the most important institutional variable. Homework grade became the most important in-class variable after the first week and consistently increased in importance as the semester progressed; homework grade became more important than cumulative GPA after the first in-semester examination. Demographic variables including gender, race or ethnicity, and first generation status were not important variables for predicting course grade