Students often struggle in the transition from high school to college. One component of this struggle is adjusting study habits to navigate new academic expectations. Another is establishing new relationships in college that can support their emotional well-being and academic success. We administered surveys consisting primarily of open-ended questions to students taking an introductory physics course in order to gain insight into students’ study habits and support structures and how they change from high school to college. We find that many students learn that they need to dedicate more time outside of class to studying in order to succeed in college. Many students trying to improve their performance report practicing more, but our results suggest that more practice alone is not sufficient; rather, students were able to increase their performance and satisfaction in the course by engaging more deeply with the material. Regarding support structures, we find that in high school, students find their teachers highly supportive and accessible, but they are less likely to approach their college professors for help. Meanwhile, many students find peers to be an important source of support in college as the amount of support they receive from their families diminishes with distance from home. Gaining a better understanding of students’ study habits, support structures, and how they conceptualize them can help us design course structures and messaging that can more effectively help students develop strong learning strategies and social networks.
This content will become publicly available on May 1, 2025
Machine learning models were constructed to predict student performance in an introductory mechanics class at a large land-grant university in the United States using data from 2061 students. Students were classified as either being at risk of failing the course (earning a D or F) or not at risk (earning an A, B, or C). The models focused on variables available in the first few weeks of the class which could potentially allow for early interventions to help at-risk students. Multiple types of variables were used in the model: in-class variables (average homework and clicker quiz scores), institutional variables [college grade point average (GPA)], and noncognitive variables (self-efficacy). The substantial imbalance between the pass and fail rates of the course, with only about 10% of students failing, required modification to the machine learning algorithms. Decision threshold tuning and upsampling were successful in improving performance for at-risk students. Logistic regression combined with a decision threshold tuned to maximize balanced accuracy yielded the strongest classifier, with a DF accuracy of 83% and an ABC accuracy of 81%. Measures of variable importance involving changes in balanced accuracy identified homework grades, clicker grades, college GPA, and the fraction of college classes successfully completed as the most important variables in predicting success in introductory physics. Noncognitive variables added little predictive power to the models. Classification models with performance near the best-performing models using the full set of variables could be constructed with very few variables (homework average, clicker scores, and college GPA) using straightforward to implement algorithms, suggesting the application of these technologies may be fairly easy to include in many physics classes.
- PAR ID:
- 10511060
- Publisher / Repository:
- American Physical Society
- Date Published:
- Journal Name:
- Physical Review Physics Education Research
- Volume:
- 20
- Issue:
- 1
- ISSN:
- 2469-9896
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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