Predicting At-Risk Students for an Introductory Programming Course: A pilot study
Keywords:Learning analytics, computer programming, at-risk students
Some novice learners of computer programming are at risk of doing badly in their first programming course. In this pilot study, we develop a logistic regression model to predict at- risk students in our introductory programming course. The model is developed using students’ high school grades on mathematics, features calculated from log data, and scores from a programming quiz. The model suggests that students who have lower mathematics grade, who submit their homework assignments late, and who have lower scores in the programming quiz are more likely to be at-risk. We discuss some implications of this result on our teaching and learning strategies in our course.
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Copyright (c) 2021 Norman Tiong Seng Lee, Oka Kurniawan
This work is licensed under a Creative Commons Attribution 4.0 International License.