Predicting At-Risk Students for an Introductory Programming Course: A pilot study

Authors

  • Norman Tiong Seng Lee
  • Oka Kurniawan

Keywords:

Learning analytics, computer programming, at-risk students

Abstract

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.

Downloads

Published

2022-08-04

How to Cite

Lee, N. T. S., & Kurniawan, O. (2022). Predicting At-Risk Students for an Introductory Programming Course: A pilot study. ASCILITE Publications, 178–185. Retrieved from https://publications.ascilite.org/index.php/APUB/article/view/261