Enhancing Student Performance Prediction In CS1 Via In-Class Coding

TL;DR

This study investigates how incorporating in-class coding exercises in CS1 courses improves early prediction of student performance, enabling timely interventions to reduce failure rates.

Contribution

It demonstrates that in-class coding exercises significantly enhance early performance prediction models in introductory computer science courses.

Findings

In-class exercises improve prediction accuracy of student performance.

Early predictions are feasible by weeks 3-5 of the semester.

The approach supports early intervention strategies.

Abstract

Computer science's increased recognition as a prominent field of study has attracted students with diverse academic backgrounds. This has significantly increased the already high failure rates in introductory courses. To address this challenge, it is essential to identify struggling students early on. Incorporating in-class coding exercises in these courses not only offers additional practice opportunities to students but may also reveal their abilities and help teachers identify those in need of assistance. In this work, we seek to determine the extent to which the practice of using in-class coding exercises enhances the ability to predict student performance, especially early in the semester. Based on data obtained in a CS1 course taught at a mid-size American university, we found that in-class exercises could improve the prediction of students' eventual performance. In particular, we found relatively accurately predictions as early as academic weeks 3 through 5, making it possible to devise early intervention strategies. This work can benefit future studies on the impact of in-class exercises as well as intervention strategies throughout the semester.