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This content will become publicly available on July 11, 2024

Title: Analysis of an Explainable Student Performance Prediction Model in an Introductory Programming Course
Prediction of student performance in Introductory programming courses can assist struggling students and improve their persistence. On the other hand, it is important for the prediction to be transparent for the instructor and students to effectively utilize the results of this prediction. Explainable Machine Learning models can effectively help students and instructors gain insights into students’ different programming behaviors and problem-solving strategies that can lead to good or poor performance. This study develops an explainable model that predicts students’ performance based on programming assignment submission information. We extract different data-driven features from students’ programming submissions and employ a stacked ensemble model to predict students’ final exam grades. We use SHAP, a game-theory-based framework, to explain the model’s predictions to help the stakeholders understand the impact of different programming behaviors on students’ success. Moreover, we analyze the impact of important features and utilize a combination of descriptive statistics and mixture models to identify different profiles of students based on their problem-solving patterns to bolster explainability. The experimental results suggest that our model significantly outperforms other Machine Learning models, including KNN, SVM, XGBoost, Bagging, Boosting, and Linear regression. Our explainable and transparent model can help explain students’ common problem-solving patterns in relationship with their level of expertise resulting in effective intervention and adaptive support to students.  more » « less
Award ID(s):
2213789
NSF-PAR ID:
10437969
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
the 16th International Conference on Educational Data Mining (EDM 2023)
Page Range / eLocation ID:
79-90
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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