ORCID
- Keith Quille: 0000-0002-1414-5142
Abstract
First-year student dropout rates represent a global challenge, particularly in Computer Science programs. In response, the Predict Student Success (PreSS) tool was developed over two decades using Naïve Bayes, a black box machine learning model, to identify students at risk of early withdrawal or academic failure. Given growing concerns about the use of black box models in high-stakes domains such as education, this study builds on former research by comparing the Naïve Bayes model to three glass box AI models: Decision Tree, Explainable Boosting Machine, and Automatic Piecewise Linear Regression. These models were used to generate four types of explainable AI visualisations: Feature Importance, Similar and Contrastive Examples, Decision Rules, and Counterfactuals. Educators evaluated these visualisations through a qualitative survey. Results indicate that glass box models can perform competitively with Naïve Bayes, and that contrastive explanation types were most preferred among educators. Open-ended responses underscore the importance of clear user guidance when implementing such tools.
Keywords
Higher Education Dropout, Human-Centered Explainable AI, Student Success Prediction, User Perception
DOI Link
Publication Date
2026-02-16
Event
3rd International Conference on Human-Centred AI - Education and Practice, HCAI-ep 2026
Publication Title
HCAI-ep 2026 - Proceedings of the 2026 Conference on Human Centered Artificial Intelligence - Education and Practice
Publisher
Association for Computing Machinery (ACM)
ISBN
9798400721533
First Page
61
Last Page
67
Deposit Date
2026-05-13
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Additional Links
Recommended Citation
Maathuis, Henry; Glazenborg, Jan; Grol, Meike; and Quille, Keith, "Educators' Evaluation of Explanation Types in XAI for Higher Education Dropout" (2026). Research Outputs: 2025-Present. 11.
https://arrow.tudublin.ie/faccomentro/11