Interpretable Dropout Prediction: Towards XAI-Based Personalized Intervention

Abstract

Student drop-out is one of the most burning issues in STEM higher education, which induces considerable social and economic costs. Using machine learning tools for the early identification of students at risk of dropping out has gained a lot of interest recently. However, there has been little discussion on dropout prediction using interpretable machine learning (IML) and explainable artificial intelligence (XAI) tools.In this work, using the data of a large public Hungarian university, we demonstrate how IML and XAI tools can support educational stakeholders in dropout prediction. We show that complex machine learning models – such as the CatBoost classifier – can efficiently identify at-risk students relying solely on pre-enrollment achievement measures, however, they lack interpretability. Applying IML tools, such as permutation importance (PI), partial dependence plot (PDP), LIME, and SHAP values, we demonstrate how the predictions can be explained both globally and locally. Explaining individual predictions opens up great opportunities for personalized intervention, for example by offering the right remedial courses or tutoring sessions. Finally, we present the results of a user study that evaluates whether higher education stakeholders find these tools interpretable and useful.

Could the Use of AI in Higher Education Hinder Students With Disabilities? A Scoping Review

“…[47]). Only four of 72 articles mentioned students with disabilities [40], [48]- [50]. Among these four, Renzella et al [48] and Nagy and Molontay [40] were the only ones to address or discuss accessibility considerations clearly.…”

“…Only four of 72 articles mentioned students with disabilities [40], [48]- [50]. Among these four, Renzella et al [48] and Nagy and Molontay [40] were the only ones to address or discuss accessibility considerations clearly. Renzella et al [48] highlighted the importance of educators reaching out and engaging with students before introducing tutoring systems that may not be compatible with speech-generation technologies or that may increase anxiety by requiring students to record themselves.…”

“…Renzella et al [48] highlighted the importance of educators reaching out and engaging with students before introducing tutoring systems that may not be compatible with speech-generation technologies or that may increase anxiety by requiring students to record themselves. Nagy & Molontay [40] explicitly chose a colourblind-friendly colour palette for their application. Xia [50] included disability as a variable to predict a final score and identify learning behaviour profiles that need attention.…”

“…Three of the presented applications had a direct effect on admission selection: one provided a short-list of candidates for a graduate program based on their CV [69], another modified the weight of admission criteria to select students with the characteristics that predicted a higher performance at university [70], and the third one predicted enrolment of students to inform scholarship allocations [45]. The other three articles sought to inform admission decisions and strategic planning by highlighting skill or knowledge gaps and strengths [40], [71], [72]. Importantly, Nagy and Molontay [40] pointed out the self-fulling prophecy issue when students are predicted to fail in a program.…”

A Systematic Review of Generative AI and (English Medium Instruction) Higher Education

“…In addition to scholarly works which examine the potential impact on student attainment, such as Ramo et al (2022), Saad and Tounkara (2023), and Urtasun (2022), other scholars have examined questions such as student motivation (Martín-Núñez et al, 2023), learner drop out (e.g., Bañeres et al, 2023;Nagy & Molontay, 2023), and student performance prediction (Jiao et al, 2022).…”