e-ISSN: 2618-6586 Open Access · Peer-Reviewed JETOL on DergiPark
JETOL — Journal of Educational Technology & Online Learning
Publisher
Gürhan Durak
Publication Model
Periodical (January – May – September)
Status
Open for Submissions

Modelling a dataset-defined skill-retention outcome in generative AI-assisted learning: An exploratory machine-learning and explainability study

Research Article

Download PDF DOI: 10.31681/jetol.1991829
Published in
Volume 9, Issue 3 (2026)
Pages
568–581
Publication Date
September 30, 2026
Submission Date
August 5, 2026
Acceptance Date
September 22, 2026
Subjects
Educational Technology and Computing, Learning Sciences

Abstract

Generative artificial intelligence (GenAI) is increasingly used in higher education, but durable learning cannot be inferred from task completion alone. This study presents an exploratory machine-learning benchmark using a public dataset of 50,000 student-like records that is treated here as synthetic/engineered because its source does not document an empirical sampling frame, institution, country, recruitment process, response rate, or primary-study ethics procedures. The target field, Skill Retention Score, is defined in the source schema on a 0-100 scale as representing skills retained and applied after the semester; however, the source provides no assessment instrument, delayed-assessment interval, reliability estimate, or validity evidence. Accordingly, the variable is analysed as a dataset-defined proxy rather than a validated measure of long-term retention. Ridge Regression, Decision Tree Regression, and Extra Trees Regression were compared in the originally reported 80:20 hold-out analysis. On that split, Extra Trees produced R² = .185, RMSE = 11.983, and MAE = 9.696. Its RMSE is approximately 9.8% lower than the full-sample outcome standard deviation of 13.282, indicating modest predictive gain. The explainability output is permutation importance aggregated to the parent-variable level; it is interpreted as a model-internal, non-directional diagnostic, and SHAP results were not available in the submitted analytical record. The results therefore describe the structure of this benchmark dataset and the behaviour of the modelling pipeline; they do not establish effects of GenAI on real students, directional relationships, or instructional recommendations.

Keywords

  • Generative AI
  • skill-retention proxy
  • synthetic data
  • machine learning
  • learning analytics
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