izvorni znanstveni rad
Student success prediction is a central topic in educational data
mining and learning analytics, as institutions increasingly rely on data-driven
approaches to enhance learning outcomes. However, the dynamic nature of
educational environments raises questions about the long-term reliability of
predictive features used in these models. This study aims to investigate the
temporal stability of features extracted by sensitivity analysis of predictive
models developed by integrating data from various sources, including the
e-learning system, student attendance records, teacher opinions, and
meteorological data. In this study, the stability of success predictors is modeled
using machine learning algorithms – Random Forest and Gradient Boosted
Decision Tree. By applying regression metrics, the precision of the model is
assessed to determine the reliability of predictive features over time.
Identification of the relevant success predictors and their temporal stability
provides insights into significant success predictors in the long term. The
results support the development of robust predictive models and highlight key
features that contribute to the reliable analysis of student success outcomes.
Predictive Data Modeling; Machine Learning; Stability of Success Predictors; Learning Management System; Learning Analytics; Random Forest; Gradient Boosted Tree
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