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Mining autonomous student patterns score on LMS within online higher education.

Ricardo Ordoñez-Avila^1,2, Jaime Meza¹, Sebastian Ventura²

¹Departamento de Sistemas Computacionales, Universidad Técnica de Manabí, Portoviejo, Manabí, Ecuador.

Peerj. Computer Science

|June 26, 2025

Summary

Data mining predicts student autonomous scores in online Law and Psychology programs using learning management system (LMS) data. Gradient boosting and XGBoost models showed strong predictive performance, offering insights for personalized online learning improvement.

Keywords:

Autonomous score Educational data mining LMS Learning analytics Patterns Virtual education

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Area of Science:

Educational Technology
Data Science
Higher Education

Background:

Learning Management Systems (LMS) are integral to online education in higher education.
Predicting student autonomous scores is crucial for understanding and enhancing online learning experiences.

Purpose of the Study:

To apply data mining techniques to predict student autonomous scores in online Law and Psychology programs.
To identify key student engagement variables influencing autonomous learning within an LMS environment.

Main Methods:

Utilized a dataset of over 16,000 student records from online Law and Psychology programs.
Employed data preprocessing, RobustScaler transformation, and Recursive Feature Elimination with Cross-Validation (RFEcv) for feature selection.
Implemented and evaluated predictive models including Gradient Boosting and Extreme Gradient Boosting (XGBoost), optimizing hyperparameters.

Main Results:

Recursive Feature Elimination with Cross-Validation (RFEcv) significantly improved model performance.
The Gradient Boosting model achieved the highest R-squared (0.6693) for the Law program and strong results for Psychology (R-squared = 0.6418).
The XGBoost model demonstrated best performance when combining both datasets (R-squared = 0.6294).

Conclusions:

Data mining, particularly with feature selection via RFEcv and hyperparameter tuning, effectively predicts student autonomous scores in online learning.
The findings suggest that LMS data can provide real-time indicators for personalized online learning improvements.
Future integration of autonomous score data directly into LMS platforms is recommended for enhanced insights.