Cloud-Enabled Analytics for Monitoring and Enhancing Student Academic Performance

DOI

10.2991/978-94-6239-789-7_14How to use a DOI?

Keywords

Cloud computing; student performance analytics; xAPI-Edu-Data dataset; machine learning; Random Forest; educational data mining; academic prediction; feature importance; smart education; learning analytics

Abstract

The proliferation of digital educational tools has brought about a need for reliable systems for tracking and predicting the academic performance of learners. This paper describes a cloud-based system for analyzing student performance which uses machine learning models with cloud computing to identify at-risk students and implement effective interventions based on the analysis. In order to avoid the inconsistencies observed in prior work, only the xAPI-Edu-Data was used in this experiment. This dataset has a total of 480 entries with 16 different features related to behavior, engagement, and academics. The preprocessing phase included missing value imputation, label encoding, feature scaling, and stratified 10-fold cross-validation to ensure reproducibility. Decision Tree, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, Random Forest, and Gradient Boosting classifiers were compared through several experiments. The Random Forest classifier was observed to have the highest accuracy of 92.1% with a precision, recall, and F1 score of 91.4%, 90.8%, and 91.1% respectively. The feature importances obtained indicated that absenteeism, raised hand, visited resource count, discussion, and parental satisfaction had the highest impact on academic success. Cloud implementation resulted in a prediction speed improvement of 66.7% from 4.8 s to 1.6 s. The study demonstrates novelty through the integration of interpretable machine learning, reproducible evaluation, and scalable cloud-based analytics for real-time educational decision support.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Anshu Mehta
AU  - Ishta Rani
AU  - Yogita Yogita
AU  - Gitanjali Gupta
AU  - Jaspreet Singh
PY  - 2026
DA  - 2026/10/08
TI  - Cloud-Enabled Analytics for Monitoring and Enhancing Student Academic Performance
BT  - Proceedings of theInternational Conference on Innovation and Practices in Artificial Intelligence (ICIPAI 2026)
PB  - Atlantis Press
SP  - 184
EP  - 197
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-789-7_14
DO  - 10.2991/978-94-6239-789-7_14
ID  - Mehta2026
ER  -