International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Volume 8 Issue 3
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Development of a Social Computing-Based Intelligent Class Record System for Early Detection of At-Risk Students Using Learning Analytics and the OULAD Dataset

Author(s) Prof. John Ivan Curbano Maurat
Country Philippines
Abstract This study aimed to design, develop, and evaluate a social computing-based intelligent class record system for the early detection of at-risk students using learning analytics, social computing, and artificial intelligence-driven predictive modeling. Traditional class record systems primarily function as repositories of attendance and grades and often lack predictive and analytical capabilities necessary for timely academic intervention. Addressing this limitation, the proposed system integrates demographic, assessment, and engagement data derived from the Open University Learning Analytics Dataset (OULAD) to provide data-driven insights into student performance and engagement patterns.

The study employed a developmental research approach involving data preprocessing, feature engineering, machine learning model development, dashboard design, and usability evaluation. Several predictive algorithms, namely logistic regression, random forest, neural network, and extreme gradient boosting (XGBoost), were utilized and compared to classify students into pass, fail, withdraw, and distinction categories. In addition, Social Network Analysis (SNA) metrics such as degree centrality, clustering, and interaction frequency were incorporated to analyze peer engagement and collaborative behaviors.

The developed system features an interactive dashboard that visualizes student risk levels, engagement trends, assessment performance, and social interaction patterns. Furthermore, the system generates automated intervention recommendations based on predictive outputs and behavioral indicators to support proactive instructional decision-making. The usability and perceived effectiveness of the system were evaluated using the System Usability Scale (SUS) among selected educators.

Findings of the study revealed that ensemble and machine learning approaches demonstrated strong predictive capabilities in identifying at-risk students, while the integration of social computing enhanced the contextual understanding of learner engagement and persistence. The proposed intelligent class record system provides educators with a holistic and interpretable platform for monitoring academic performance and initiating timely interventions. The study contributes to the growing field of learning analytics by integrating predictive analytics, social computing, explainable artificial intelligence, and dashboard visualization into a unified educator-centered system that supports evidence-based and proactive educational practices.
Keywords At-Risk Students, Dashboard, Early Warning System, Educational Data Mining, Educational Technology, Learning Analytics template
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 8, Issue 3, May-June 2026
Published On 2026-05-30
DOI https://doi.org/10.36948/ijfmr.2026.v08i03.79818
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E-ISSN 2582-2160
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