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All Journal G-Tech : Jurnal Teknologi Terapan
Imalatul Hidayah
Universitas Islam Negeri Maulana Malik Ibrahim Malang, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering

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Educational Data Mining in Online Learning: Data Mining Techniques and Algorithms, Factors, Equity and Accessibility Dimensions (A Systematic Literature Review) Imalatul Hidayah; Ririen Kusumawati
G-Tech: Jurnal Teknologi Terapan Vol 10 No 1 (2026): G-Tech, Vol. 10 No. 1 January 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i1.8628

Abstract

This Systematic Literature Review (SLR) examines the application of Educational Data Mining (EDM) in online learning from 2015 to 2025 using the PRISMA approach. Thirty-two studies were analyzed to identify the data mining techniques used, the factors analyzed, and the extent to which the literature considers the equity and accessibility dimensions. The review results indicate that EDM is widely applied to predict academic performance, identify learning behavior patterns, detect at-risk students, and analyze the use of learning resources. The dominant techniques include classification, prediction, sequence analysis, process mining, and clustering. However, the equity and accessibility aspects are rarely discussed explicitly most studies only implicitly address accessibility through digital interaction behavior, while social factors related to equity, such as learning readiness, environmental support, and the digital divide, appear in only a small proportion. Furthermore, the variety of data formats and limited course coverage limit the generalizability of the findings. Overall, this study emphasizes the need for stronger integration between educational analytics and the social dimension for EDM to more effectively support equitable distribution of quality and access to online learning.
Integrating Boolean Logic-Based Feature Representation and Artificial Neural Networks for Digital Divide Level Classification in Online Learning Imalatul Hidayah; Ririen Kusumawati; Mochamad Imamudin; Zainal Abidin
G-Tech: Jurnal Teknologi Terapan Vol 10 No 3 (2026): G-Tech, Vol. 10 No. 3 July 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i3.10060

Abstract

Digital divide remains a major challenge in online learning, affecting students’ ability to access and benefit from digital education. While previous studies have primarily focused on first-level digital divide factors or employed machine learning models using raw input variables, limited research has explored the integration of logical feature engineering and artificial intelligence to model the multidimensional nature of digital divide. This study proposes a hybrid classification framework that combines Boolean Logic-based feature engineering and Artificial Neural Networks (ANN) for digital divide classification in online learning. The proposed framework utilizes four variables, namely Primary Device, Internet Stability, Equity Score, and Accessibility Score. Boolean Logic operations (AND, OR, and XOR) were applied to generate additional logical representations of digital access conditions before the ANN classification process. Data were collected from 324 student respondents participating in online learning. The model was evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results demonstrate that the optimized ANN model achieved 93% accuracy, 93% weighted precision, 93% weighted recall, and 93% weighted F1-score. Comparative analysis revealed that Boolean Logic features contributed additional logical representations of digital access patterns, although their impact on predictive performance was not consistently significant. The findings highlight that digital divide should be viewed as a multidimensional phenomenon and demonstrate the potential of ANN based predictive modeling to support data-driven digital inclusion strategies in education.
Co-Authors Mochamad Imamudin Ririen Kusumawati Zainal Abidin