Towards Explainable Multidimensional Digital Divide Classification in Online Learning Using Artificial Neural Networks and SHAP

Imalatul Hidayah; Ririen Kusumawati; Mochamad  Imamudin

doi:10.59395/ijadis.v7i2.1666

Authors

Imalatul Hidayah Departmen of Informatics Engineering, Maulana Malik Ibrahim State Islamic University of Malang, Indonesia
Ririen Kusumawati Departmen of Informatics Engineering, Maulana Malik Ibrahim State Islamic University of Malang, Indonesia
Mochamad Imamudin Departmen of Informatics Engineering, Maulana Malik Ibrahim State Islamic University of Malang, Indonesia

DOI:

https://doi.org/10.59395/ijadis.v7i2.1666

Keywords:

Artificial Neural Network, Digital Divide, Explainable AI , Online Learning, SHAP

Abstract

The transformation of online learning increases access to education, but also increases the risk of digital divide influenced by various technological and educational factors. This study aims to develop a framework Towards Explainable Multidimensional Digital Divide Classification in Online Learning Using Artificial Neural Networks and SHAP to classify cluster-derived digital divide categories transparently. Data were obtained from 324 students through a questionnaire covering Primary Device, Internet Stability, Equity Score, and Accessibility Score. The target Digital Divide categories were analytically derived using K-Means clustering based on respondents' multidimensional characteristics and subsequently used as labels for supervised classification. The data were processed through one-hot encoding, Min-Max normalization, and splitting the training and test data before training the model. The performance of ANN was compared with Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine (SVM). The results showed that ANN achieved 90.77% accuracy, with competitive performance against the comparison models. Furthermore, SHapley Additive exPlanations (SHAP) was used to interpret the model's decisions at both global and local levels. The analysis shows that Primary Device is the most influential factor, followed by Accessibility Score, Equity Score, and Internet Stability. The main contribution of this research is the presentation of an Explainable AI framework that combines the predictive capabilities of ANN with the interpretability of SHAP to support a more transparent multidimensional digital divide classification and can serve as a basis for developing data-driven digital inclusion policies.

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