Machine Learning in Educational Large Data Analysis

Dear Colleagues,

The growing availability of large-scale educational data has catalyzed a surge in the application of machine learning (ML) in educational research (Ersozlu et al., 2024). Institutions routinely collect vast amounts of information through enrollment records, learning management systems, national assessments and digital learning platforms such as MOOCs (Du et al., 2020). These data sources have created new opportunities to investigate patterns in student learning, behavior and outcomes at an unprecedented scale. Machine learning offers a set of tools well-suited to this data-rich environment. Over the past decade, it has been increasingly used to model complex relationships and generate predictive insights that complement or extend traditional statistical approaches (Delen, 2010; Sghir et al., 2023). Applications have ranged from forecasting academic performance and identifying at-risk students to classifying engagement patterns and automating assessment tasks. More recently, ML techniques have also been applied to qualitative data, such as open-ended survey responses, discussion threads, student writing and course evaluations, enabling researchers to detect themes and sentiment at scale (Kastrati et al., 2021; Nawaz et al., 2022).

This Special Issue calls for proposals that use ML techniques to address educational issues with or without comparison to statistical procedures. The ML techniques include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, large language models (LLMs), support vector machine (SVM), decision trees (DTs), artificial neural networks (ANNs), Naïve Bayes (NB), K-nearest neighbors (KNNs), Random Forests (RFs), Gradient boosting, XGBoost, SHAP, LightGBM and TensorFlow.

References

• Ersozlu, Z., Taheri, S., & Koch, I. (2024). A review of machine learning methods used for educational data. Education and Information Technologies, 29(16), 22125-22145. https://doi.org/10.1007/s10639-024-12704-0.
• Du, Chuanjun, Ruoying He, Zhiyu Liu, Tao Huang, Lifang Wang, Zhongwei Yuan, Yanping Xu, Zhe Wang, and Minhan Dai. (2020). Composited temperature-salinity-dissolved inorganic phosphate relationships in the South China Sea of fall season. https://doi.pangaea.de/10.1594/PANGAEA.921708.
• Delen, D. (2010). A comparative analysis of machine learning techniques for student retention management. Decision Support Systems, 49(4), 498-506. https://www.sciencedirect.com/science/article/abs/pii/S0167923610001041.
• Sghir, N., Adadi, A., & Lahmer, M. (2023). Recent advances in Predictive Learning Analytics: A decade systematic review (2012–2022). Education and information technologies, 28(7), 8299-8333. https://doi.org/10.1007/s10639-022-11536-0.
• Kastrati, Z., Dalipi, F., Imran, A. S., Pireva Nuci, K., & Wani, M. A. (2021). Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study. Applied Sciences, 11(9), 3986. https://doi.org/10.3390/app11093986.
• Nawaz, R., Sun, Q., Shardlow, M., Kontonatsios, G., Aljohani, N. R., Visvizi, A., & Hassan, S.-U. (2022). Leveraging AI and Machine Learning for National Student Survey: Actionable Insights from Textual Feedback to Enhance Quality of Teaching and Learning in UK’s Higher Education. Applied Sciences, 12(1), 514. https://doi.org/10.3390/app12010514.

Prof. Dr. Chuang Wang
Prof. Dr. Yuyang Cai
Guest Editors

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• machine learning
• educational research
• large data
• data analysis
• statistical procedure
• large language model