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Learning Analytics Based on Big Data: Student Behavior Prediction and Personalized Educational Strategy Formulation
- 1 Hunan Institute of Technology, Hunan, China
* Author to whom correspondence should be addressed.
Abstract
Learning analytics have become a game-changer in education by using big data to analyse student behaviours, predict student outcomes and provide personalised interventions. This paper outlines the main components in learning analytics including data collection, predictive modelling and personalised educational strategies. It demonstrates how predictive models can be used to identify at-risk students and why real-time feedback can keep students engaged and motivated. Two case studies and examples of the data are used to illustrate how institutions can shift from reactive to proactive mode using learning analytics to track engagement, performance, and personalise the learning path. The study also shows that learning technologies are becoming adaptive to personalise learning experience, which results in a more learner-centric approach to education catering for the individual needs of students. Overall, the study demonstrates the role of learning analytics in creating a data-driven environment, which improves the student learning success and retention by addressing the challenges ahead of time.
Keywords
Learning analytics, big data, predictive modeling, student engagement, at-risk students.
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Cite this article
Luo,X. (2024). Learning Analytics Based on Big Data: Student Behavior Prediction and Personalized Educational Strategy Formulation. Applied and Computational Engineering,116,7-13.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 5th International Conference on Signal Processing and Machine Learning
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