POA-MLSP: a multi-dimensional learning analytics framework for predicting CET4 writing performance based on a production-oriented approach and student engagement patterns

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Analysis predicts writing performance in students using a new framework, indicating effective pedagogy with multi-source data.

POA-MLSP framework achieves R² = 0.75 for predicting student performance, enhancing writing instruction effectiveness.
Key comparisons reveal POA-MLSP outperforms linear regression (R² = 0.58), random forest (R² = 0.66), and support vector machines (R² = 0.63).
Assessment across 124 students over 16 weeks utilizes multi-source data, including engagement and performance metrics.
Integration of educational theory improves prediction accuracy, supporting timely pedagogical interventions.

Li et al. (2025) studied this question.

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