What factors enhance students’ achievement? A machine learning and interpretable methods approach
Year: 2025
Type: article
Study design: Observational Study
Abstract: Prior research on student achievement has typically examined isolated factors or bivariate correlations, failing to capture the complex interplay between learning behaviors, pedagogical environments, and instructional design. This study addresses these limitations by employing an ensemble of five machine learning algorithms (SVM, DT, ANN, RF, and XGBoost) to model multivariate relationships between four behavioral and six instructional predictors, using final exam performance as our outcome variable. Through interpretable AI techniques, we identify several key patterns: (1) Machine learning with explainability methods effectively reveals nuanced factor-achievement relationships; (2) Behavioral metrics (hw_score, ans_score, discus_score, attend_score) show consistent positive associations; (3) High-achievers demonstrate both superior collaborative skills and preference for technology-enhanced environments; (4) Gamification frequency (s&v_num) significantly boosts outcomes; while (5) Assignment frequency (hw_num) exhibits counterproductive effects. The results advocate for: (a) teachers should balance direct instruction with active learning modalities to optimize achievement, and (b) early warning systems should leverage identifiable learning features to proactively support struggling students. Our framework enables educators to transform predictive analytics into actionable pedagogical improvements.
Source: PLoS ONE
Institutions: China Three Gorges University, Wuhan Business University
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
Estimated APC paid: $2,382
OpenAlex ID: W4410434005