A Pilot Study on Multimodal Data Fusion for Academic Identification

This study investigates the efficacy of early multimodal data fusion for identifying academic performance profiles and predicting academic risk in specialized postgraduate education under small-sample constraints (n=51 graduate students distributed across three independent cohorts). Student numerical grades and multi-source qualitative faculty evaluations (self-, peer-, and hetero-evaluation) were integrated at the feature level prior to modeling. To overcome the limitations of traditional small-sample validation, a multi-layered framework was deployed, combining non-linear Kernel Principal Component Analysis (KPCA), multi-method clustering consensus, bootstrap resampling (1000 iterations), stratified nested cross-validation, and an external cohort validation strategy. The analysis revealed a bimodal distribution of academic risk (low-risk: 71.4%, high-risk: 28.6%) with high consensus across unsupervised algorithms (Adjusted Rand Index values spanning 0.922–1.000). Supervised predictive models yielded an unbiased nested cross-validation accuracy of 0.980±0.040, while external cohort validation demonstrated generalizability with a mean cross-cohort accuracy of 0.970±0.052 and a mean AUC of 0.997±0.001 (95% CI: 0.996–0.998). SHAP feature importance analysis identified Machine Learning (28.68%), Artificial Intelligence (17.93%), and Big Data (16.66%) as the primary drivers of risk differentiation. This study contributes a methodologically rigorous, moderately stable, and reproducible workflow for educational data mining in sample-constrained environments, providing actionable insights for evidence-based curriculum design and early pedagogical interventions. However, given the limited sample size, results should be interpreted as exploratory evidence rather than definitive population-level conclusions.