Neuroplasticity-informed hierarchical and uncertainty-aware modeling of secondary-school english learning

Introduction This study develops a neuroplasticity-informed computational framework for modeling heterogeneous English-learning outcomes in secondary-school educational settings. Rather than treating neuroplasticity as a directly observable neural quantity, the study uses it as a theoretical perspective for conceptualizing learning-related behavior as adaptive, experience-sensitive, and heterogeneous across learners and contexts. Methods We propose HierarchicalFactorizedUncertainty, a unified predictive framework that integrates multi-level hierarchical representation learning, sparse low-rank learning–context interaction modeling, and aleatoric–epistemic uncertainty modeling. The Hierarchical Composition Mapper learns structured representations from learner–language evidence, while the Factorized Relation Projector models compact associations between latent representations and recorded task or contextual information. The Uncertainty Propagation Resolver represents observation-dependent predictive variability and model uncertainty using heteroscedastic prediction and stochastic model inference. Sparse low-rank regularization further suppresses weak or redundant interaction factors and improves representation compactness. The framework is evaluated on four complementary learner corpora—CSEE, ELLIPSE, SEEFLEX, and LOCSE—covering written and spoken English performance, heterogeneous task or contextual conditions, and both cross-sectional and longitudinal observations. Results and discussion Under controlled data partitions and repeated training runs, Hierarchical Factorized Uncertainty achieves the best mean performance across all reported prediction metrics relative to conventional statistical, recurrent, and pretrained Transformer baselines, although the statistical strength of the improvements varies by dataset and metric. Ablation analyses show that removing the Hierarchical Composition Mapper produces the largest mean deterioration among the architecture- and regularization-related ablations across all four datasets. The Factorized Relation Projector provides an additional consistent predictive contribution, with a comparatively pronounced effect on SEEFLEX, while the relative contributions of the implemented aleatoric and epistemic uncertainty mechanisms vary across datasets and are strongest when both are retained. These findings support structured, context-sensitive, and uncertainty-aware modeling for observable English-learning outcomes while maintaining a clear distinction between behavioral prediction and direct evidence of neuroplastic change. HierarchicalFactorizedUncertainty is therefore intended as a predictive framework with potential decision-support value for English assessment and teacher review rather than as a causal model of instructional effectiveness or neural restructuring.