@article{4825,
  author = {Martin Lopez Nores},
  title = {Predictive Modelling of Overall Performance in IoT-Enabled Multicultural English Learning Environments: An Ensemble Learning and Interpretability Analysis},
  journal = {Digital Signal Processing and Artificial Intelligence for Automatic Learning},
  year = {2026},
  volume = {5},
  number = {3},
  doi = {https://doi.org/10.6025/dspaial/2026/5/3/187-208},
  url = {https://www.dline.info/dspai/fulltext/v5n3/dspaiv5n3_3.pdf},
  abstract = {The convergence of Internet of Things (IoT) technologies, artificial intelligence, and educational data analytics
has generated considerable interest in personalised English language instruction within multicultural learning
environments. However, the extent to which IoT-derived behavioural, linguistic, and engagement features
can reliably predict overall learner performance remains insufficiently examined, particularly with respect
to model transparency and pedagogical interpretability. This study conducts a comprehensive predictive
analysis of Overall_Performance using the English Learning Interaction Dataset, comprising 15,000 learningsession
records and 54 features collected from IoT-enabled smart classrooms at Chinese universities. Three
ensemble tree-based regressors (Gradient Boosting, Random Forest, and Extra Trees) were trained to predict
continuous performance outcomes, while Random Forest and Logistic Regression classifiers were applied to
a dichotomised high-performance target (  75). Additionally, one-way analysis of variance (ANOVA) was
employed to compare mean performance across six teaching strategies. Model interpretability was assessed
using SHapley Additive exPlanations (SHAP), Partial Dependence Plots (PDP), and Individual Conditional
Expectation (ICE) curves. Results revealed negligible predictive utility across all models: ensemble regressors
yielded R² values at or below zero (best: Gradient Boosting, R² = - 0.0022; MAE  15.0; RMSE  17.3), and
binary classifiers produced ROC-AUC values indistinguishable from chance (  0.50). One-way ANOVA
detected no statistically significant difference in Overall_Performance among the six teaching strategies (F
= 0.237, p = 0.946). Despite weak overall predictivity, SHAP and feature-importance analyses consistently
identified Communication_Effectiveness, Pronunciation_Score, Assignment_Marks, Feedback_Score, and
Grammar_Score as the relatively strongest predictors, while PDP/ICE plots revealed flat average marginal
effects accompanied by substantial between learner heterogeneity. These findings indicate that the current
tabular feature set is insufficient to predict overall performance with practical accuracy or to differentiate among pedagogical approaches. The study delivers a “Tabular Gradient Boosting” analysis and highlights
the need for longitudinal modelling frameworks and alternative outcome metrics to realise the full potential
of AI-driven personalised English education.},
}