Multiclass classification in educational data mining presents persistent challenges including class imbalance, miscalibrated probability outputs, and insufficient statistical validation. We proposed RXK-VEM, a hybrid ensemble framework that integrates random forest (RF), extreme gradient boosting (XGBoost), and K-nearest neighbors (KNN) through a formally defined vote-entropy-weighted meta-fusion (VEM) operator, followed by meta-level calibration using multinomial logistic regression. The VEM operator is defined as a mapping on the probability simplex ${\mathrm{\Delta }}^{C-1}$, aggregating heterogeneous base learner outputs into a unified probabilistic representation with provable closure properties. We further established a Rademacher complexity-based generalization bound showing that operating in the compressed $C$-dimensional probability space ( $C\ll d$) tightens the generalization gap relative to classifiers trained directly on raw features, providing theoretical justification for the stacking architecture. We validated RXK-VEM on two structurally distinct educational datasets: A primary academic performance dataset ( $N=560$, five classes) from Universiti Teknologi Malaysia and a secondary student dropout dataset ( $N=4,424$, three classes) from the University of California, Irvine (UCI) repository. On the primary dataset, RXK-VEM achieves 91.07% accuracy, 91.22% precision, and an 86.21% Matthews correlation coefficient (MCC), outperforming all individual base learners and conventional ensemble strategies. On the secondary dataset, the model achieves 77.30% accuracy and a 62.33% MCC, maintaining competitive performance across all metrics. Statistical validation through five-fold stratified cross-validation, paired $t$-tests, and Wilcoxon signed-rank tests confirms that improvements over weaker baselines are consistent and not attributable to random variation. A systematic ablation study quantifies the complementary contribution of each base learner, and Shapley additive explanations analysis validates the interpretability of the identified predictors. The proposed framework offers a mathematically rigorous, empirically validated, and interpretable architecture for probabilistic ensemble integration in multiclass educational prediction tasks.

Air-writing is a widely used technique for writing arbitrary characters or numbers in the air. In this study, a data collection technique was developed to collect hand motion data for Bengali air-writing, and a motion sensor-based data set was prepared. The feature set as then utilized to determine the most effective machine learning (ML) model among the existing well-known supervised machine learning models to classify Bengali characters from air-written data. Our results showed that medium Gaussian SVM had the highest accuracy (96.5%) in the classification of Bengali character from air writing data. In addition, the proposed system achieved over 81% accuracy in real-time classification. The comparison with other studies showed that the existing supervised ML models predicted the created data set more accurately than many other models that have been suggested for other languages.