SVM and random forest classifiers showed consistent performance in classifying ethnic bias, with F1-scores of 0.71 and 0.70 on the test set¶

Claim · All claims
Evidence · 1 study · design r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly

Subclaims¶

q2 i? On the independent test set, SVM achieved an F1-score of 0.71 and RF 0.70 for bias classification, with SVM and the stacked model both reaching the highest accuracy (0.75). → Josmario Albuquerque 2026

Evidence¶

Josmario Albuquerque 2026¶

Josmario Albuquerque, Bart Rienties, Martin Hlosta, Wayne Holmes. (2026). Learning Analytics to Uncover Ethnic Bias in Educational Texts: An Ensemble Learning Approach. Journal of Learning Analytics 13(1). https://doi.org/10.18608/jla.2026.8905

q2 · i? · design · r2

Evaluation of fine-tuned classifiers on a held-out 20% test set (69 samples; 15 biased, 54 not biased) after training with SMOTE oversampling and stratified K-fold cross-validation; the table reports "the highest accuracy (0.75)" for STK and SVM.

"the stacked model (STK) and the SVM model both achieved the highest accuracy (0.75), with similar performance in terms of F1-score (0.71 for SVM vs. 0.69 for STK). RF and XGB models also performed comparably, with F1-scores of 0.70 and 0.69, respectively."

Discussion¶

Related Claims¶

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