| This paper presents a unified benchmark of fairness-aware SSL for learning analytics on MBA and OULAD. We compare three base classifiers (Decision Tree, Random Forest, XGBoost), ten SSL wrappers, and a fairness-only baseline across four label rates. Fairness for gender is evaluated with demographic parity difference (DP), equalized odds difference (EO), and equal opportunity difference (EOp). Across 792 runs, the strongest utility-oriented selections are usually Self-Training, SETRED, and CoTrainingByCommittee, whereas the fairest outcomes are often obtained by DeTriTraining at a substantial accuracy cost. Overall, the results show a persistent but context-dependent utility--fairness trade-off across datasets and supervision regimes, implying that early-warning deployment requires explicit utility and disparity guardrails rather than a single globally best method. |
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