Educational Data Mining (EDM) has achieved substantial gains in predictive performance, yet many existing approaches remain centered on single-objective optimization, most often accuracy. This does not adequately reflect the multi-dimensional nature of real-world educational decision-making, which requires balancing interpretability, fairness, robustness, efficiency, and timeliness. This perspective advocates a shift toward multi-objective, interpretable, and trustworthy EDM frameworks. We highlight the role of multi-objective optimization in modeling trade-offs through Pareto-optimal solutions and address the challenge of actionable decision-making through bargaining-based mechanisms, such... More >