TY  - JOUR
AU  - Keivanian, Farshid
AU  - Fan, Zongwen
PY  - 2026
DA  - 2026/04/11
TI  - Beyond Accuracy: Toward Interpretable, Multi-Objective, and Trustworthy Educational Data Mining Systems
JO  - ICCK Transactions on Educational Data Mining
T2  - ICCK Transactions on Educational Data Mining
JF  - ICCK Transactions on Educational Data Mining
VL  - 2
IS  - 2
SP  - 52
EP  - 55
DO  - 10.62762/TEDM.2026.988161
UR  - https://www.icck.org/article/abs/TEDM.2026.988161
KW  - educational data mining
KW  - multi-objective optimization
KW  - interpretable AI
KW  - trustworthy AI
KW  - educational decision-making
AB  - 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 as Nash bargaining, to select balanced and transparent outcomes. In addition, we discuss the value of fuzzy logic and adaptive methods for handling uncertainty and supporting interpretable reasoning in dynamic learning environments. Finally, we emphasize the importance of governance, accountability, and rigorous evaluation, and argue that emerging technologies should be assessed not only by performance gains but also by their practical and educational relevance. Overall, this perspective outlines a human-centered research agenda for the development of trustworthy, interpretable, and context-aware EDM systems.
SN  - 3070-5843
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Keivanian2026Beyond,
  author = {Farshid Keivanian and Zongwen Fan},
  title = {Beyond Accuracy: Toward Interpretable, Multi-Objective, and Trustworthy Educational Data Mining Systems},
  journal = {ICCK Transactions on Educational Data Mining},
  year = {2026},
  volume = {2},
  number = {2},
  pages = {52-55},
  doi = {10.62762/TEDM.2026.988161},
  url = {https://www.icck.org/article/abs/TEDM.2026.988161},
  abstract = {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 as Nash bargaining, to select balanced and transparent outcomes. In addition, we discuss the value of fuzzy logic and adaptive methods for handling uncertainty and supporting interpretable reasoning in dynamic learning environments. Finally, we emphasize the importance of governance, accountability, and rigorous evaluation, and argue that emerging technologies should be assessed not only by performance gains but also by their practical and educational relevance. Overall, this perspective outlines a human-centered research agenda for the development of trustworthy, interpretable, and context-aware EDM systems.},
  keywords = {educational data mining, multi-objective optimization, interpretable AI, trustworthy AI, educational decision-making},
  issn = {3070-5843},
  publisher = {Institute of Central Computation and Knowledge}
}