An Interpretable and Hierarchical Positive-Prior-Driven Student Evaluation Enhancement Decision-Making Strategy
TL;DR
A hierarchical student evaluation enhanced decision-making model which is based on a zero-order fuzzy classifier as the construction unit and achieves the collaborative optimization of evaluation efficiency and interpretability is proposed.
Abstract
A scientific and reasonable student evaluation system plays a crucial guiding role in the development of higher education. However, for the current student evaluation theories and models, their operability is relatively weak. The constructed models have poor adaptability (weak robustness), lack cross-scenario research on guiding policies, and the evaluation results of the models are also relatively single. Therefore, this study proposes a hierarchical student evaluation enhanced decision-making model which is based on a zero-order fuzzy classifier as the construction unit. By introducing the administrative decision-making characteristics of the educational administration department (such as policy orientation indicators, teaching intervention suggestions, etc.), combined with a hierarchical fuzzy system and an improved ridge regression algorithm, it achieves the collaborative optimization of evaluation efficiency and interpretability. The experimental results show that the model demonstrates excellent classification performance and semantic interpretability on the degree student evaluation dataset, and can accurately predict students’ academic performance and support personalized educational decisions.