An interpretable polytomous cognitive diagnosis framework for predicting examinee performance

Abstract

As a fundamental task of intelligent education, deep learning-based cognitive diagnostic models (CDMs) have been introduced to effectively model dichotomous testing data. However, it remains a challenge to model the polytomous data within the deep-learning framework. This paper proposed a novel Polytomous Cognitive Diagnosis Framework (PCDF), which employs Cumulative Category Response Function (CCRF) theory to partition and consolidate data, thereby enabling existing cognitive diagnostic models to seamlessly analyze graded response data. By combining the proposed PCDF with IRT, MIRT, NCDM, KaNCD, and ICDM, extensive experiments were complemented by data re-encoding techniques on the four real-world graded scoring datasets, along with baseline methods such as linear-split, one-vs-all, and random. The results suggest that when combined with existing CDMs, PCDF outperforms the baseline models in terms of prediction. Additionally, we showcase the interpretability of examinee ability and item parameters through the utilization of PCDF.