  During the past few decades, cognitive diagnostics modeling has attracted
increasing attention in computational education communities, which is capable
of quantifying the learning status and knowledge mastery levels of students.
Indeed, the recent advances in neural networks have greatly enhanced the
performance of traditional cognitive diagnosis models through learning the deep
representations of students and exercises. Nevertheless, existing approaches
often suffer from the issue of overconfidence in predicting students' mastery
levels, which is primarily caused by the unavoidable noise and sparsity in
realistic student-exercise interaction data, severely hindering the educational
application of diagnostic feedback. To address this, in this paper, we propose
a novel Reliable Cognitive Diagnosis(ReliCD) framework, which can quantify the
confidence of the diagnosis feedback and is flexible for different cognitive
diagnostic functions. Specifically, we first propose a Bayesian method to
explicitly estimate the state uncertainty of different knowledge concepts for
students, which enables the confidence quantification of diagnostic feedback.
In particular, to account for potential differences, we suggest modeling
individual prior distributions for the latent variables of different ability
concepts using a pre-trained model. Additionally, we introduce a logical
hypothesis for ranking confidence levels. Along this line, we design a novel
calibration loss to optimize the confidence parameters by modeling the process
of student performance prediction. Finally, extensive experiments on four
real-world datasets clearly demonstrate the effectiveness of our ReliCD
framework.
