  Personalized learner modeling using cognitive diagnosis (CD), which aims to
model learners' cognitive states by diagnosing learner traits from behavioral
data, is a fundamental yet significant task in many web learning services.
Existing cognitive diagnosis models (CDMs) follow the proficiency-response
paradigm that views learner traits and question parameters as trainable
embeddings and learns them through learner performance prediction. However, we
notice that this paradigm leads to the inevitable non-identifiability and
explainability overfitting problem, which is harmful to the quantification of
learners' cognitive states and the quality of web learning services. To address
these problems, we propose an identifiable cognitive diagnosis framework
(ID-CDF) based on a novel response-proficiency-response paradigm inspired by
encoder-decoder models. Specifically, we first devise the diagnostic module of
ID-CDF, which leverages inductive learning to eliminate randomness in
optimization to guarantee identifiability and captures the monotonicity between
overall response data distribution and cognitive states to prevent
explainability overfitting. Next, we propose a flexible predictive module for
ID-CDF to ensure diagnosis preciseness. We further present an implementation of
ID-CDF, i.e., ID-CDM, to illustrate its usability. Extensive experiments on
four real-world datasets with different characteristics demonstrate that ID-CDF
can effectively address the problems without loss of diagnosis preciseness.
