  Intelligent learning diagnosis is a critical engine of intelligent tutoring
systems, which aims to estimate learners' current knowledge mastery status and
predict their future learning performance. The significant challenge with
traditional learning diagnosis methods is the inability to balance diagnostic
accuracy and interpretability. Although the existing psychometric-based
learning diagnosis methods provide some domain interpretation through cognitive
parameters, they have insufficient modeling capability with a shallow structure
for large-scale learning data. While the deep learning-based learning diagnosis
methods have improved the accuracy of learning performance prediction, their
inherent black-box properties lead to a lack of interpretability, making their
results untrustworthy for educational applications. To settle the above
problem, the proposed unified interpretable intelligent learning diagnosis
(UIILD) framework, which benefits from the powerful representation learning
ability of deep learning and the interpretability of psychometrics, achieves a
better performance of learning prediction and provides interpretability from
three aspects: cognitive parameters, learner-resource response network, and
weights of self-attention mechanism. Within the proposed framework, this paper
presents a two-channel learning diagnosis mechanism LDM-ID as well as a
three-channel learning diagnosis mechanism LDM-HMI. Experiments on two
real-world datasets and a simulation dataset show that our method has higher
accuracy in predicting learners' performances compared with the
state-of-the-art models, and can provide valuable educational interpretability
for applications such as precise learning resource recommendation and
personalized learning tutoring in intelligent tutoring systems.
