  Cognitive diagnosis is a fundamental and crucial task in many educational
applications, e.g., computer adaptive test and cognitive assignments. Item
Response Theory (IRT) is a classical cognitive diagnosis method which can
provide interpretable parameters (i.e., student latent trait, question
discrimination, and difficulty) for analyzing student performance. However,
traditional IRT ignores the rich information in question texts, cannot diagnose
knowledge concept proficiency, and it is inaccurate to diagnose the parameters
for the questions which only appear several times. To this end, in this paper,
we propose a general Deep Item Response Theory (DIRT) framework to enhance
traditional IRT for cognitive diagnosis by exploiting semantic representation
from question texts with deep learning. In DIRT, we first use a proficiency
vector to represent students' proficiency in knowledge concepts and embed
question texts and knowledge concepts to dense vectors by Word2Vec. Then, we
design a deep diagnosis module to diagnose parameters in traditional IRT by
deep learning techniques. Finally, with the diagnosed parameters, we input them
into the logistic-like formula of IRT to predict student performance. Extensive
experimental results on real-world data clearly demonstrate the effectiveness
and interpretation power of DIRT framework.
