2 research outputs found
Enhancing Item Response Theory for Cognitive Diagnosis
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.Comment: Accepted by CIKM'2019. https://github.com/chsong513/DIR
Domain Adaption for Knowledge Tracing
With the rapid development of online education system, knowledge tracing
which aims at predicting students' knowledge state is becoming a critical and
fundamental task in personalized education. Traditionally, existing methods are
domain-specified. However, there are a larger number of domains (e.g.,
subjects, schools) in the real world and the lacking of data in some domains,
how to utilize the knowledge and information in other domains to help train a
knowledge tracing model for target domains is increasingly important. We refer
to this problem as domain adaptation for knowledge tracing (DAKT) which
contains two aspects: (1) how to achieve great knowledge tracing performance in
each domain. (2) how to transfer good performed knowledge tracing model between
domains. To this end, in this paper, we propose a novel adaptable framework,
namely adaptable knowledge tracing (AKT) to address the DAKT problem.
Specifically, for the first aspect, we incorporate the educational
characteristics (e.g., slip, guess, question texts) based on the deep knowledge
tracing (DKT) to obtain a good performed knowledge tracing model. For the
second aspect, we propose and adopt three domain adaptation processes. First,
we pre-train an auto-encoder to select useful source instances for target model
training. Second, we minimize the domain-specific knowledge state distribution
discrepancy under maximum mean discrepancy (MMD) measurement to achieve domain
adaptation. Third, we adopt fine-tuning to deal with the problem that the
output dimension of source and target domain are different to make the model
suitable for target domains. Extensive experimental results on two private
datasets and seven public datasets clearly prove the effectiveness of AKT for
great knowledge tracing performance and its superior transferable ability