  The ever growing abundance of learning traces in the online learning
platforms promises unique insights into the learner knowledge assessment (LKA),
a fundamental personalized-tutoring technique for enabling various further
adaptive tutoring services in these platforms. Precise assessment of learner
knowledge requires the fine-grained Q-matrix, which is generally designed by
experts to map the items to skills in the domain. Due to the subjective
tendency, some misspecifications may degrade the performance of LKA. Some
efforts have been made to refine the small-scale Q-matrix, however, it is
difficult to extend the scalability and apply these methods to the large-scale
online learning context with numerous items and massive skills. Moreover, the
existing LKA models employ flexible deep learning models that excel at this
task, but the adequacy of LKA is still challenged by the representation
capability of the models on the quite sparse item-skill graph and the learners'
exercise data. To overcome these issues, in this paper we propose a
prerequisite-driven Q-matrix refinement framework for learner knowledge
assessment (PQRLKA) in online context. We infer the prerequisites from
learners' response data and use it to refine the expert-defined Q-matrix, which
enables the interpretability and the scalability to apply it to the large-scale
online learning context. Based on the refined Q-matrix, we propose a
Metapath2Vec enhanced convolutional representation method to obtain the
comprehensive representations of the items with rich information, and feed them
to the PQRLKA model to finally assess the learners' knowledge. Experiments
conducted on three real-world datasets demonstrate the capability of our model
to infer the prerequisites for Q-matrix refinement, and also its superiority
for the LKA task.
