Causal-Driven Skill Prerequisite Structure Discovery

Knowing a prerequisite structure among skills in a subject domain effectively enables several educational applications, including intelligent tutoring systems and curriculum planning. Traditionally, educators or domain experts use intuition to determine the skills' prerequisite relationships, which is time-consuming and prone to fall into the trap of blind spots. In this paper, we focus on inferring the prerequisite structure given access to students' performance on exercises in a subject. Nevertheless, it is challenging since students' mastery of skills can not be directly observed, but can only be estimated, i.e., its latency in nature. To tackle this problem, we propose a causal-driven skill prerequisite structure discovery (CSPS) method in a two-stage learning framework. In the first stage, we learn the skills' correlation relationships presented in the covariance matrix from the student performance data while, through the predicted covariance matrix in the second stage, we consider a heuristic method based on conditional independence tests and standardized partial variance to discover the prerequisite structure. We demonstrate the performance of the new approach with both simulated and real-world data. The experimental results show the effectiveness of the proposed model for identifying the skills' prerequisite structure.