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Knowledge components (KCs) have many applications. In computing education, knowing the demonstration of specific KCs has been challenging. This paper introduces an entirely data-driven approach for (i) discovering KCs and (ii) demonstrating KCs, using students’ actual code submissions. Our system is based on two expected properties of KCs: (i) generate learning curves following the power law of practice, and (ii) are predictive of response correctness. We train a neural architecture (named KC-Finder) that classifies the correctness of student code submissions and captures problem-KC relationships. Our evaluation on data from 351 students in an introductory Java course shows that the learned KCs can generate reasonable learning curves and predict code submission correctness. At the same time, some KCs can be interpreted to identify programming skills. We compare the learning curves described by our model to four baselines, showing that (i) identifying KCs with naive methods is a difficult task and (ii) our learning curves exhibit a substantially better curve fit. Our work represents a first step in solving the data-driven KC discovery problem in computing education. 

In this paper, we introduce ProgSnap2, a standardized format for logging programming process data. The goal of this common format is to encourage collaboration among researchers by helping them to share data, analysis code, and data-driven tools to support students. We first highlight possible use cases for ProgSnap2 and give a high-level overview of the format. We then share two case studies of our experience using the format and outline goals for the future of ProgSnap2, including a call for collaboration with interested researchers.