Four papers are accepted at EDM21 in Paris, France. The topics of the papers cover student sub-goal detection, student performance prediction, bug detection, and detection of progress/struggling moments in CS courses for novice programmers.
Yang will be presenting a paper “Toward Semi-Automatic Misconception Discovery Using Code Embeddings” this year at LAK. The paper uses deep learning methods to extract embedding vectors from student code submissions in an introductory programming class in a US university, and use these embeddings to detect possible misconceptions by clustering them in the vector space. The results suggest that code embeddings can be used to find misconceptions that cannot be detected by normal methods such as tree edit distances.
HINTS lab has received $6,000 research credits from Amazon Web Services for the research of Representation Learning from CS Education Data. This research fund, granted to Dr. Price and Yang will be used to support intensive computing in leveraging machine learning models for CS education research topics.