Predicting Students' Future Success: Harnessing Clickstream Data with Wide & Deep Item Response Theory

We propose a novel model, Wide & Deep Item Response Theory (Wide & Deep IRT), to predict the
correctness of students’ responses to questions using historical clickstream data. This model combines
the strengths of conventional Item Response Theory (IRT) models and Wide & Deep Learning for Recommender
Systems. By leveraging clickstream data, Wide & Deep IRT provides precise predictions of
answer correctness while enabling the exploration of behavioral patterns among different ability groups.
Our experimental results based on a real-world dataset (EDM Cup 2023) demonstrate that Wide &
Deep IRT outperforms conventional IRT models and state-of-the-art knowledge tracing models while
maintaining the ease of interpretation associated with IRT models. Our model performed very well in the
EDM Cup 2023 competition, placing second on the public leaderboard and third on the private leaderboard.
Additionally, Wide & Deep IRT identifies distinct behavioral patterns across ability groups. In the
EDM Cup 2023 dataset, low-ability students were more likely to directly request an answer to a question
before attempting to respond, which can negatively impact their learning outcomes and potentially indicates
attempts to game the system. Lastly, the Wide & Deep IRT model consists of significantly fewer
parameters compared to traditional IRT models and deep knowledge tracing models, making it easier to
deploy in practice. The source code is available via Open Science Framework https://osf.io/8vcfd/.