People

I am interested in integrating innovative item types, such as answer‑until‑correct items, within latent variable models, and data mining, predictive modeling, and applications of AI (e.g., deep learning and large language models) for educational big data, with an emphasis on improved interpretability.

One of the innovative item types is answer‑until‑correct (AUC) items, which are multiple‑choice items where students can keep responding until they choose the correct option. I have developed new item response theory models for this testing procedure and explored applications such as computerized adaptive testing and detection of differential item functioning, which is critical for ensuring fairness across demographic groups.

Educational data mining can yield rich insights about students. From platform log data, we can learn their tendencies and interaction patterns and predict their proficiency, for example, the probability of answering items correctly. In one of my projects, I developed a deep learning model that uses students’ interactions with the system, curriculum information, and students’ class memberships to learn from the data and to uncover the effects of each action type and the associations among problems. Learn more on my Google Scholar page.