My research focuses on educational statistics (psychometrics), machine learning and AI in educational data, and visualization.

• Latent variable modeling for innovative item types
• Data mining
• Deep learning and large language models
• Knowledge tracing
• Measurement models (item response theory and cognitive diagnosis models)

I am currently accepting doctoral students in the Quantitative Methods in Education (QME) program. Students with research interests that align with mine are encouraged to apply.

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Visit Yikai (EK) Lu's Google Scholar page

Research Tags:

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.

Education

• PhD, Joint in Psychology and Computer Science, University of Notre Dame

• MS, Computer Science and Engineering, University of Notre Dame

• MS, Applied and Computational Mathematics and Statistics, University of Notre Dame

• BS, Psychology, University of Minnesota - Twin Cities

• BS, Computer Science, University of Minnesota - Twin Cities

Selected Publications

Lu, Y., Folwer, J., & Cheng, Y. (2025) A family of sequential item response models for multiple-choice, multiple-attempt test items. Psychometrika. https://doi.org/10.1017/psy.2024.18

Lu, Y., & Wang, C. (2024) FAVis: Visual analytics of factor analysis for psychological research. In Proceedings of 2024 IEEE Visualization Conference (VIS).

Lu, Y., Tong, L., & Cheng, Y. (2024) Advanced Knowledge Tracing: Incorporating process data and curricula information via an attention-based framework for accuracy and interpretability. Journal of Educational Data Mining. https://doi.org/10.5281/zenodo.13712553

Ober, T. M., Lu, Y., Blacklock, C. B., Liu, C., & Cheng, Y. (2023). Development and validation of a cognitive load measure for general educational settings.Journal of Psychoeducational Assessment. https://doi.org/10.1177/07342829231169171

Lu, Y., Ober, T. M., Liu, C., & Cheng, Y. (2022). Application of Neighborhood Components Analysis to process and survey data to predict student learning of statistics. In Proceedings of 2022 IEEE International Conference on Advanced Learning Technologies (ICALT) (pp. 147-151). https://doi.org/10.1109/ICALT55010.2022.00051