Ying (Alison) Cheng


Associate Director, Lucy Family Institute for Data and Society; Fellow, Institute for Educational Initiatives

E442 Corbett Family Hall
Notre Dame, IN 46556

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Learning Analytics and Measurement in Behavioral Sciences (LAMBS)

Professor Cheng is open to admitting graduate students for fall 2024 matriculation.

Primary Area: Quantitative


Dr. Ying "Alison" Cheng currently directs the Learning Analytics and Measurement in Behavioral Sciences (LAMBS) lab. Her research focuses on two areas: 1) Psychological and educational measurement; 2) Learning analytics.

In the first area, she is interested in theoretical development and applications of item response theory (IRT), including computerized adaptive testing (CAT), test equity across different ethnicity/gender groups (formally known as different item functioning or DIF), classification accuracy and consistency with licensure/certification of state graduation exams, and cognitive diagnostic models and their applications to CAT.

In the second area, she is interested in applying data mining techniques to large-scale, multi-modal learning data, e.g., process data collected from human-computer interactions.

Representative Publications

Ober, T. M., Hong, M. Reboucas, D., Carter, M., Liu, C. Cheng, Y.(2021). "Linking Self-report and Process Data to Performance across Different Assessment Types." Computers and Education. DOI: 10.1016/j.compedu.2021.104188

Hong, M., Lin, L. & *Cheng, Y. (2021). "Asymptotically Corrected Person Fit Statistics for Multidimensional Constructs." Psychometrika, 86,464–488.

Hong, M., Cheng, Y., & Steedle, J. (2020). "Comparing insufficient effort responding detection methods: Practical advice and recommendations." Educational and Psychological Measurement, 80, 312 –345. DOI: 10.1177/0013164419865316.

Yu, X., & Cheng, Y.(2019). "A change-point analysis procedure based on weighted residuals to detect back random responding." Psychological Methods, 24, 658 –674. DOI: 10.1037/met0000212.

Liu, C., & Cheng, Y. (2018). "An application of the support vector machine for attribute-by-attribute classification in cognitive diagnosis." Applied Psychological Measurement, 42,58-72.DOI: 10.1177/0146621617712246.