Ying (Alison) Cheng
ProfessorAssociate Director, Lucy Family Institute for Data and Society; Fellow, Institute for Educational Initiatives
- Office
- E442 Corbett Family Hall
Notre Dame, IN 46556 - ycheng4@nd.edu
Learning Analytics and Measurement in Behavioral Sciences (LAMBS)
Professor Cheng is open to admitting graduate students for fall 2024 matriculation.
Biography
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.