Compare Several Supervised Machine Learning Methods in Detecting Aberrant Response Pattern

Yi Lu, Yu Zhang, Lorin Mueller


Abstract
An aberrant response pattern, e.g., a test taker is able to answer difficult questions correctly, but is unable to answer easy questions correctly, are first identified lz and lz*. We then compared the performance of five supervised machine learning methods in detecting aberrant response pattern identified by lz or lz*.
Anthology ID:
2025.aimecon-main.3
Volume:
Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers
Month:
October
Year:
2025
Address:
Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States
Editors:
Joshua Wilson, Christopher Ormerod, Magdalen Beiting Parrish
Venue:
AIME-Con
SIG:
Publisher:
National Council on Measurement in Education (NCME)
Note:
Pages:
21–24
Language:
URL:
https://aclanthology.org/2025.aimecon-main.3/
DOI:
Bibkey:
Cite (ACL):
Yi Lu, Yu Zhang, and Lorin Mueller. 2025. Compare Several Supervised Machine Learning Methods in Detecting Aberrant Response Pattern. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers, pages 21–24, Wyndham Grand Pittsburgh, Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).
Cite (Informal):
Compare Several Supervised Machine Learning Methods in Detecting Aberrant Response Pattern (Lu et al., AIME-Con 2025)
Copy Citation:
PDF:
https://aclanthology.org/2025.aimecon-main.3.pdf