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Direct linkAndreea Dutulescu; Stefan Ruseti; Denis Iorga; Mihai Dascalu; Danielle S. McNamara – Grantee Submission, 2025
Automated multiple-choice question (MCQ) generation is valuable for scalable assessment and enhanced learning experiences. How-ever, existing MCQ generation methods face challenges in ensuring plausible distractors and maintaining answer consistency. This paper intro-duces a method for MCQ generation that integrates reasoning-based explanations…
Descriptors: Automation, Computer Assisted Testing, Multiple Choice Tests, Natural Language Processing
Download full textKolb, John; Farrar, Scott; Pardos, Zachary A. – International Educational Data Mining Society, 2019
Misconceptions have been an important area of study in STEM education towards improving our understanding of learners' construction of knowledge. The advent of largescale tutoring systems has given rise to an abundance of data in the form of learner question-answer logs in which signatures of misconceptions can be mined. In this work, we explore…
Descriptors: Misconceptions, Expertise, Mathematics Teachers, Semantics
Michalenko, Joshua J.; Lan, Andrew S.; Waters, Andrew E.; Grimaldi, Philip J.; Baraniuk, Richard G. – International Educational Data Mining Society, 2017
An important, yet largely unstudied problem in student data analysis is to detect "misconceptions" from students' responses to "open-response" questions. Misconception detection enables instructors to deliver more targeted feedback on the misconceptions exhibited by many students in their class, thus improving the quality of…
Descriptors: Data Analysis, Misconceptions, Student Attitudes, Feedback (Response)
