Title: Data-Mining Textual Responses to Uncover Misconception Patterns,
Abstract: 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 instruction. In this paper, we propose a new natural language
processing-based framework to detect the common misconceptions among students'
textual responses to short-answer questions. We propose a probabilistic model
for students' textual responses involving misconceptions and experimentally
validate it on a real-world student-response dataset. Experimental results show
that our proposed framework excels at classifying whether a response exhibits
one or more misconceptions. More importantly, it can also automatically detect
the common misconceptions exhibited across responses from multiple students to
multiple questions; this property is especially important at large scale, since
instructors will no longer need to manually specify all possible misconceptions
that students might exhibit.