Data-mining textual responses to uncover misconception patterns

Joshua Michalenko, Andrew S. Lan, Richard G. Baraniuk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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 (NLP) framework to detect the common misconceptions among students' textual responses to open-response, short-Answer questions. We introduce a probabilistic model for students' textual responses involving misconceptions and experimentally validate it on a real-world student-response dataset. Preliminary experimental results show that 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 is especially important at large scale, since instructors will no longer need to manually specify all possible misconceptions that students might exhibit.

Original languageEnglish (US)
Title of host publicationL@S 2017 - Proceedings of the 4th (2017) ACM Conference on Learning at Scale
PublisherAssociation for Computing Machinery, Inc
Pages245-248
Number of pages4
ISBN (Electronic)9781450344500
DOIs
StatePublished - Apr 12 2017
Event4th Annual ACM Conference on Learning at Scale, L@S 2017 - Cambridge, United States
Duration: Apr 20 2017Apr 21 2017

Other

Other4th Annual ACM Conference on Learning at Scale, L@S 2017
CountryUnited States
CityCambridge
Period4/20/174/21/17

Keywords

  • Learning analytics
  • Misconception detection
  • Natural language processing

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Education
  • Software
  • Computer Science Applications

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