Semantic feedback for paper-based programming exams

Ihan Hsiao, Sesha Kumar Pandhalkudi Govindarajan

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

Abstract

We design and study ExamParser, an innovative intelligent semantic automatic indexing method, for orchestrating today's programming classes. ExamParser automatically processes paper-based exams by associating sets of concepts to the exam questions, which provide graders semantic grading guidelines and leave personalized semantic feedback. Results showed that the ExamPraser significantly extract more and diverse concepts from exams. It also achieves high coherence within exam, indicating the automatic concept extraction from exams is promising and could be a potential technological solution to provide personalized feedback for large-size programming classes.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE 16th International Conference on Advanced Learning Technologies, ICALT 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages65-69
Number of pages5
ISBN (Electronic)9781467390415
DOIs
StatePublished - Nov 28 2016
Event16th IEEE International Conference on Advanced Learning Technologies, ICALT 2016 - Austin, United States
Duration: Jul 25 2016Jul 28 2016

Other

Other16th IEEE International Conference on Advanced Learning Technologies, ICALT 2016
CountryUnited States
CityAustin
Period7/25/167/28/16

Keywords

  • Computing education
  • Personalized learning
  • Programming
  • Semantic feedback
  • Visual analytics

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Education
  • Computer Networks and Communications
  • Computer Science Applications

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