Mobile grading paper-based programming exams: Automatic semantic partial credit assignment approach

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

8 Scopus citations

Abstract

In this paper, we report a study of an innovative mobile application to support grading paper-based programming exams. We call the app – Programming Grading Assistant (PGA). It scans pre-generated QR-codes of paper-based question-and-concepts associations and uses OCR to recognize handwritten answers. PGA provides interfaces for teachers to calibrate recognition results, as well as to adjust partial credit assignment according to conceptual incorrectness of the answers. We evaluate the mobile grading process and the quality of grading results based on the assessed semantic information. The results demonstrate that the mobile grading approach keeps persistent traces of students’ performance, including semantic feedback and ultimately enhances grading consistency.

Original languageEnglish (US)
Title of host publicationAdaptive and Adaptable Learning - 11th European Conference on Technology Enhanced Learning, EC-TEL 2016, Proceedings
PublisherSpringer Verlag
Pages110-123
Number of pages14
Volume9891 LNCS
ISBN (Print)9783319451527
DOIs
StatePublished - 2016
Event11th European Conference on Technology Enhanced Learning, EC-TEL 2016 - Lyon, France
Duration: Sep 13 2016Sep 16 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9891 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other11th European Conference on Technology Enhanced Learning, EC-TEL 2016
CountryFrance
CityLyon
Period9/13/169/16/16

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Hsiao, I. (2016). Mobile grading paper-based programming exams: Automatic semantic partial credit assignment approach. In Adaptive and Adaptable Learning - 11th European Conference on Technology Enhanced Learning, EC-TEL 2016, Proceedings (Vol. 9891 LNCS, pp. 110-123). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9891 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-45153-4_9