Quality assurance and enhancement: An application of digitalised data

Siu Wah Julia Chen, Hua Fang Lin, Dennis Foung, Caroline Nixon

Research output: Chapter in book / Conference proceedingChapter in an edited book (as author)Academic researchpeer-review

Abstract

Quality assurance and enhancement exercises are important in higher education. Curriculum assurance and enhancement exercise, relied in the past primarily on raw assessment data and self-reported, which lacked follow-up mechanisms gauging its effectiveness. This paper reports on an impact study of a curriculum review exercise using both digitalised data and self-reported data. Both the original review and its impact study were conducted on an English Programme in a Hong Kong university taken by around 6,000 students each year. Both adopted a learning analytics approach with digitalised behavioural and assessment data. Results of the impact study, which is the focus of this paper, demonstrate the strength of using learning analytics, including its capability of inter-course and intra-course investigations. Learning analytics can also empirically confirm and/or refute concerns reported by teachers and students. The use of digitalised data for learning analytics offers opportunities to implement and follow-up on quality assurance measures.
Original languageEnglish
Title of host publicationDigital transformation and disruption of higher education
EditorsA. Kaplan
PublisherCambridge University Press
Pages130-144
ISBN (Print)9781108838900
DOIs
Publication statusPublished - Jun 2022

Keywords

  • Big Data
  • blended learning
  • curriculum review
  • English for academic purposes
  • impact study
  • learning analytics
  • online learning
  • tracer study

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