Personalizing computer science education by leveraging multimodal learning analytics

David Azcona, Ihan Hsiao, Alan F. Smeaton

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

1 Scopus citations

Abstract

This Research Full Paper implements a framework that harness sources of programming learning analytics on three computer programming courses a Higher Education Institution. The platform, called PredictCS, automatically detects lower-performing or 'at-risk' students in programming courses and automatically and adaptively sends them feedback. This system has been progressively adopted at the classroom level to improve personalized learning. A visual analytics dashboard is developed and accessible to Faculty. This contains information about the models deployed and insights extracted from student's data. By leveraging historical student data we built predictive models using student characteristics, prior academic history, logged interactions between students and online resources, and students' progress in programming laboratory work. Predictions were generated every week during the semester's classes. In addition, during the second half of the semester, students who opted-in received pseudo real-time personalised feedback. Notifications were personalised based on students' predicted performance on the course and included a programming suggestion from a top-student in the class if any programs submitted had failed to meet the specified criteria. As a result, this helped students who corrected their programs to learn more and reduced the gap between lower and higher-performing students.

Original languageEnglish (US)
Title of host publicationFrontiers in Education
Subtitle of host publicationFostering Innovation Through Diversity, FIE 2018 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538611739
DOIs
StatePublished - Mar 4 2019
Event48th Frontiers in Education Conference, FIE 2018 - San Jose, United States
Duration: Oct 3 2018Oct 6 2018

Publication series

NameProceedings - Frontiers in Education Conference, FIE
Volume2018-October
ISSN (Print)1539-4565

Conference

Conference48th Frontiers in Education Conference, FIE 2018
CountryUnited States
CitySan Jose
Period10/3/1810/6/18

Keywords

  • Computer Science Education
  • Educational Data Mining
  • Learning Analytics
  • Machine Learning
  • Peer Learning
  • Predictive Modelling

ASJC Scopus subject areas

  • Software
  • Education
  • Computer Science Applications

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