Smartlearn: Predicting learning performance and discovering smart learning strategies in flipped classroom

Yu Yang, Hanqing Wu, Jiannong Cao

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

7 Citations (Scopus)

Abstract

In flipped classroom, students are expected to learn new contents in online learning system before attending offline classes to reinforce their knowledge. This online and offline blended education model has become more and more popular. However, spending more time to actively engage in online learning does not result in better learning performance, so that how to wisely arrange online learning plan is a big challenge. In this paper, we build a LASSO model to accurately predict students' performance in course projects and their final grade by online learning behaviour data in flipped classroom. The LASSO selected features show that learning online between first and second flipped classes after midnight, and during the second flipped class would benefit students' project performance but studying one day before the examination and studying at night is counterproductive. Our results provide novel insight into guiding students to learn wisely and perform better in flipped classroom.

Original languageEnglish
Title of host publication2016 International Conference on Orange Technologies, ICOT 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages92-95
Number of pages4
ISBN (Electronic)9781538648315
DOIs
Publication statusPublished - 1 Feb 2018
Event2016 International Conference on Orange Technologies, ICOT 2016 - Melbourne, Australia
Duration: 18 Dec 201620 Dec 2016

Publication series

Name2016 International Conference on Orange Technologies, ICOT 2016
Volume2018-January

Conference

Conference2016 International Conference on Orange Technologies, ICOT 2016
Country/TerritoryAustralia
CityMelbourne
Period18/12/1620/12/16

Keywords

  • Academic performance
  • Behaviour trends
  • Flipped classroom
  • Learning analytics
  • Online learning

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Behavioral Neuroscience
  • Cognitive Neuroscience

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