A multivariate learning evaluation model for programming course in online learning environment

Qingchun Hu, Yong Huang, Liping DENG

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

The items used for learning evaluation in online learning are not only scores, but also students' learning behavior, including engagement in learning contents, activities in online forum. This paper proposes a multivariate learning evaluation model to assess students learning in online learning environment for programming course. The learning behavior is accessed by data flow. The data flow is divided into four categories, which includes learning guidance, understanding innovation, interactive sharing and learning support. The correlation analysis of various structures and unstructured data flow generated in learning activities will be embodied in the multiple learning evaluation model as parameters. And the results are visualized to learners. The findings show that multivariate learning evaluation is helpful to improve students' achievement and reflection towards their learning.

Original languageEnglish
Title of host publication14th International Conference on Computer Science and Education, ICCSE 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages737-740
Number of pages4
ISBN (Electronic)9781728118444
DOIs
Publication statusPublished - Aug 2019
Event14th International Conference on Computer Science and Education, ICCSE 2019 - Toronto, Canada
Duration: 19 Aug 201921 Aug 2019

Publication series

Name14th International Conference on Computer Science and Education, ICCSE 2019

Conference

Conference14th International Conference on Computer Science and Education, ICCSE 2019
Country/TerritoryCanada
CityToronto
Period19/08/1921/08/19

Scopus Subject Areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems and Management
  • Education

User-Defined Keywords

  • Learning behavior
  • Learning evaluation
  • Online learning

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