Small k-teams recommendation in social learning networks

Ankhtuya Ochirbat, Munkhtsetseg Namsraidorj, Wu Yuin Hwang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

A tool for efficient team formation based on students' proficiency and their communication in social networks is demonstrated. An experiment was conducted using two separate group discovering mechanisms with a pool of 68 students in two subjects. The proposed tool was used to discover groups in the experimental group with 31 students who followed the subject Object Oriented Analysis and Design and the grouping mechanism available in Moodle was used in the control group of 37 students who followed the subject Project Management during four weeks in the Spring semester of the 2013/2014 academic year. The study was carried out in accordance with a quasi-experimental research with a pretest and a post test design. The result indicates that small grouping based on the algorithm has better outcome.

Original languageEnglish
Title of host publicationProceedings - 2014 7th International Conference on Ubi-Media Computing and Workshops, U-MEDIA 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages286-291
Number of pages6
ISBN (Electronic)9781479942664
DOIs
StatePublished - 3 Oct 2014
Event2014 7th International Conference on Ubi-Media Computing and Workshops, U-MEDIA 2014 - Ulaanbaatar, Mongolia
Duration: 12 Jul 201414 Jul 2014

Publication series

NameProceedings - 2014 7th International Conference on Ubi-Media Computing and Workshops, U-MEDIA 2014

Conference

Conference2014 7th International Conference on Ubi-Media Computing and Workshops, U-MEDIA 2014
Country/TerritoryMongolia
CityUlaanbaatar
Period12/07/1414/07/14

Keywords

  • collaborative learning
  • small team recommandation
  • social learning network
  • team formation

Fingerprint

Dive into the research topics of 'Small k-teams recommendation in social learning networks'. Together they form a unique fingerprint.

Cite this