Exploring an Approach for Grouping through Predicting Group Performance from Analysis of Learner Characteristics

Jingyun Wang, Kojima Kentaro

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

Abstract

In this paper, we present a mathematical model for forming heterogeneous groups of learners under different teaching strategies. This model requires a formulation which can effectively predict the learning performance of cooperative learning groups. Therefore, we explore the correlations between learning performance and various learner characteristics including learning motivation, learning strategy use, learning styles and gender based on real-world data. By means of analyzing learner data of 157 students in a cooperative learning course, learner attributes irrelevant to cooperative learning performance are excluded from the formulation; this sharply decreases the workload of group formation calculation. In future work, a tool will be implemented based on this adjustable mathematical model and this tool will be used in daily teaching to evaluate its effectiveness.

Original languageEnglish
Title of host publicationProceedings - 2018 7th International Congress on Advanced Applied Informatics, IIAI-AAI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages288-293
Number of pages6
ISBN (Electronic)9781538674475
DOIs
Publication statusPublished - Jul 2 2018
Event7th International Congress on Advanced Applied Informatics, IIAI-AAI 2018 - Yonago, Japan
Duration: Jul 8 2018Jul 13 2018

Publication series

NameProceedings - 2018 7th International Congress on Advanced Applied Informatics, IIAI-AAI 2018

Conference

Conference7th International Congress on Advanced Applied Informatics, IIAI-AAI 2018
CountryJapan
CityYonago
Period7/8/187/13/18

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Communication
  • Information Systems
  • Information Systems and Management
  • Education

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