A novel metaheuristic approach for collaborative learning group formation.
In this paper, a new approach for the formation of four‐member collaborative learning groups is presented. Group formation is presented by the mathematical optimization problem. Based on the proposed approach and the variable neighbourhood search (VNS) algorithm, the application that solves the pres...
| Published in: | Journal of Computer Assisted Learning Vol. 34; no. 6; pp. 907 - 917 |
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| Main Authors: | , , , |
| Format: | algorithm equations & formulas research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
Dec2018
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=132914555&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 132914555 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Dec2018 vid: 34 iid: 6 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 132914555 132914555 132914555 10.1111/jcal.12299 132914555 ppf: 907 ppct: 10 formats: tig: atl: A novel metaheuristic approach for collaborative learning group formation. aug: au: Lambić, Dragan Lazović, Bojana Djenić, Aleksandar Marić, Miroslav affil: Faculty of Education, University of Novi Sad, Serbia sug: subj: Collaboration Learning Methods Learning Group Processes Algorithms Problem Solving Interpersonal Relations Social Behavior Serbia Student Selection Computer-Assisted Instruction Learning Laboratories Software Design Academic Performance Regression Male Female Adult Funding Source Human Adult: 19-44 years Male Female ab: In this paper, a new approach for the formation of four‐member collaborative learning groups is presented. Group formation is presented by the mathematical optimization problem. Based on the proposed approach and the variable neighbourhood search (VNS) algorithm, the application that solves the presented problem and provides the appropriate division into groups is created. The proposed approach considers the scores of a pretest, interpersonal relationships, and prosocial behaviour/openness skill of students. In order to validate our approach, an experiment was designed with 108 first‐year university students of Belgrade Business School—Higher Educational Institution for Applied Studies. Experimental and control groups were divided into four‐member groups. The experimental group was divided by using the proposed method and the control group by student selection and random selection. Multilevel analysis is used to determine whether there is a significant difference in learning outcomes between the two groups. The experimental results showed that students from the experimental group achieved significantly higher success than the students from the control group. In addition, computational results obtained with the proposed VNS algorithms are compared and verified with the results obtained by random (Monte Carlo) method. Lay Description: What is currently known about the subject matter of this paper: Group formation have great influence on the success of collaborative learning.Consideration of multiple variables for group formation can be very demanding.Previous computer‐based methods for group formation do not use VNS. What this paper adds: In this paper, new metaheuristic approach for group formation is proposed.The proposed approach considers the scores of a pretest.The proposed approach considers interpersonal relationships.The proposed approach considers prosocial behaviour/openness skill of students. The implications of study findings for practitioners: The proposed approach have positive impact on students' academic performance.This approach can divide a large number of students into smaller groups based on multiple variables. pubtype: Academic Journal doctype: algorithm equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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