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Examining the effect of a genetic algorithm-enabled grouping method on collaborative performances, processes, and

Xu Li1, Fan Ouyang2, WenZhi Chen1

  • 1College of Computer Science and Technology, Zhejiang University, 38 Zheda Rd., Hangzhou, 310027 People's Republic of China.

Journal of Computing in Higher Education
|June 7, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an optimized genetic algorithm for automatic grouping in computer-supported collaborative learning (CSCL), enhancing group performance and student perceptions compared to traditional methods.

Keywords:
Computer-supported collaborative learningGenetic algorithmGroup formationHigher educationLearning analytics

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Area of Science:

  • Educational Technology
  • Computer-Supported Collaborative Learning (CSCL)
  • Artificial Intelligence in Education

Background:

  • Group formation significantly impacts collaborative learning processes and outcomes.
  • Automatic grouping aims to maximize student diversity within groups but faces challenges like uneven group sizes and inaccessible student data.
  • Existing methods often struggle with optimal group composition and diversity.

Purpose of the Study:

  • To propose and evaluate an optimized, genetic algorithm-based grouping method for CSCL.
  • To address the challenges of uneven group size and inaccessibility of student characteristics in automatic grouping.
  • To compare the effectiveness of the proposed method against traditional grouping strategies.

Main Methods:

  • Development of a conceptual model and algorithm module for genetic algorithm-based grouping.
  • Quasi-experimental research design comparing automatic, random, and student-formed groups.
  • Analysis of group performance, collaborative processes, and student perceptions in higher education settings.

Main Results:

  • Groups formed using the optimized genetic algorithm method demonstrated superior final performance compared to random and student-formed groups.
  • Enhanced collaborative processes were observed in the experimental groups.
  • Students in the optimized groups reported more positive perceptions of the collaborative experience.

Conclusions:

  • The proposed genetic algorithm-based grouping method effectively addresses limitations of traditional automatic grouping techniques in CSCL.
  • Optimized automatic grouping can significantly improve learning outcomes, collaboration, and student satisfaction in higher education.
  • Findings support the integration of advanced grouping algorithms and collaborative analytics in CSCL environments.