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Robust SSRL analysis framework for intervention strategy construction in CSCL environment.

Li Chengzheng1, Peng Peng2, Cao Lei2

  • 1China West Normal University, Educational and Information Technology Center, Trivandrum 637000, China.

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|March 27, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a reliable machine learning framework for analyzing socially shared regulation of learning (SSRL) in collaborative learning. It enables automatic identification of key activities and sequences to optimize teaching interventions and improve learner performance.

Keywords:
Computer-supported collaborative learningEnsemble learningMachine learningSequential pattern miningSocially shared regulation of learning

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Area of Science:

  • Educational Technology
  • Artificial Intelligence in Education
  • Collaborative Learning

Background:

  • Socially Shared Regulation of Learning (SSRL) is crucial in Computer-Supported Collaborative Learning (CSCL).
  • Existing SSRL analysis lacks systematic frameworks for reliable, automated mining of regulation activities and transition sequences.
  • Current SSRL analysis is not effectively linked to developing or refining teaching intervention strategies.

Purpose of the Study:

  • To propose a robust framework for SSRL analysis using advanced machine learning techniques.
  • To automatically identify significant SSRL regulation activities and high-contribution transition sequences.
  • To develop and refine optimal teaching intervention strategies to enhance learner performance in CSCL.

Main Methods:

  • Development of a novel framework based on advanced machine learning techniques.
  • Implementation of an Ensemble Learning-based classification model incorporating four distilled regulation activities.
  • Iterative experimental validation of the framework and intervention strategies over five rounds.

Main Results:

  • The framework reliably identifies significant SSRL regulation activities and transition sequences.
  • The developed teaching intervention strategies demonstrably improve learner performance in CSCL environments.
  • The iterative process successfully updated and verified the intervention strategies based on empirical results.

Conclusions:

  • The proposed machine learning framework offers a reliable and automated approach to SSRL analysis in CSCL.
  • This work bridges the gap between SSRL analysis and the practical application of targeted teaching interventions.
  • The study contributes a novel method for enhancing SSRL analysis and optimizing collaborative learning outcomes.