Understanding Classroom Interaction Using Epistemic and Social Network Analysis

Xieling CHEN, Di ZOU*, Gary CHENG, Haoran XIE

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Referred Conference Paperpeer-review

Abstract

This study focuses on understanding classroom interaction using epistemic and social network analysis. Based on the classroom interaction data concerning an online course named Justice with 12 episodes, we demonstrate epistemic and social network analyses’ advantages in evaluating the quality of classroom interaction, instructors’ performance in promoting students’ higher-order thinking, and individual students’ performance in the classroom interaction. Results suggest that to promote productive classroom interaction and students’ high-level cognitive contributions, instructors can 1) ask questions that explicitly require explanations, 2) encourage students to think with others, 3) encourage children to think about counterexamples or to challenge others’ opinions or ideas, and 4) arrange group discussion activities.
Original languageEnglish
Title of host publicationICBL 2022: Blended Learning: Engaging Students in the New Normal Era
EditorsRichard Chen LI, Simon S. K. CHEUNG, Peter H. F. NG, Leung-Pun WONG, Fu Lee WANG
PublisherSpringer, Cham
Pages157-167
ISBN (Electronic)9783031089398
ISBN (Print)9783031089381
DOIs
Publication statusPublished - 18 Jun 2022

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume13357
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Classroom interaction
  • Epistemic network analysis
  • social network analysis
  • Visualization

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