Comparing the visual affordances of discrete time Markov chains and epistemic network analysis for analysing discourse connections

Datos Bibliográficos

ID22162201
AutoresDaniela Vasco (0000-0001-5042-7449, Griffith University, autor de correspondencia), Kate Thompson (0000-0003-0738-0205, Queensland University of Technology), Sakinah S J Alhadad (0000-0002-0883-1883, Griffith University), Sakinah Alhadad, M Zahid Juri (Queensland University of Technology)
Año2024
Volumen9
Fecha de publicación2024-07-16
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaFrontiers in Education (JOURNAL)
Identificadores de la revistaISSN: 2504-284X • E-ISSN: 2504-284X
EditorialFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/feduc.2024.1401996
OpenAlexW4400695108
IdiomaEN
Referencias citadas34

Introduction Researchers in the learning sciences have been considering methods of analysing and representing group-level temporal data, particularly discourse analysis, in Computed Supported Collaborative Learning for many years. Methods This paper compares two methods used to analyse and represent connections in discourse, Discrete Time Markov Chains and Epistemic Network Analysis. We illustrate both methods by comparing group-level discourse using the same coded dataset of 15 high school students who engaged in group work. The groups were based on the tools they used namely the computer, iPad, or Interactive Whiteboard group. The aim here is not to advocate for a particular method but to investigate each method’s affordances. Results The results indicate that both methods are relevant in evaluating the code connection within each group. In both cases, the techniques have supported the analysis of cognitive connections by representing frequent co-occurrences of concepts in a given segment of discourse. Discussion As the affordances of both methods vary, practitioners may consider both to gain insight into what each technique can allow them to conclude about the group dynamics and collaborative learning processes to close the loop for learners

Affordance · Collaborative learning · Conversation · Conversation analysis · Discourse analysis · Group work · Human–computer interaction · Knowledge management · Linguistics · Machine learning · Markov chain · Mathematics education · Multimedia · Whiteboard · Cognitive Science and Mapping · Complex Network Analysis Techniques · Computer Science · Innovative Teaching and Learning Methods · Psychology