Please use this identifier to cite or link to this item: https://doi.org/10.18608/jla.2022.7671
Title: Investigating Cognitive Network Models of Learners’ Knowledge Representations
Authors: Siew, Cynthia 
Issue Date: 11-Mar-2022
Publisher: Society for Learning Analytics Research
Citation: Siew, Cynthia (2022-03-11). Investigating Cognitive Network Models of Learners’ Knowledge Representations. Journal of Learning Analytics 9 (1) : 120-129. ScholarBank@NUS Repository. https://doi.org/10.18608/jla.2022.7671
Abstract: This commentary discusses how research approaches from Cognitive Network Science can be of relevance to research in the field of Learning Analytics, with a focus on modelling the knowledge representations of learners and students as a network of interrelated concepts. After providing a brief overview of research in Cognitive Network Science, I suggest that a focus on the cognitive processes that occur in the knowledge network, as well as the mechanisms that give rise to changes in the structure of knowledge networks, can lead to potentially informative insights into how learners navigate their knowledge representations to retrieve information and how the knowledge representations of learners develop and grow over the course of their educational careers. Learning Analytics can leverage these insights to design adaptive learning or online learning platforms that optimize learning, and inform pedagogical practice and assessment design that support the development of effective and robust knowledge structures.
Source Title: Journal of Learning Analytics
URI: https://scholarbank.nus.edu.sg/handle/10635/228942
ISSN: 19297750
DOI: 10.18608/jla.2022.7671
Appears in Collections:Elements
Staff Publications

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
7671-Article Text-36637-1-10-20220311.pdf591.54 kBAdobe PDF

OPEN

PublishedView/Download

SCOPUSTM   
Citations

1
checked on Oct 1, 2022

Page view(s)

28
checked on Oct 6, 2022

Download(s)

2
checked on Oct 6, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.