ORCID: https://orcid.org/0000-0002-9956-1670; Dascălu, Mihai; Tarnai, Christian and Trăușan-Matu, Ștefan
ORCID: https://orcid.org/0000-0001-8082-8497
(2020):
Predicting newcomer integration in online learning communities. Automated dialog assessment in blogger communities.
In: Computers in Human Behavior, Vol. 105: p. 106202
Abstract
Using online learning communities (OLCs) from the Internet as informal learning environments raises the question how likely these communities will integrate learners as new members, i.e., how integrative these OLCs will be. Such prediction is the purpose of the current study. To achieve this, an identification method of the central, intermediate, and peripheral OLC layers is proposed. Based on the CSCL approaches of voices interanimation and polyphony, an advanced natural language processing framework was employed for dialog analysis in N = 20 integrative vs. non-integrative blog-based OLCs involving 2342 users over one year. Hierarchical clusters built upon communicative centrality reflect socio-cognitive structures including central, intermediate, and peripheral OLC members. The resulting clusters were assigned to the central, intermediate and peripheral community layers with 55–100% consistency, whereas most consistent identification criterion of the socio-cognitive OLC structures was the outdegree centrality, followed by the blog owner inclusion, the numbers of participants, eccentricity and indegree centrality. Subsequently, OLC integrativity was predicted with up to 90% accuracy based on topic complexity, socio-cognitive structure, and automatically assessed dialog characteristics. Consequences for further research and educational practice are discussed.
Item Type: | Journal article |
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Keywords: | Learning communities; Newcomer integration; Dialog analysis; Social learning analytics |
Faculties: | Psychology and Education Science > Department Psychology > Education and Educational Psychology |
Subjects: | 100 Philosophy and Psychology > 150 Psychology 300 Social sciences > 370 Education |
ISSN: | 0747-5632 |
Language: | English |
Item ID: | 84384 |
Date Deposited: | 12. Jan 2022, 17:22 |
Last Modified: | 12. Jan 2022, 17:22 |