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ERIC Number: ED560532
Record Type: Non-Journal
Publication Date: 2015-Jun
Pages: 8
Abstractor: As Provided
Reference Count: 45
ISBN: N/A
ISSN: N/A
Modeling Learners' Social Centrality and Performance through Language and Discourse
Dowell, Nia M.; Skrypnyk, Oleksandra; Joksimovic, Srecko; Graesser, Arthur C.; Dawson, Shane; Gaševic, Dragan; Hennis, Thieme A.; de Vries, Pieter; Kovanovic, Vitomir
International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015)
There is an emerging trend in higher education for the adoption of massive open online courses (MOOCs). However, despite this interest in learning at scale, there has been limited work investigating the impact MOOCs can play on student learning. In this study, we adopt a novel approach, using language and discourse as a tool to explore its association with two established measures related to learning: traditional academic performance and social centrality. We demonstrate how characteristics of language diagnostically reveal the performance and social position of learners as they interact in a MOOC. We use Coh-Metrix, a theoretically grounded, computational linguistic modeling tool, to explore students' forum postings across five potent discourse dimensions. Using a Social Network Analysis (SNA) methodology, we determine learners' social centrality. Linear mixed-effect modeling is used for all other analyses to control for individual learner and text characteristics. The results indicate that learners performed significantly better when they engaged in more expository style discourse, with surface and deep level cohesive integration, abstract language, and simple syntactic structures. However, measures of social centrality revealed a different picture. Learners garnered a more significant and central position in their social network when they engaged with more narrative style discourse with less overlap between words and ideas, simpler syntactic structures and abstract words. Implications for further research and practice are discussed regarding the misalignment between these two learning-related outcomes. [Additional funding for this research was provided by the Canada Research Chairs Program. For complete proceedings, see ED560503.]
International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Publication Type: Speeches/Meeting Papers; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: National Science Foundation (NSF); Institute of Education Sciences (ED); Bill and Melinda Gates Foundation; US Department of Homeland Security; Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada (NSERC)
Authoring Institution: International Educational Data Mining Society
IES Funded: Yes
Grant or Contract Numbers: REC 0106965; ITR 0325428; HCC 0834847; DRK-12- 0918409; R305G020018; R305A080589; Z934002/UTAA08-063