The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Social presence in online discussions as a process predictor of academic performance
Open accessAbstract
With the steady development of online education and online learning environments, possibilities to support social interactions between students have advanced significantly. This study examined the relationship between indicators of social presence and academic performance. Social presence is defined as students' ability to engage socially with an online learning community. The results of a multiple regression analysis showed that certain indicators of social presence were significant predictors of final grades in a master's level computer science online course. Moreover, the study also revealed that teaching presence moderated the association between social presence and academic performance, indicating that a course design that increased the level of meaningful interactions between students had a significant impact on the development of social presence, and thus could positively affect students' academic performance. This is especially important in situations when discussions are introduced to promote the development of learning outcomes assessed in courses. Another implication of our results is that indicators of social presence can be used for early detection of students at risk of failing a course. Findings inform research and practice in the emerging field of learning analytics by prompting the opportunities to offer actionable insights into the reasons why certain students are lagging behind.
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Metadata
- Authors
- Joksimović, S., Gašević, D., Kovanović, V., Riecke, B. E., & Hatala, M.
- Venue
- Journal of Computer Assisted Learning
- Publisher
- Wiley
- Published
- 2015-06-04
- Volume
- 31
- Issue
- 6
- Pages
- 638-654
- ISSN
- 0266-4909 (ISSN-L)
- 1365-2729
- OpenAlex
- W1504818606
- Cited by
- 278
- References
- 8
- FWCI
- 59.51
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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