Social Presence in Two Massive Open Online Courses (MOOCs): A Multiple Case Study
Bibliographic record
Abstract
The purpose of this study was to explore the role social presence plays within two Massive Open Online Courses (MOOCs) offered by two American institutions of higher education through the Canvas and Ed.X learning software consortia. Social presence is one of three presences that comprise Garrison and colleagues’ Community of Inquiry (CoI) conceptual framework (Garrison, Anderson & Archer, 2000; Garrison, 2013). Descriptive multiple case study methodology was used for the study, with data collected through surveys, individual interviews, focus group interviews, and discussion board postings. Findings show that, while participants in MOOCs felt comfortable expressing themselves “as real people” (a key indicator of social presence), the majority did not view themselves as being part of a community of learners within their respective courses. Overall, in both MOOCs, participants experienced social presence least among the three CoI presences. Participants in both MOOCs experienced social presence as it helped them to realize learning objectives (i.e., to successfully complete their respective courses). Social presence played a supportive role to cognitive presence. Factors affecting social presence included participants’ ability and/or willingness to direct their own learning, types of available technology, availability of time, and depth of course content. There were three implications for practice for MOOC designers and facilitators. The first implication is that leveraging students’ personal interests through course activities and content can help enhance social presence. The second implication is that making more varied use of the features and functionality of learning management software can afford students additional and better opportunities for social interaction. The third implication is that encouraging greater amounts and quality of collaboration through the design of assignments and other assessment and evaluation items can lead to improved social presence, and an enhanced educational experience overall. Further MOOC research should address different kinds of MOOCs than were studied as part of this research, and a greater number of MOOCs, and using different research methodologies, and including greater amounts of MOOC designer and instructor perspectives. Further research on different elements of the CoI model and the areas of overlap among the three CoI presences within MOOCs is also warranted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".