How to develop a GROOC: Establishing group dynamics in MOOCs
Bibliographic record
Abstract
The term GROOC has recently been defined, by Professor Mintzberg of McGill University (McGill, 2015), to describe group-oriented MOOCs, based on one he has developed on social activism. He has also made it clear that he sees no requirement to provide additional support to address group dynamics, stating that groups should be able to handle losing a few members and still function appropriately (Poets & Quants, 2015). However, the existing research in this area, building from a massive research base in traditional group work theory (Cohen & Lotan, 2014), has identified that group formation and maintenance require considerable extra planning and support. The authors have recently completed the first instantiation of a MOOC, on Entrepreneurship and Innovation in IT, as part of the dCCD-FLITE (distributed Concurrent Design Framework for eLearning in IT Entrepreneurship) research project (dCCD-FLITE, 2015), and their research has confirmed the difficulties in both forming and maintaining groups, and student reluctance to engage in group-based activities. In this paper we discuss the existing research on establishing group dynamics in MOOCs, identifying the key factors influencing success and failure, and then consider the outcomes from the dCCD-FLITE MOOC. The authors have already reported on this work, and have now further analysed the data gathered from the MOOC to consider alternative approaches to establishing Group Dynamics in MOOCs, and are currently planning to run the course once more utilising social media as a catalyst for group formation and maintenance.
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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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".