Building Creative Critical Online Learning Communities through Digital Moments
Bibliographic record
Abstract
This paper is a mixed methods case study measuring student perceptions of a pedagogical strategy called “Digital Moments” (DM) for developing creative interactive online learning communities. The theoretical framework within which this resides is the Fully Online Learning Community (FOLC) model (vanOostveen et al, 2016), based on a foundation of problem‑based learning, cognitive and social presence, and learner‑centred pedagogies.The article reviews a specific teaching strategy for increasing social presence and student engagement through the use of creative and artistic expression in problem‑based learning spaces. Using “Digital Moments” as a way to build inclusion in two synchronous graduate online courses, the author describes how the teaching strategy increased student participation, developed student ownership of learning, and encouraged collaborative processes between participants. This teaching strategy makes a significant contribution to digital pedagogy. Although the growth of online learning is quite substantial, our ability to develop online communities that inspire critical and creative thinking has not kept pace. Traditional teacher‑centred learning environments do not meet the needs of students in today’s Fourth Industrial Revolution. As such, the FOLC model provides an online learning community model that removes traditional teacher‑learner roles, allows the instructor to act as a facilitator and challenges learners to co‑design and co‑create the learning process. Within this digital space, collaborative disruption is encouraged, and, in fact necessary for the types of critical and creative thinking to emerge that are central to the FOLC model. Digital Moments, is one example of a pedagogical strategy that enables learners to co‑create and own the digital learning space, within a fully online learning community.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".