Building Creative Critical Online Learning Communities through Digital Moments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it