Heating Up Online Learning: Insights from a Collaboration Employing Arts Based Research/Pedagogy for an Adult Education, Online, Community Outreach Undergraduate Course
Bibliographic record
Abstract
This article examines a three-stage collaboration in the design and implementation of a community outreach online course for an adult education program at a Canadian university. The collaboration used a participatory arts-based pedagogy approach that is designed to evoke thought rather than prescribe meanings. This manuscript has been structured to parallel a script format with Act I reporting how a group of university drama students employed the ‘playbuilding’ research/pedagogical methodology to devise a series of tableaus and video vignettes that examined concepts of community development that would be used in the design of an online community outreach and adult literacy elective course. Act II argues for and provides the devised script as evidence (data) of student learning. Act III discussed how an adult education instructor designed the new course, incorporating the vignettes as a central component and what was observed from delivering the online course over several iterations. Embedded in the discussion were: the processes involved in both instructional environments; and an examination of the impact of the dramatic pedagogical approach in the digital environment, particularly in relation to transformation, meaning making, and community outreach. The insights, however, are not coded in an etic analytical style. Rather, the authors used an emic approach with themes embedded within the narrative structure. Given its collaborative nature, the co-authors employ a polyvocal format through which their individual voices are made explicit.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.025 | 0.015 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| 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".