Bibliographic record
Abstract
The “discussion post” has been a staple in higher education online classrooms for decades. While educators of online learning widely rely on asynchronous discussion posting to engage students using institutional learning management systems (LMS), discussion posting requires mediation and motivation to sustain participation, is considered task-oriented by students, and has been frequently criticized for inauthentic dialogue. The Slides Strategy, which utilizes a collaborative Google Slide deck in concert with Parallaxic Praxis, a knowledge-generating framework, creates an effective environment for meaningful engagement – demonstrating student understanding of material, creating classroom community, and provoking rich, critical dialogue. Collaborative slides used as a pedagogical tool in this way encourage value of all perspectives, diverse modality and thought, and inclusivity through a platform that allows different literacies to cohabitate, working toward decolonizing academia. This paper contextualizes reflections from five asynchronous online courses taught by different instructors, and provides evidence assessing the effectiveness of this strategy through instructor and student perspectives. As educational institutions continue to grapple with an increasing reliance on, and need for, innovative, dynamic, and supportive online learning environments in a post-pandemic landscape, the Slides Strategy moves the online discussion post to a more authentic and critically reflexive academic conversation.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.049 | 0.015 |
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".