Crowdsourcing the (Un)Textbook: Rethinking and Future Thinking the Role of the Textbook in Open Pedagogy
Bibliographic record
Abstract
In this paper, we adopt a critical lens to investigate educators' understanding of both traditional and alternative textbooks and examine how open pedagogy may call for a rethinking of textbooks and how they are used in a pedagogical setting. Within the context of open pedagogy, including open textbooks, we conducted workshops that involved faculty, instructional designers, educational developers, and academic administrators during three conferences in 2019: OER19 Conference held in Galway, Ireland; the Cascadia Open Education Summit held in Vancouver, British Columbia; and the Educational Technology Users Group held in Kamloops, British Columbia. Based on data collected during these three interactive workshops, combined with personal reflections from the project instigators, we discuss emerging issues and tensions in the use of textbooks as pedagogical agents/artefacts in teaching and learning, and their relation to open pedagogy. Specifically, we consider what aspects of the use and design of textbook may be rethought in the context of open pedagogy as increasingly ubiquitous access to knowledge and open licensing of content and data become more widely available. This is achieved by prompting educators to describe the best and worst features of the traditional textbook format and reflect on what they might imagine as a potential future for the textbook as a resource to support open pedagogy.
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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 | 0.034 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".