Bibliographic record
Abstract
This design case study describes the development of the Rethink Learning Design untextbook, an open educational resource designed to challenge traditional textbook structures and embrace open pedagogy. The project, initiated by a team of four educators, aimed to create a digital space that prioritizes interactivity, agency, accessibility, structure, and voice. Dissatisfied with existing platforms’ limitations in fostering non-linear learning and multi-vocality, the team collaborated with a Web developer to design a software tool to meet pedagogical needs. This tool allows for nonlinear organization of content, encourages multiple entry points, and allows for various open licensing options, facilitating a more inclusive and participatory learning experience. The resource features contributions from educators worldwide, organized into chapters that address various aspects of open and critical learning design. A key feature in the tool is the embedded reflective-practice framework, which encourages users to engage critically with the content and consider multiple perspectives. In this paper, we acknowledge ongoing design challenges, such as managing user annotations and feedback, and balancing learner agency with a navigable structure. Despite these challenges, the project offers a valuable model for developing open educational resources that promote critical engagement and challenge traditional pedagogical approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".