Learning beyond the course content: Development of an Open Access eTextbook powered by faculty, students and librarians
Bibliographic record
Abstract
Open-access eTextbooks have the potential to be dynamic digital resources that can be updated with research findings, adapted to align course learning outcomes and student learning goals, and developed to be more accessible to a greater number of learners. We have developed an open access eTextbook for second-year anatomy and physiology students at the University of Toronto Mississauga (UTM) to meet the specific learning requirements of second-year Biology students in an Animal Physiology course. In our presentation, we will share our collaborative journey of the creation of this open eTextbook and the eTextbook itself. Specifically, we will: a) share how we embedded course learning outcomes within the textbook and interactive elements of the textbook to better support student learning; b) share how librarians, staff, graduate students, and faculty collaborated and continue to collaborate on curating, creating and editing of the content developed for the open textbook; c) share how undergraduate students learn the content using the textbook but also how they apply skills beyond the course content expectations when using or interacting with the textbook; d) share challenges we encountered, and e) reflect upon next stages and issues on the horizon, as we engage in creating, improving and disseminating information about this open textbook project.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".