Open textbooks: We have led the horse to water – now what?
Bibliographic record
Abstract
Poster presentation at the Canadian Association of Physicists conference at SFU, Burnaby (June 3-7, 2019).\n The use of open textbooks is increasing dramatically in first year physics courses. This poster presents the results of scholarly research around student perceptions, the use and impact of open textbooks, as well as suggestions for how instructors might change what they do in their classroom around their use of open textbooks. Comparing and contrasting student’s attitudes in first year physics, astronomy and biology classes to open textbooks is the theme of this poster. It also relates attitudes towards open educational resources (OER) to simple demographic information and the overall cost of textbooks to determine whether there are indicators that can be measured a priori to suggest that students in a particular course may be more or less receptive to the incorporation of OER. More than 300 students were surveyed in 10 courses over two years at Douglas College so there is enough data to form interesting correlations. The questions that were asked included demographic questions as well as questions such as “How often does your instructor encourage you to read your textbook?” and “What is your best estimate of the percentage of exam questions that could be correctly answered using only the textbook?” Results: Student perceive that the open textbook is as\ngood as or better than commercial books. Satisfaction increases as the book is modified to match learning outcomes.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 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".