BOOK REVIEW: TEACHING IN A DIGITAL AGE: GUIDELINES FOR TEACHING AND LEARNING
Bibliographic record
Abstract
Dr. Tony Bates is a contemporary theorist in the field of educational technology. One of his most important books is Teaching in the Digital Age, which has received considerable attention around the world, and most of the educational planners and educators in the field of distance education use the book as a practical guide in the educational design of digital environments and is one of the internationally recognized sources in the field of online teaching. The book introduces the principles for effective teaching in an online environment and provides an instruction and guide for instructors about online teaching and learning and is also a good practice guideline for redesigning teaching and enables teachers and instructors to earn the knowledge and skills they will need in a digital age. This valuable collection has been translated into different foreign languages around the world includes translated versions in Turkish, Spanish, Vietnamese, French, Persian, Chinese,Portuguese, and is available on the BCcampus website in Canada as a recognized and credible open-source. Many countries are trying to translate the book Teaching in the Digital Age into the official language of their country in the future and make it available to researchers in the field of distance education.
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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.021 |
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".