Educational Scholarship in the Digital Age: A Scoping Review and Analysis of Scholarly Products
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Boyer’s framework of scholarship was published before significant growth in digital technology. As more digital products are produced by medical educators, determining their scholarly value is of increasing importance. This scoping systematic review developed a taxonomy of digital products and determined their fit within Boyer’s framework of scholarship. We conducted a broad literature search for descriptions of digital products in the medical literature in July 2013 using Medline, EMBASE, ERIC, PSYCHinfo, and Google Scholar. A framework analysis categorized each product using Boyer’s model of scholarship, while a thematic analysis defined a taxonomy of digital products. 7422 abstracts were found and 524 met inclusion criteria. Digital products mapped primarily to the scholarship of teaching (85.4%) followed by integration (7.6%), application (5.5%), and discovery (1.5%). A taxonomy of 19 categories was defined. Web-based or computer assisted learning (41%) was described most frequently. We found that digital products are well described in medical literature and fit into Boyer’s framework of scholarship and proposed a taxonomy of digital products that parallel traditional forms of the scholarship of teaching and learning. This research should inform the development of tools to examine the impact and quality of digital products.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it