Educational Scholarship in the Digital Age: A Scoping Review and Analysis of Scholarly Products
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.
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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 | 0.025 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.045 | 0.044 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".