Teaching Knowledge Synthesis Methodologies in a Higher Education Setting: A Scoping Review of Face-to-Face Instructional Programs
Bibliographic record
Abstract
Abstract Background – Knowledge synthesis (KS) reviews are increasingly being conducted and published. Librarians are frequently taking a role in training colleagues, faculty, graduate students, and others on aspects of knowledge syntheses methods. Objective – In order to inform the design of a workshop series, the authors undertook a scoping review to identify what and how knowledge synthesis methods are being taught in higher education settings, and to identify particularly challenging concepts or aspects of KS methods. Methods – The following databases were searched: MEDLINE, EMBASE & APA PsycInfo (via Ovid); LISA (via ProQuest); ERIC, Education Research Complete, Business Source Complete, Academic Search Complete, CINAHL, Library & Information Science Source, and SocIndex (via EBSCO); and Web of Science core collection. Comprehensive searches in each database were conducted on May 31, 2019 and updated on September 13, 2020. Relevant conferences and journals were hand searched, and forward and backward searching of the included articles was also done. Study selection was conducted by two independent reviews first by title/abstract and then using the full-text articles. Data extraction was completed by one individual and verified independently by a second individual. Discrepancies in study selection and data extraction were resolved by a third individual. Results – The authors identified 2,597 unique records, of which 48 full-text articles were evaluated for inclusion, leading to 17 included articles. 12 articles reported on credit courses and 5 articles focused on stand-alone workshops or workshop series. The courses/workshops were from a variety of disciplines, at institutions located in North America, Europe, New Zealand, and Africa. They were most often taught by faculty, followed by librarians, and sometimes involved teaching assistants. Conclusions – The instructional content and methods varied across the courses and workshops, as did the level of detail reported in the articles. Hands-on activities and active learning strategies were heavily encouraged by the authors. More research on the effectiveness of specific teaching strategies is needed in order to determine the optimal ways to teach KS methods.
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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 | 0.113 | 0.301 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.031 | 0.026 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".