Bibliographic record
Abstract
Objective – To determine how librarians use evidence when planning a teaching or training session, what types of evidence they use and what the barriers are to using this evidence. The case study also sought to determine if active learning techniques help overcome the barriers to using evidence in this context. Methods – Five librarians participated in a continuing education course (CEC) which used active learning methods (e.g. peer teaching) and worked with a number of texts which explored different aspects of teaching and learning. Participants reflected on the course content and methods and gave group feedback to the facilitator which was recorded. At the end of the course participants answered a short questionnaire about their use of educational theory and other evidence in their planning work. Results – Findings of this case study confirm the existence of several barriers to evidence based user instruction previously identified from the literature. Amongst the barriers reported were the lack of suitable material pertaining to specific learner groups, material in the wrong format, difficulty in accessing educational research material and a lack of time. Participants gave positive feedback about the usefulness of the active learning methods used in the CEC and the use of peer teaching demonstrated that learning had taken place. Participants worked with significant amounts of theoretical material in a short space of time and discussion and ideas were stimulated. Conclusions – Barriers to engaging with evidence when preparing to teach may be addressed by provision of protected time to explore evidence in an active manner. Implementation would require organisational support, including recognition that working with research evidence is beneficial to practice.
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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".