Evaluating learning in library training workshops: using the retrospective pretest design
Bibliographic record
Abstract
The aim of this study was to assess the effectiveness of an evaluation instrument using the retrospective pretest design to measure changes in participants' behaviour after library training. This article focuses on the measurement component of training evaluation the process of answering the question of how much change has occurred. Participants, who were from a large, public academic geriatric care centre in Toronto, included administrators, researchers, clinical and other staff, and university students doing field placements at the hospital. Participants attended one of four 1-hour sessions on the topic of Effectively Searching Google and Google Scholar that were held over a 3-month period. Sixty days post training, a self-administered retrospective pretest questionnaire, consisting of 10 searching behaviour statements developed using the learning objectives for the training session, was used to measure the impact of library training on participants' behaviour. Participants were asked to indicate their level of frequency of performing a searching behaviour described in the statement before and after training using a five-point, Likert-type scale ranging from 1, almost never; 2, seldom; 3, about half the time; 4, often; to 5, almost always. Summary baseline statistics are reported for respondents who never or rarely exhibited the behaviour prior to training. For the change measure, we report the simple percentage of respondents who improved. The findings of this study showed the potential of using the retrospective pretest to help librarians document the outcomes of library training. The benefits of gathering data using the retrospective pretest are discussed.
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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 | 0.084 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".