Evaluating an Instructional Intervention for Research Data Management Training
Bibliographic record
Abstract
Objective – At a large research university in Canada, a research data management (RDM) specialist and two liaison librarians partnered to evaluate the effectiveness of an active learning component of their newly developed RDM training program. This empirical study aims to contribute a statistical analysis to evaluate an RDM instructional intervention. Methods – This study relies on a pre- and post-test quasi-experimental intervention during introductory RDM workshops offered 12 times between February 2022 and January 2023. The intervention consists of instruction on best practices related to file-naming conventions. We developed a grading rubric differentiating levels of proficiency in naming a file according to a convention reflecting RDM best practices and international standards. We used manual content analysis to independently code each pre- and post-instruction file name according to the rubric. Results – Comparing the overall average scores for each participant pre- and post-instruction intervention, we find that workshop participants, in general, improved in proficiency. The results of a Wilcoxon signed-rank test demonstrate that the difference between the pre- and post-test observations is statistically significant with a high effect size. In addition, a comparison of changes in pre- and post-test scores for each rubric element showed that participants grasped specific elements more easily (i.e., implementing an international standard for a date format) than others (i.e., applying information related to sequential versioning of files). Conclusion – The results of this study indicate that developing short and targeted interventions in the context of RDM training is worthwhile. In addition, the findings demonstrate how quantitative evaluations of instructional interventions can pinpoint specific topics or activities requiring improvement or further investigation. Overall, RDM learning outcomes grounded in practical competencies may be achieved through applied exercises that demonstrate immediate improvement directly to participants.
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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 | 0.008 | 0.027 |
| 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".