A Survey of Public Library-Led Digital Literacy Training in Canada: Perceptions of Administrators and Instructors
Bibliographic record
Abstract
Objective – This paper presents the results from a survey of administrators and instructors at public libraries across Canada investigating the delivery of digital literacy training led by public libraries. The goal of the survey was to capture a snapshot of the Canadian public library-led digital literacy training landscape and to explore differences in perceptions of training activities between public library administrators and instructors. Methods – An online survey was distributed to administrators and instructors at public libraries across Canada with the help of two national public library associations. The survey instrument was developed based on a theoretical framework from the research team’s prior case study investigations of community-led digital literacy training. The survey included closed- and open-ended questions concerning the availability of adequate/sustained funding, the adequacy of dedicated classroom resources, the competency of teaching staff, the helpfulness of support staff, the amount and frequency of knowledge sharing of best practices, the amount of rigorous and regular performance measurement, the scheduling of the training provided, the skills taught, the pedagogical approaches used, and the marketing carried out. Responses were analyzed using both quantitative and qualitative data analysis techniques. Results – Public library administrators and instructors in Canada are generally satisfied with the delivery of digital literacy training; however, room for improvement exists. Instructors are more positive about the delivery of this training than administrators. Findings support and extend the research team’s conceptual model, specifically in terms of providing more insight and clarity on how the learning environment and program components affect the delivery of digital literacy training led by public libraries. Results highlight how training is situated in context and how libraries need to fine-tune the delivery of this training in ways that are reflective of libraries’ learning environments and program components. Conclusion – Results are of high interest to researchers and library practitioners who wish to leverage evidence-based library and information practice to understand and address the factors affecting the successful delivery of public library-led digital literacy training. Though funding is always an obstacle for any public service organization, libraries can make improvements to the delivery of their training in other ways, such as carrying out more robust performance measurement and using results more transparently, participating in more knowledge-sharing opportunities, and better understanding learner needs and preferences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.683 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".