Learning management systems and online tools to support continuous workplace learning in academic libraries
Bibliographic record
Abstract
In today’s evolving academic landscape, which has been made even more changeable by the COVID-19 global pandemic, library managers and administration must consider accessible and sustainable methods for providing continuous workplace learning programmes for library staff. Establishing sustainable continuous workplace learning and professional development initiatives is critical for library staff to remain confident in their service delivery and use of digital tools. It is possible for libraries to develop online continuous workplace learning programmes that employ an array of online tools that are already in use by the library, such as those used for course delivery, internal documentation and online communication. Specifically, as many libraries make use of learning management systems (LMS) to embed their information literacy programming for faculty and students, there is an opportunity to strategically use LMS to support professional development and continuous workplace learning for library staff. Drawing from examples for Carleton University Library, this paper explores how the use of an LMS and other online tools for continuous workplace learning can provide library staff with equitable online access to develop essential technical and practical skills, while helping to build a workplace culture that prioritises learning and skill development. Employing these tools in continuous learning and training programmes can allow libraries to become ‘learningful’ workplaces where staff at all levels are supported and are confident in their work.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".