Integrating LinkedIn Learning through librarian–faculty collaboration: Case studies from a Canadian university
Bibliographic record
Abstract
This paper explores how librarian–faculty collaboration at a Canadian university promoted the strategic integration of LinkedIn Learning (LiL), a third party digital learning platform, into teaching, professional development and co-curricular activities. Using specific institutional case studies, the paper examines how librarians led instructional design, facilitated access to digital microlearning content and supported self-directed and reflective learning across academic and professional settings. The first case explores the integration of LiL into undergraduate commerce courses and a graduate eHealth programme where curated learning paths addressed specific skill gaps and improved student readiness. The second case investigates how LiL supported reflective practice and metacognitive development in an engineering management course, with measurable effects on student engagement and skill development. The third case showcases the use of LiL in staff training and development, including tailored learning pathways for academic library staff. Findings indicate that academic librarians played a crucial role in aligning LiL content with curricular and institutional objectives, fostering inclusive and adaptable learning environments and assisting learners in using digital tools effectively. The expiration of McMaster’s campus-wide LiL licence in autumn 2025 has raised sustainability concerns, including licensing expenses, access equity and long-term curricular planning. The study concludes that while third party platforms such as LiL can improve digital pedagogy, their success relies on cross-functional teamwork, continuous institutional backing and well-defined instructional alignment. The paper provides practical strategies for integrating digital platforms into curricula and examines the limitations of depending on proprietary tools. These findings add to broader discussions about digital transformation in higher education and the changing instructional roles of academic librarians and faculty collaboration. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.016 |
| 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; 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".