Enhancing Users’ Perceived Significance of Academic Library with MOOC Services
Bibliographic record
Abstract
Objective – Academic libraries have been impacted by the tremendous changes taking place in higher education due to the arrival of the internet and web-based technologies. Several articles have shown the decline in library usage and user need for electronic resources. The entry of MOOCs into higher education has repurposed the library’s roles and services. This research aims to explore the possible MOOC services of academic libraries and their effect on the user perception towards the significance of academic libraries. Methods – The academic library’s MOOC services are derived from the extensive literature review and subsequently a research model based on extant literature has been developed to evaluate user behaviour. The research model is evaluated using confirmatory factor analysis methods. Results – The academic library’s services for MOOCs have been categorized as, (a) user support services, (b) information services, and (c) infrastructure services. The study shows that each of these service categories have a positive impact on the library usage intention of the users. This in turn has a positive effect on the library’s perceived significance. Conclusion – The library services for MOOC users defined in this research and the findings are useful for librarians to develop new service strategies to stay relevant for the user.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".