Integrating digital libraries and virtual learning environments
Bibliographic record
Abstract
Purpose This paper aims to compare three virtual learning environments (VLEs) (WebCT, Blackboard and creation of study environments) with respect to how well they have incorporated elements of digital libraries. Design/methodology/approach The comparative evaluation technique has been used to compare the three selected VLEs along five key dimensions of digital libraries: content/format support, metadata, search/browse features, customizability and preservation. Findings Within the three selected VLEs, content reusability, search/browse functionality, along with customizability and personalizability appear to be the best addressed digital library elements. Research limitations/implications This paper gives a sense of how well some current VLEs are implementing elements of digital libraries, as well as what areas are lacking. The results could have been further enhanced by examining additional VLEs. Practical implications This study provides a window into what options currently exist with respect to the integration of digital libraries and VLEs, as well as where these packages should go in the future. It provides recommendations related to seamless access, metadata implementation, controlled vocabulary and preservation. Originality/value This paper is of value to librarians, digital library developers, instructors and VLE designers – giving them feedback on how these environments should be structured to enhance information access. It is the first comparative evaluation of these three VLEs with respect to the implementation of digital library elements.
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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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".