Integrating digital libraries and virtual learning environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.009 |
| Open science | 0.001 | 0.001 |
| 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 it