Bibliographic record
Abstract
In recent years information technology and the World Wide Web in particular have changed profoundly the way we teach and learn, and this revolution will continue in the coming years with more and more universities, colleges and even elementary schools incorporating Web-based e-learning systems of some kinds into their education business. Different from other Web-based systems, a fully integrated e-learning system should provide different groups of users such as program directors, course authors, editors, course coordinators, instructors, tutors, students and administrators with access to different web documents and Web services, although they do share some of the documents and services. Therefore, the access control of such Web-based e-learning systems has become an issue for both researchers and practitioners in e-learning. In this paper, we present a role-based access control scheme that has been successfully used in the development EduPalace, an integrated Web- based system for e- learning and e-education. We will begin with an introduction to EduPalace to see what access control we need in such an integrated e-learning system, and then to present the access control scheme in detail. In conclusion, we will discuss some of the advantages of this scheme as well as how this scheme may be used in developing other Web-based systems such as those for e-business and e-service.
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 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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".