Towards Safeguarding Users’ legitimate rights in Learning Management Systems (LMS): A case study of the Blackboard LMS at Sorbonne University, Abu Dhabi (SUAD).
Bibliographic record
Abstract
This paper sought to establish the extent to which users’ legitimate rights are safeguarded in Learning management systems (LMS), specifically, on the Blackboard system, used for teaching at Sorbonne University, Abu Dhabi (SUAD). Firstly, users’ legitimate rights that must be protected were identified. Subsequently, the security and privacy guarantees afforded by Blackboard were assessed. Lastly, policy gaps and technological deficiencies undermining protection of users’ legitimate rights were identified. The study adopted a qualitative research approach and a case study research design. Data was collected through content analysis, document review and interviews. The research revealed that to a large extent Blackboard, LMS safeguarded most of the users’ legitimate rights. However, the system is silent on some legitimate rights such as storage limitation and data sharing arrangements. Further, it emerged that Blackboard’s privacy practices are to a large extent informed by educational institutions using its products. The study concludes that safeguarding user’s legitimate rights is a collective responsibility between the learning management services providers and the educational institutions. As such, there is need for educational institutions using Blackboard and other learning management systems to craft robust data protection regimes. Keywords: Learning Management Systems, Privacy, Users' legitimate rights
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| 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".