Unresolved Privacy and Ethics Issues Related to Learning Analytics in Higher Education and Academic Librarianship
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
Learning analytics involve big data collection, analysis processes, and technology that are used in higher education institutes and academic libraries to support student success and perform organizational assessment. Since these processes require the input of personally identifiable student and patron information to be effective, there are major ethical and legal considerations that must be addressed concerning privacy. This article demonstrates that privacy concerns about learning analytics can be mitigated by requiring informed consent from participants, establishing protocols for the collection and management of personally identifiable information, and advocating privacy rights of patrons. By synthesizing and expanding on viewpoints from the literature, this article offers recommendations pertaining to the collection, analysis, and management of patron data that are gathered for the purpose of learning analytics.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.014 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.000 | 0.001 |
| 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