The Ethics of Student Privacy: Building Trust for Ed Tech
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
This article analyzes the opportunities and risks of data driven education technologies (ed tech). It discusses the deployment of data technologies by education institutions to enhance student performance, evaluate teachers, improve education techniques, customize programs, devise financial assistance plans, and better leverage scarce resources to assess and optimize education results. Critics fear ed tech could introduce new risks of privacy infringements, narrowcasting and discrimination, fueling the stratification of society by channeling “winners” to a “Harvard track” and “losers” to a “bluer collar” track; and overly limit the right to fail, struggle and learn through experimentation. The article argues that together with teachers, parents and students, schools and vendors must establish a trust framework to facilitate the adoption of data driven ed tech. Enhanced transparency around institutions’ data use philosophy and ethical guidelines, and novel methods of data “featurization,” will achieve far more than formalistic notices and contractual legalese.
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 | 0.020 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| 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