Ethics in educational technology research: Informing participants on data sharing risks
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
Abstract Participants in educational technology research regularly share personal data which carries with it risks. Informing participants of these data sharing risks is often only done so through text contained within a consent form. However, conceptualizations of data sharing risks and knowledge of responsible data management practices among teachers and learners may be impoverished—limiting the effectiveness of a consent form in communicating such risks in a manner that adequately supports participants in making informed decisions about sharing their data. At two high schools participating in an educational research project involving the use of technology in the classroom, we investigate teacher and student conceptions of data sharing risks and knowledge of responsible data management practices; and introduce a communication approach that attempts to better inform educational technology research participants of such risks. Results of this study suggest that most teachers have not received formal training related to responsibly managing data; and both teachers and students see the need for such training as they come to realize that their understanding of responsible data management is underdeveloped. Thus, efforts beyond solely explaining data sharing risks in an informed consent form may be needed in educational technology research to facilitate ethical self‐determination.
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 | 0.015 | 0.183 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.014 |
| Insufficient payload (model declined to judge) | 0.001 | 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