Ethics in educational technology research: Informing participants on data sharing risks
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.
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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 | 0.334 | 0.419 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".