WHEN THE CLASSROOM BECOMES DATAFIED: A BASELINE FOR BUILDING DATA ETHICS POLICY AND DATA LITERACIES ACROSS HIGHER EDUCATION
Bibliographic record
Abstract
This paper overviews a summer 2020 pilot survey of educators’ perspectives on the intersection of educational technology and datafication in higher education classrooms. The brief, international survey of university teachers used four proxy questions to frame a baseline snapshot of higher education teaching populations’ knowledge, practices, experience, and perspectives on data and online learning: this paper focuses specifically on the results of the knowledge and practice questions. The paper suggests that, in the Emergency Remote Education (ERE) context generated by the COVID-19 pandemic, higher education instructors teaching online demonstrate patterns of limited knowledge and practice surrounding the data aspects of their classroom tools. The paper posits an urgent need for institutional and sector-wide policy and faculty development around data and online classroom tools, and for data ethics to be addressed as part of institutions’ ERE transition online.
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How this classification was reachedexpand
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.013 | 0.013 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads 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".