Personal, Institutional, and Societal Barriers to Educators’ Engagement with Datafication on Campus
Bibliographic record
Abstract
Datafied digital systems have permeated higher education over the past decade. Registration, grading, financial operations, alumni communications, and often teaching take place through digital platforms that extract and collate data, about students as well as faculty and staff. At the level of these data system processes, academics may not have the knowledge or practices to fully grasp the shift in their workplace that datafication represents. However, our research suggests that educators do understand the paradigm shift that datafication represents and have strong beliefs about how institutions should proceed to protect students and academia itself. Our team conducted an in-depth Comparative Case Study (CCS) investigation of how university educators make sense of the datafied infrastructures in and on which they work. This presentation overviews the knowledge, practices, experiences, and perspectives of educators in various institutional status positions from six different countries, in relation to datafied digital tools. We will focus particularly on the barriers that participants articulated to their own engagement with data, at personal, institutional, and societal levels. We will frame ways barriers are reinforced by institutional approaches to datafication, overview participants’ concerns, and explore how datafication has altered faculty’s power position as knowers within the academy.
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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 | 0.022 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.014 |
| Scholarly communication | 0.018 | 0.009 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".