The Educators’ Datafied Present and Future: Complexity as an Approach to Developing Educators' Data Literacies
Bibliographic record
Abstract
In the higher education context, an increasing concern on the technical or instrumental approach permeates attention to academics’ data literacies and faculty development. The need for data literacy to deal specifically with the rise of learning analytics in higher education has been raised by some authors, though in spite of some focus on the need to develop academics’ data literacy to embrace fair practices, this literature is often also rooted in a technical or data-driven perspective. In this paper, the authors summarize an empirical study based on 137 articles using the terms “data literacy,” “teachers,” and “faculty development,” spanning from 2014 and 2019. The findings point out that out of the total, 78 papers reviewed took an instrumental, data science-focused perspective on data literacy, were the technical abilities like extracting data and interpreting or reporting appropriately (authors, in press). Data safety and effective data management perspectives accounted for another 35 of the 137 articles. Only seven took up data literacy from a critical perspective, while only five looked at the pedagogical practice. These preliminary findings require awareness and discussion on the light of appropriate faculty development approaches and activities. We introduce some recommendations aimed at understanding data as a complex emerging phenomenon in our societies, which requires building the literacies to face their negative effects like data surveillance and algorithmic biases, but also, to uncover its emancipatory power.
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.
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 | 0.070 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.007 |
| Science and technology studies | 0.008 | 0.037 |
| Scholarly communication | 0.034 | 0.037 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.004 | 0.008 |
| 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".