Open Data as driver of critical data literacies in Higher Education
Bibliographic record
Abstract
Participation in today’s datafied society requires a series of transversal skills. In fact, we need technical abilities and media literacies weaved in a critical approach to understand the socio-political and cultural mechanisms that affects individuals and groups. Higher Education (HE) must lead in the development of critical, socio-technical pedagogic approaches to understand and analyse data. To this end, adopting Open Data as the base of Open Educational Practices has potential to trigger authentic learning. situations. In this regard, the approach aims at going beyond the development of technical abilities to extract, elaborate and integrate Open Data in services, activities and projects. In fact, using data as OER in research-based learning activities for data journalism and civic monitoring techniques can be the catalyser for the appropriation of the datafied public spaces and also, to data ownership and activism. On the basis of these pedagogical practices, HE could play a key role in fostering critical approaches. The abilities developed in HE should transcend the classroom, to understand datafication in society. In time, HE students and teachers would contribute to shaping informed and transformative democratic practices and dialogue empowering citizens to address social justice concerns. This envisioned strategy requires of faculty development and engagement, as data literacies need of disciplinary and pedagogical efforts to innovate in curricular and learning design. Furthermore, supporting faculty’s awareness and practices to shape critical and ethical approaches to data implies care for spaces of dialogue at the juncture of technical and social needs. Care for interdisciplinary thinking and understanding the differences between “Psyche and Tekné”, building on Umberto Galimberti’s conceptualisation of the problem of balance between ethics/social sciences and technological advancement. Session content This workshop explores the educational potential of Open Data as a driver of interdisciplinary dialogue in learning design and pedagogical practices. It will offer instruments for designing educational interventions in two simple phases: 1- A conceptual (but dialogical!) introduction, to present the principles, the policy context and existing practices in citizen science, responsible research and innovation and Open Data, and the connections with data literacy in HE will be defined from the perspective of the researchers and their experiences in using Open Data for educational/learning purposes. An initial overview of the principles and resources to work with Open Data as OER in the context of Data in Education will be introduced. Also the frameworks to develop data literacy in HE will also be considered with a focus on the issues hindering these practices will be also displayed. 2- A “hands on” exercise in which the concepts above will be applied to the participants’ pedagogical practices, and their sense discussed on the light of both practical and deontological implications. The educational potential of Open Data in the participants perspective will collect personal reflections to understand in which extent the concept of open data could be applied to personal pedagogical practices. Which datasets could be useful? Which are the critical issues that I could face to use open data in my pedagogical practices? The reflections will be collected by using sticky notes and a map of possible future practices. Session recording: https://youtu.be/BZJX2BifYIg
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 | 0.038 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.012 | 0.040 |
| Scholarly communication | 0.034 | 0.028 |
| Open science | 0.002 | 0.031 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".