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Record W7041951890

Open Data as driver of critical data literacies in Higher Education

2020· other· en· W7041951890 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Research Online (The Open University) · 2020
Typeother
Languageen
FieldComputer Science
TopicSemantic Web and Ontologies
Canadian institutionsnot available
Fundersnot available
KeywordsTransformative learningAppropriationHigher educationDisciplineDemocracyOpen dataOpen educationEconomic JusticeCritical thinking
DOInot available

Abstract

fetched live from OpenAlex

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. 
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\nThis 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. 
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\nSession content 
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\nThis 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: 
\n1- 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. 
\n2- 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.
\nSession recording: https://youtu.be/BZJX2BifYIg 
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\nReferences 
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\nAtenas, J. Havemann, L. (2019) Open Data and Education. In T. Davies, S. Walker, M. Rubinstein, F. Perini (Eds.), The State of Open Data: Histories and Horizons. Cape Town and Ottawa: African Minds and International Development Research Centre. Print version DOI: https://doi.org/10.5281/zenodo.2677851 
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\nAtenas, J., Havemann, L., Priego, E. (2015). Open Data as Open Educational Resources: Towards Transversal Skills and Global Citizenship. Open Praxis, 7(4), 377–389. https://doi.org/10.5944/openpraxis.7.4.233 
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\nManca, A., Atenas, J., Ciociola, C., Nascimbeni, F. (2017). Critical pedagogy and open data for educating towards social cohesion. Italian Journal of Educational Technology, 25(1), 111–115. https://doi.org/10.17471/2499-4324/862 
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\nRaffaghelli, J. E. (2017). Alfabetizzare ai dati nella società dei big e open data: una sfida formativa. FormazioneInsegnamento – European Journal of Research on Education and Teaching, 25(3), 279–304. https://doi.org/107346/-fei-XV-03-17_21 
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\nRaffaghelli, J. E. (2018a). Educators’ Data Literacy Supporting critical perspectives in the context of a “datafied” education. In M. Ranieri, L. Menichetti, M. Kashny-Borges (Eds.), Teacher education training on ict between Europe and Latin America (pp. 91–109). Roma: Aracné. https://doi.org/10.4399/97888255210238 
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\nRaffaghelli, J. E. (2018b). Open Data for Learning: A case study in Higher Education. In A. Volungeviciene A. Szűcs (Eds.), Exploring the Micro, Meso and Macro Navigating between dimensions in the digital learning landscape. Proceedings of the EDEN Annual Conference, 2018 (pp. 178–190). Genoa, Italy: European Distance and E-Learning Network. https://doi.org/978-615-5511-23-3 
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\nRaffaghelli, J. E. (2019). DEVELOPING A FRAMEWORK FOR EDUCATORS’ DATA LITERACY IN THE EUROPEAN CONTEXT: PROPOSAL, IMPLICATIONS AND DEBATE. In International Conference on Education and New Learning Technologies EDULEARN (pp. 10520–10530). Palma de Mallorca: IATED. https://doi.org/10.21125/edulearn.2019.2655

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesOpen science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.266
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.004
Open science0.1140.208
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.494
GPT teacher head0.501
Teacher spread0.006 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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