Open Data as driver of critical data literacies in Higher Education
Notice bibliographique
Résumé
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. \n \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. \n \nSession content \n \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 \n \nReferences \n \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 \n \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 \n \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 \n \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 \n \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 \n \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 \n \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
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,004 |
| Science ouverte | 0,114 | 0,208 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».