NURTURING OPEN CONNECTIONS AND COLLABORATION AGAINST THE DOMINATION OF DATAFICATION IN HIGHER EDUCATION.
Juliana Raffaghelli. Professor, Department of Psychology and Education, Universitat Oberta de Catalunya – email@example.com – ORCID 0000-0002-8753-6478
Javiera Atenas. Latin American Initiative for Open Data; Open Education Working Group; Research Fellow, Departamento de Didácticas Aplicadas, Facultad de Educación, Universitat de Barcelona Javiera.firstname.lastname@example.org – ORCID 0000-0001-5006-529X
With the kind collaboration of Leo Havemann, Postgraduate Researcher, The Open University (UK) 2. Digital Education Advisor, University College London. email@example.com – ORCID 0000-0003-1843-l
The ongoing phenomenon of datafication is pervasive and it is characterizing a paradigmatic change in the way human knowledge is processed, circulated and used. While there was an initial vision of utopia connected to the full access to massive amounts of data and the pro-active developments of artificial intelligence applied to data, in the recent years there has been a dystopian vision mainly driven by the reaction of the society to the odds of surveillance, discrimination and exclusion connected to data-driven practices.
Pro-action and re-action could be deemed as new epistemologies relating datafication. However, Open Education and pedagogies could pave a third way connected to cultivating data literacy as a mean to foster socio-political activism, participation and awareness on datafication.
This, in time, could become a driver of societal transformation towards the appropriation of the datafied public spaces. Therefore, HE, and mostly, public universities could play a key role in fostering critical approaches to knowledge production and the phenomenon of datafication in the society. In time, this would contribute to shape informed and transformative democratic practices and dialogue empowering citizens to address social justice concerns. However, this envisioned strategy requires faculty development and engagement. In Higher Education Institutions, fostering data literacies requires of disciplinary and pedagogical efforts to innovate in curricular design to design learning activities, using a research based learning approach (Atenas, Havemann, & Priego, 2015; Maybee & Zilinski, 2015; Raffaghelli, 2018b)
Despite the growing phenomenon of datafication at society and educational level, there is dearth of documentation on academic development in data literacies. There is a growing number of studies on teachers’ data literacy relating teachers’ skills to deal with school data, particularly to analyze the school performance and supporting school management (Dunlap & Piro, 2016; Hoogland et al., 2016; Mandinach & Gummer, 2016). For example, for Mandinach and Gummer (2013), teachers’ data literacy is to be defined as “the ability to understand and use data effectively to inform decisions … composed of a specific skill set and knowledge base that enables educators to transform data into information and ultimately into actionable knowledge” (p. 30). In their framework for teachers’ data literacy Mandinach and Gummer (2016) suggest a data-driven workflow with five components: Identify problems and Frame Questions through Data; Use Data; Transform Data into Information; Transform Information into Decision; Evaluate Outcomes. This applies to assessment and evaluation in learning processes, but it is mostly connected to school management (Schildkamp & Kuiper, 2010). At this point, it is of particular interest to notice that these authors have developed their approach in the context of the American Evidence-Based Education movement, where evidence is intended as highly quantitative data informing the policy context (Slavin, 2002).
Teachers’ “Data-driven” decision taking is intended as the ability of collecting or extracting educational data to support institutional decisions relating the collective of students. However, in Higher Education this perspective is rather connected to learning analytics as data-driven practice, particularly regarding the agenda of educational data mining connected to both learning and academic analytics, the attention on academics’ data literacy and the need of promoting faculty development is less evident in the agenda.
The issue of data literacy to deal with learning analytics has been raised by some authors (Persico & Pozzi, 2015; Wasson, Hansen, & Netteland, 2016). More recently, the project SHEILA (https://sheilaproject.eu/) focused the lack of institutional policies to establish fair practices in learning analytics implementation; however, in one communication the project coordination pointed out the need of develop academics’ data literacy to embrace fair practices (Tsai & Gasevic, 2017). Other areas connected to data literacy in the academic profession relate research. The paradigm of Open Science, which invites citizenship to explore and contribute with greater precision to the data collected by researchers, is generating an opportunity to innovate teaching and learning. The scholarly practice might address new connections between research and teaching through the use of Open Data as Open Educational Resources (Atenas et al., 2015), towards a widespread scientific culture. However, actual practices in Higher Education reveal several issues in implementing these types of innovation (Raffaghelli, 2018a).
The state of art is highlighting an emergent situation that requires more accurate reflections, instruments and interventions.
The authors of this workshop advocate for academics’ reflection and awareness as primary strategy to promote “data activism” as critical positionings aiming at overcoming the passive acceptance of technological innovations taking the form of datafication.
Data activism would represent a great opportunity to contribute to scholars’ professional development research, in terms of agentic practices. In this regard, faculty development to prepare the pedagogies dealing with datafication in the society and across educational as emergent topic require several strands of research that might encounter up to a certain point, but can also run in separate way. The present action Lab is aimed at informing, sharing and discussing the practical applications of this perspective.
In the following, we’ll introduce the action lab goal, contents and activities foreseen.
Action Lab Goal
To promote self-awareness of datafication as problem, providing lens to analyse own professional situation and debate towards the discovery of pathways to support “data-activism” as perspective of professional and institutional development across an open community of scholars.
Action Lab Outline
Concept definition (Datafication in HE) – Datafication in own professional practices (teaching, research, management & planning, etc.) – Pathways for action taking: individual & institutional level, transcending the frontiers of institutional level through an open community of scholars.
- The lab will use post its and posters to build collective representations of the data collected, adopting itself collaborative data visualization strategies.
- The slides and reflections will be shared using open software and tools, particularly ZENODO as Open Research Data platform to gather the final reflections.
- All participants will sign a form to make all data treatment GDPR compliant.
- Concept Definition – Roundtable (who I am, what do I do) – Very brief presentation of the phenomenon and ongoing debate.
- Self-analysis: How much datafied am I? – A moment of reflection on own experiences of datafication. Feelings & Reflections connected to data tracked and shared.
- Collaborative data presentation and discussion -A moment to share own experiences of datafication, compare with colleagues and analyse trends in Higher Education. Expressing concerns, ideas and learning needs relating datafication.
- Pathways for action taking: Building an Open Community – Using the 2×2 graphic representation to place our personal awareness against the institutional attention to the problem.
- Discussion & Conclusions.
- Informed Consent
- SLIDES …nothing new under the sun 😉
- MENTIMETER PRESENTATION (Covering Steps 1-2)
- Digital Copies of paper resources (Covering Steps 3-5)
- ACTION TAKING! The Data Literacy Map (ZENODO resources and community)
Atenas, 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
Dunlap, K., & Piro, J. S. (2016). Diving into data: Developing the capacity for data literacy in teacher education. Cogent Education, 3(1). https://doi.org/10.1080/2331186X.2015.1132526
Hoogland, I., Schildkamp, K., van der Kleij, F., Heitink, M., Kippers, W., Veldkamp, B., & Dijkstra, A. M. (2016). Prerequisites for data-based decision making in the classroom: Research evidence and practical illustrations. Teaching and Teacher Education, 60, 377–386. https://doi.org/10.1016/J.TATE.2016.07.012
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Mandinach, E. B., & Gummer, E. S. (2016). What does it mean for teachers to be data literate: Laying out the skills, knowledge, and dispositions. Teaching and Teacher Education, 60, 366–376. https://doi.org/10.1016/j.tate.2016.07.011
Maybee, C., & Zilinski, L. (2015). Data informed learning: A next phase data literacy framework for higher education. Proceedings of the Association for Information Science and Technology, 52(1), 1–4. https://doi.org/10.1002/pra2.2015.1450520100108
Persico, D., & Pozzi, F. (2015). Informing learning design with learning analytics to improve teacher inquiry. British Journal of Educational Technology, 46(2), 230–248. https://doi.org/10.1111/bjet.12207
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Tsai, Y.-S., & Gasevic, D. (2017). Learning analytics in higher education — challenges and policies. In Proceedings of the Seventh International Learning Analytics & Knowledge Conference on – LAK ’17 (pp. 233–242). New York, New York, USA: ACM Press. https://doi.org/10.1145/3027385.3027400
Wasson, B., Hansen, C., & Netteland, G. (2016). Data Literacy and Use for Learning when using Learning Analytics for Learners. In S. Bull, B. M. Ginon, J. Kay, M. D. Kickmeier-Rust, & M. D. Johnson (Eds.), Learning Analytics for Learners, 2016 workshops at LAK (pp. 38–41). Edimburg: CEUR. Retrieved from http://ceur-ws.org/Vol-1596/paper6.pdf