The International Data Science & Social Research (DSSR) Conference was held in Naples from 17 to 19 February 2016. The main aim of the conference was to discuss the Data Science through interdisciplinary lenses, its challenges and opportunities – especially stressing the importance of the dialogue between the disciplines and negotiation of a common language of understanding.
One of the panel sessions organised was “Big Data in the Field of Education: Learning Analytics Paradigms and Applications” organised jointly by the EMMA and Federica.eu Projects, partners of the Conference). The EMMA team was present in various sessions – starting from an in-depth description of the learning analytics offered on the EMMA platform, discussed by Maka Eradze (UNINA/Tallinn University) and Kairit Tammets (Tallinn University). The paper “Learning Analytics for MOOCs: The EMMA Case” gave a state-of-the-art review of learning analytics, focusing on learning analytics in different MOOC platforms. The paper gave an overview of the EMMA project and its integration of a learning analytics system, initial results were also given. It stressed the importance of a theory-driven approach in learning analytics. Dashboard visualisations were presented and possibilities of how to use EMMA learning analytics dashboard views for sense making and reflection of the MOOCs and MOOC experience were discussed.
Big questions were raised in the paper by Rosanna de Rosa (UNINA) and Chiara Ferrari (IPSOS): “Governing by data: some considerations on the role of learning analytics in education”. This introductory paper aimed at exploring the social and political role that educational data and learning analytics will play in the near future and their implications in terms of social and economic impact. Investigating the impact of such algorithms on the teacher and student experience means to identify not only strengths and weaknesses of analytics tools and practices, but also figuring out their role for social control and/or cultural growth of a society as a whole, and to imagine the future of education as an industry. The presentation can be found here.
Mart Laanpere (Tallinn University) presented how learning analytics are used in Estonia and summarised the lessons learned from the Estonian OER initiatives and how their initial failure led the research group to design a pedagogical and technological framework for the next-generation brokerage platform for digital learning resources: eSchoolbag. With the focus on the issues of data heterogeneity, increasingly distributed nature of learning, this paper also discussed the importance of theory-driven research and practice. It also introduced the learning analytics solution that is currently being built into the platform and illustrate it with some typical usage scenarios. Find the presentation here.
Francis Brouns from the Open University Netherlands presented a paper covering two European projects that partly share a learning analytics approach. In the paper “Value of learning analytics in MOOCs” (Francis Brouns, Marco Kalz, Olga Firssova) critical questions were raised, such as determining what constitutes progress and performance, in particular because teachers in MOOCs can’t assess learners’ products at individual level. The paper presented the authors’ ideas on how learning analytics can be of assistance based on their experiences in the ECO and EMMA MOOC projects. Here models has been developed for learning and knowledge uptake and metrics have been defined that take into account the opportunities and features of the respective MOOC platforms.The presentation can be found here.
During the plenary session Susan Halford from the University of Southampton discussed in her presentation “Knowing the social world in the data revolution” why data science is necessary and why big data should be seen as an inter disciplinary field and not only from the perspective of IT or maths. Further on she highlighted the importance of a underlying social framework and that ethical aspects should be taken into account.
All the presented papers shared the general spirit of the Data Science and Social Research conference in raising questions on the policy, theory and practice of Learning Analytics and Big Data. The issues as the importance of theory-driven learning analytics, ethical and privacy considerations, paradigm shift in research and socio-technical landscape were also discussed.