Data analysis in EMMA: Learning about Learners, Learning Needs, Learning Uptake

by Chiara Ferrari, IPSOS, Italy and Eleonora Pantò, CSP – Innovazione nelle ICT S.C.A R.L, Italy

Eleonora Panto

Chiara FerrariMOOCs are currently a hot topic in the debate inflaming education communities and they are also the source of adversarial opinions as to their value, their contribution to teaching and learning and their ability to meet learners’ evolving learning needs. Formal data analysis of MOOCs has been underway for quite some time, and it has taken various approaches and often quite different forms, producing a wealth of outputs and reports. Such outputs can point to conflicting conclusions but mostly they concur that MOOCs represent a healthy injection of disruption in current models of Higher Education. MOOCs may well represent a unique opportunity for smaller institutions despite the fact that many hesitate to engage in them, due to a lack of perceived capacity and/or opportunity.
This opportunity can be seized by joining the EMMA pilot – the first European Multiple MOOCs Aggregator whose programme of data analysis is a powerful combination of various methodologies which will deliver useful insights as to how to make the most out of the new platform.
Current and potential EMMA partners can avail of a full set of tools including:

  • a tracking tool created by CSP
  • a learning analytics model customized to EMMA functions developed by Tallin University along with a teacher/student dashboard
  • a socio-demographic profiling possibility collected within the platform registration procedure and a full set of data on expectations and needs collected via a questionnaire designed by Ipsos. The platform interaction experience and the learning experience will also be evaluated by the users, via an ad hoc set of questions developed by Ipsos

The combination of this significant range of data will allow EMMA partners to promote various and diverse actions, some of which are of operational nature (e.g. to adapt the platform to users’ needs, to fine-tune MOOC material to learners’ requests, …), others are of a more cognitive nature: the objective is to promote a better knowledge of learners based on the different ways in which they interact with EMMA.

Here are some examples of the questions the data analysis will enable the partners to answer, they are presented in random order and relate to very diverse areas of exploration:

  • In what categories / clusters do EMMA learners fit; are there some especially interesting sub-populations, which might drive future MOOC design?
  • How does the level of engagement in a MOOC change in relation to the presence of certain types of materials / activities?
  • Does the unique multilingual approach, typical of EMMA, represent an asset for learners?
  • Does the innovative building blocks feature offered by EMMA represent an opportunity?
  • Are there specific variables which can be used as predictors of success for a MOOC?

More to come, of course, along with the evolution and growth of EMMA and with the first insights which will be captured and disseminated early next year.

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