Learning analytics methodology for MOOCs

Few days ago I finished the deliverable of EMMA project about learning analytics methodology for MOOCs together with colleagues from OuNL, Unina, IPSOS, Atos. It would be fair to say that our deliverable is rather proposal of the methodology, because learning analytics application will be piloted in September and after the first evaluation adjustments will be made.

Learning analytics in EMMA project will focus on: a) real-time analytics through learning analytics dashboards for teachers and students; b) retrospective analysis of the digital traces in EMMA platform. First approach aims to support participants’ learning activities whereas the second approach is intended for more in-depth analysis of the MOOCs and overall EMMA evaluation. As EMMA is a MOOC platform then calculating the dropout and clustering the participants will be one of the research aims. Additionally uptake of the knowledge, students’ progress and social structures emerging from MOOCs will be analyzed in the pilot phase.

Theoretically we have relied on the work of  Greller and Drachsler (2012). They give a general framework for learning analytics and offer focusing attention to six critical dimensions within the research lens. According to the framework, each of the dimensions can have several values and it can be extended upon a need. Represented dimensions are: stakeholders, objectives, data, instruments, external constraints and internal constraints.

Different studies about MOOCs and learning analytics were investigated for the deliverable. Most of them focused on clustering the participants and calculating the drop-out rate. EMMA learning analytics approach takes the retention rates and clusters of the users into account, because dropout is important, but will redefine drop-out in the context of a MOOC while in addition considering the concept of drop-in. Such clustering enables to approach the participants more personally by taking the different types of users and their personal learning objectives into account. This is accomplished by making use of a variety of both qualitative and quantitative analyses.

In pilot phase of the EMMA MOOCs following aspects will be analyzed:

  • Clustering of the participants
  • Progress and performance
  • Uptake of knowledge
  • Social structures
  • Engagement with the content

Technical architecture consists of tracking tool in EMMA, learning record store (Learning Locker) and dashboard module. xAPI standard will be used when storing learning experiences as it offers good opportunities for the personalized advice foreseen in EMMA. The context (social ties, groups, activity duration) and also semantics and used tags are also part of the tracked learning activities in EMMA analytics for conducting more in-depth analysis and provide meaningful dashboards.

EMMA’s learning analytics application is an advanced solution in learning analytics field for MOOCs since it makes a combination of the xAPI specification and the Learning Record Store (LRS) Learning Locker for storing and sharing the learning experiences that is not widely in common by MOOC platforms. In particular because the dashboards for students and instructors that will be developed are based on the collected and analyzed events in EMMA platform and are geared towards the specific conditions that apply to MOOC settings. Moreover, these dashboards do not only provide feedback about the courses and learning activities, but also offer reflection and monitoring opportunities in support of the personalized learning objectives of the students.

EMMA learning analytics approach will be introduced in Ec-tel 2014 conference in two different workshops. Firstly in MOOCs workshop and in learning analytics workshop.


Greller, W., & Drachsler, H. (2012). Translating Learning into Numbers: A Generic Framework for Learning Analytics. Educational Technology & Society, 15 (3), 42–57.