Open Learning Analytics
The main objective for Learning Analytics is to unveil so far hidden information out of the educational data to gain new insights and prepare those for the different educational stakeholders (learners, teachers, parents, and managers).
​This new kind of information can support individual learning, enhance teaching quality, but also improve organisational knowledge management processes and system administration. It involves various pre-processing steps from basic information models to structure data such as data harvesting, storing, cleaning, anonymisation, analysis, mining and visualisation. Open Data movements such as Linked Data provide sematic structures that are important for those pre-processing steps. It contributes to make the results of Learning Analytics research and big data initiatives more comparable and contributes to a knowledge base about the effects of Learning Analytics in K12, Higher Education and the corporate sector.
Dr. Drachsler, Hendrik


In the last decade the amount of user and usage data and the development of open and linked data sources has created new challenges for society. Learning Analytics (LA) is a research field that aims at understanding the potential and limitations of big data for learning support. Despite the great enthusiasm currently surrounding LA, there are substantial questions for research. Along with technical research questions such as the scalability of the infrastructure, compatibility of educational datasets, the comparability and adequacy of algorithmic, and appropriate visualisation technologies, there are also other problem areas that influence the acceptance and the impact of LA. Among these are questions of data ownership, transparency  of the analytic process, ethical use and dangers of abuse as well as the demand for new key competences to interpret and act on LA’s results and the need for LA supported instruction methods.

Data science in education has been coined ‘Learning Analytics’, an umbrella term for research questions from overlapping research domains such as educational science, computer and data science. The use of data to inform decision-making in education and training is not new but the scope and scale of its potential impact for teaching and learning has increased by orders of magnitude over the last few years. We are now at a stage where data can be automatically harvested at previously unimagined levels of granularity and variety. Analysis of these data has the potential to provide evidence-based insights into learner abilities and patterns of behaviour that in turn can provide crucial insights to guide curriculum design, delivery to improve outcomes for all learners, change assessment from mainly summative to more formative assessments, and thus contribute to national and European economic and social well-being.

Although the great success surrounding Learning Analytics and various (startup) companies that jumped on the bandwagon and provided learning analytic tools, most learning analytics strategies of educational organisations are still at the initial phases. Considering the five step sophistication model (see Figure 2) developed by the Society of Learning Analytics Research (SoLAR) (Siemens, et al., 2013) there is still a lot of work to be done in order to transform the educational sector to a data-driven educational science. We therefore conduct research according to the Learning Analytics Framework by Greller & Drachsler (2012) and follow the following research questions. 

Leading research questions

  • What kind of data models are most supportive for educational research and practice?
  • How can LA data be stored and used to create valuable/useful tools for educational stakeholders (teachers, students, parents, managers)?
  • How can the stakeholders be supported with personalised information based on the LA data?
  • How can existing ethics and privacy guidelines be applied for the uptake of LA in Europe?
  • Which additional competences are needed for educational stakeholders (teachers, students, parents, managers) to deal with the affordance of LA tools?
  • How can LA information be used with existing Instructional Design Methods?


  • LACE: Learning Analytics Community Exchange
    European project (7th Framework Programme)
    The project aims to create a community of people interested in learning analytics and educational data mining. This community should build bridges between research, policy and practice to realise the potential of learning analytics and educational data mining in Europe. 
  • LinkedUP
    European project (7th Framework Programme)
    The project aims to push forward the exploitation of public, open data available on the Web, in particular for education. In order to do that, LinkedUp will organise a LinkedUp Challenge: the project challenges the educational world to realise personalised university degree-level education of global impact based on open Web data and information.
  • SIG dataTEL
    Data-driven Research and Learning Analytics
    SIG dataTEL aims to increasing research on educational datasets as it is expected that this will create more transparent, mutually comparable, trusted and repeatable experiments that lead to evidence-driven knowledge with approved indicators for theories in education and in particularly in TEL.
  • SURF SIG Learning Analytics
    Learning analytics betreft het verzamelen, analyseren en interpreteren van data over studenten in het onderwijs. De verzamelde data kunnen worden ingezet ter verbetering van het onderwijs.
  • Open Discovery Space
    European project (7th Framework Programme)
The project aims to develop a socially-powered and multilingual open learning infrastructure to boost the adoption of eLearning resources. The interface has been designed with students, teachers, parents and policy makers in mind.
  • EU ICT-PSP - ECO - MOOC around Open Educational Resources
    ECO is an European project based on Open Educational Resources (OER) that gives free access to a list of MOOC (Massive Open Online Courses) in 6 languages.
    If you make part of this project, you will have also the opportunity to create your own MOOC. The main goal of this project is to broaden access to education and to improve the quality and cost-effectiveness of teaching and learning in Europe.


Key publications:

  • Drachsler, H. & Greller, W. (2016, 25-29 April). Privacy and Analytics – it’s a DELICATE issue. A Checklist to establish trusted Learning Analytics. 6th Learning Analytics and Knowledge Conference 2016, Edinburgh, UK.
  • Drachsler, H., Verbert, K., Santos, O. C., & Manouselis, N. (2015). Panorama of Recommender Systems to Support Learning. In F. Rici, L. Rokach, & B. Shapira (Eds.), 2nd Handbook on Recommender Systems (pp. 421- 451). Springer, US.
  • Tabuenca, B., Kalz, M., Drachsler, H., & Specht, M. (2015). Time will tell: The role of mobile learning analytics in self-regulated learning. Computers & Education, 89, 53–74.
  • Drachsler, H., Stoyanov, S., & Specht, M. (2014, March). The Impact of Learning Analytics on the Dutch Education System. Presentation given at The 4th International Conference on Learning Analytics and Knowledge, Indianapolis, Indiana, USA.
  • Scheffel, M., Drachsler, H., Stoyanov, S., & Specht, M. (2014). Quality indicators for learning analytics. Educational Technology & Society, 17(4), 117–132.
  • Greller, W., & Drachsler, H. (2012). Translating Learning into Numbers: A Generic Framework for Learning Analytics. Educational Technology & Society, 15(3), 42–57.
  • Florian, B., Glahn, C., Drachsler, H., Specht, M., & Fabregat, R. (2011). Activity-based learner-models for Learner Monitoring and Recommendations in Moodle. In C. D. Kloos, D. Gillet, R. M. Crespo Carcía, F. Wild, & M. Wolpers (Eds.), Towards Ubiquitous Learning: 6th European Conference on Technology Anhanced Learning, EC-TEL 2011 (pp. 111-124). September, 20-23, 2011, Palermo, Italy. LNCS 6964; Heidelberg, Berlin: Springer.
  • Verbert, K., Manouselis, N., Drachsler, H., & Duval, E. (2012). Dataset-Driven Research to Support Learning and Knowledge Analytics. Educational Technology & Society, 15(3), 133–148.
  • Drachsler, H., Hummel, H. G. K., Van den Berg, B., Eshuis, J., Waterink, W., Nadolski, R. J., Berlanga, A. J., Boers, N., & Koper, R. (2009). Effects of the ISIS Recommender System for navigation support in self-organised Learning Net-works. Journal of Educational Technology and Society, 12(3), 122-135.