Learning to learn” – the ability to monitor and productively adapt one’s learning process – is a key competence formulated by the European Parliament (2006) and increasingly a central focus of education. Prior research has shown that self-regulated learning (SRL) leads to better learning performance but students often experience difficulties to adequately self-regulate their learning. Instructional scaffolds are a successful method to help learners and consequently improve learning outcomes.

Learning analytics and machine learning offer an approach to better understand SRL-processes during learning. Yet, current approaches lack validity or require extensive analysis after the learning process.

This research collaboration will research how to advance support given to students by: i) improving unobtrusive data collection and machine learning techniques to gain better measurement and understanding of SRL-processes and ii) using these new insights to facilitate student’s SRL by providing personalized scaffolds.

We will reach this goal by investigating and improving trace data in exploratory studies (exploratory study 1 and study 2) and using the insight gained from these studies to develop and test personalized scaffolds based on individual learning processes in laboratory (experimental study 3 and study 4) and a subsequent field study (field study 5). Our joint expertise in the fields of self-regulated learning and learning analytics provide superior opportunities to develop and test more powerful adaptive educational technologies.

FLoRA is funded by DFG, ESRC and NWO as part of the Open Research Area (Call 5) under grant number BA 2044/10-1 | GA 2739/1-1 | MO 2698/1-1.

The project is a collaboration between:

  1. University of Edinburgh, Edinburgh, UK.
  2. Technical University of Munich, Germany.
  3. Behavioural Science Institute, Radboud University, The Netherlands.
  4. Monash University, Melbourne, Australia