Learning to learn
“Learning to learn” – the ability to monitor and productively adapt one’s learning process – is a crucial 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 in adequately self-regulating 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 understanding SRL processes during learning. Yet, current approaches lack validity or require extensive analysis after learning.
This research collaboration will research how to advance support given to students by:
- Improving hidden data collection and machine learning techniques to gain better measurement and understanding of SRL processes
- using these new insights to facilitate students’ SRL by providing personalized scaffolds.
Methods
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 a laboratory (experimental study 3 and study 4) and a subsequent field study (field study 5). Our joint expertise in self-regulated learning and learning analytics provides superior opportunities to develop and test more powerful adaptive educational technologies.
