About
Self-regulation is an invisible demanding mental skill that individuals must acquire to effectively function (cognitively, socially, emotionally) in the world. The problem is that the processes at the foundation of this regulation are invisible and thus very challenging to understand, support, and influence. While self-regulation of learning (SRL) is difficult for individuals, it is even more so when interacting with peers and in teams; co-regulation (CoRL) and socially shared regulation (SSRL) respectively. CLEVER aims to identify and support the regulation of complex learning process in groups in such a way that the individuals concomitantly also acquire those skills.
Trigger moments |
To do this, CLEVER targets trigger regulation moments in collaboration by analyzing input from various multimodal sources (i.e., 360°video, log data, heart rate, skin conductivity, self-report) making invisible mental cognitive, motivational, and emotional learning processes visible to both learners and teachers to allow them to regulate those processes to regulate them better and learn more efficiently.
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Ecologically valid setting |
CLEVER conducts exploratory and intervention research in ecologically valid settings following the temporal collaboration progress of 16-18 year old high school students (N=200) working in advanced physics who are supported by dashboards designed to prompt SRL, CoRL, and SSRL.
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Multimodal data |
It implements temporal data analysis and triangulation of multimodal data to gain insight in challenging trigger regulation moments and changes in learning activities. CLEVER uses educational data mining and learning analytics to transform the data into meaningful learning patterns and visualize them via dashboards.
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