Empowering Teachers to Support Self-Regulated Learning of Primary School Students

Digital learning environments (DLEs) create new opportunities for individualized learning but increase demands on students’ self-regulated learning (SRL) skills. Younger and struggling learners do not always benefit from learning in DLEs because they lack the necessary SRL skills. This prerequisite also engenders inequity for students from disadvantaged socio-economic and academic backgrounds.

DLEs offer numerous opportunities to support SRL, which improves learning outcomes. However, there is a lack research on how specific learner characteristics influence students’ perception and use of digital SRL supports and how this might inform the development of adaptive SRL support designs.

The project DEEP-SRL, a collaboration with PHSZ, investigates how to empower teachers to support SRL skills among primary students. We focus on learning analytics using machine learning to foster SRL assessment and promotion. We aim to build SRL profiles of student behavior from trace data and develop and evaluate an interactive SRL dashboard for teachers. The dashboard will provide real-time feedback and recommendations, enabling teachers to adapt their instructional strategies to meet the diverse needs of their students.

This project will substantially augment the theoretical discourse surrounding primary school students’ SRL in DLE and enlighten the role of teachers and technology in promoting students’ SRL skills. The findings from evaluating interactive SRL analytics will assist teachers in tailoring their SRL support to specific learning contexts and the diverse needs of their students. This knowledge can inform teacher education and training.

More information: Website of the deep consortium