This project addresses important aspects of our learning ecology in Europe that are largely ignored in the current developments of adaptive educational technologies. The goal is to develop measurements of students’ cognition, metacognition, emotion and motivation during learning in order to support the development of more powerful adaptive educational technologies. Novel personalised support for learners to support self-regulated learning can be developed based on trace and multimodality data measures of students’ states during learning. Moreover, tools can also be developed for teachers to enhance their instructional decision making and ability to support learners’ individual differences.
Thus, we aim to make a significant contribution to the current debate about how learning analytics and multimodality data can improve education discussing:
- Application in education: visualisation and recommendation services
- Optimisation of adaptive education technologies
This demands for a concerted interdisciplinary dialogue combining findings from psychology and educational sciences with advances in computer sciences and artificial intelligence. The participants in this E-CIR are leading international researchers who have articulated different emerging perspectives and methodologies to measure cognition, metacognition, motivation and emotions during learning. The participants recognize the need for intensive collaboration to accelerate progress with new interdisciplinary methods to develop more powerful adaptive educational technologies.