The Coronavirus Crisis has spurred an urgent need to support students’ learning via digital technologies. Digital technologies can also adapt to learners’ knowledge and skills and can adaptively provide them with learning materials and instruction that are tailored to their competence. However, this requires that the digital learning environment is able to model learners’ understanding and performance during the learning process and make predictions about each individual learners’ potential progress during the learning activity.
This project aims at establishing theoretical and methodological foundations for providing learners with adaptive support during mathematics and science education. To this end, the project combines four research strands, that are
1) developing digital learning materials that is based on learning-progressions in mathematics, biology, chemistry or physics.
2) Collecting authentic data from students who are engaging with this learning material in order to develop predictor models of how learners’ competence develops over time.
3) Reconstructing learners’ learning trajectories, and finally
4) investigating the effectiveness of different learning trajectories and developed instructional support that helps learners achieve their learning goals.