ALICE: Analyzing Learning for Individual Competence development in mathematics and science Education
| Workgroup | Knowledge Construction Lab |
| Duration | 05/2021-04/2024 |
| Funding | Leibniz-Competition |
Project description
The coronavirus crisis has once again demonstrated the importance of supporting learning in schools through digital technologies. Far beyond the use of digital platforms to distribute assignments to students, digital technologies enable the tracking of individual student learning and the provision of targeted support tailored to individual needs. This research project investigates the extent to which data derived from student interactions with digital technologies in mathematics and science classrooms can be used to 1) continuously evaluate individual student learning, 2) reconstruct learning pathways across sequences of learning activities, and 3) identify those pathways associated with the development of competencies in mathematics and science.
Greater individualization of learning is necessary to help all students develop the competencies needed for career, social, and cultural participation-particularly in such critical areas as mathematics and science. However, individualized learning, also known as personalized and adaptive learning, requires the continuous assessment of students' learning, the reconstruction of their learning trajectories, and the extrapolation of those trajectories in terms of their skill development. This requires a theory of learning, and based on it, a model of competence development and methods that allow for continuous assessment of learning across a range of learning activities and subsequent mapping to learners' competence development.
As digital technologies become increasingly ubiquitous in math and science classrooms, they lend themselves to the development of such a methodology. As students work with digital technologies, their interactions with these technologies can be recorded and automatically analyzed. Automated analysis of these interactions enables timely assessment of performance.
Cooperations
Prof. Dr. Knut Neumann (IPN Kiel)
Prof. Dr. Hendrik Drachsler (DIPF Frankfurt)
Prof. Dr. Nikol Rummel (Ruhr-University Bochum)
