ALICE - Analyzing Learning for Individualized Competence development in mathematics and science Education
In the ALICE project, students' learning during mathematics and science lessons is evaluated on the basis of data from students' interaction with digital technologies. Learning paths that correspond to the development of competencies is reconstructed and identified. A theoretical and methodological basis for improved individualization of instruction is being created to help students achieve their educational goals and develop mathematical and scientific competencies.
The coronavirus crisis has created an urgent need to support students' learning through digital technologies. This need underscores the importance of efforts in the education sector to take advantage of the unique opportunities to support learning based on digital technologies and the analysis of digital data. Far beyond the use of digital platforms to distribute assignments to students, digital technologies make it possible to track the learning of individual students' learning and provide targeted support tailored to the individual needs of each student. Greater individualization of learning is advocated as a means to support all students in developing the skills needed for professional, social and cultural participation - especially in such in such important areas as mathematics and science.
However, individualized learning, also known as personalized and adaptive learning, requires continuous assessment of student learning, reconstruction of learning pathways and extrapolation of these pathways in terms of student competency development. This requires a theory of learning and a model of competency development. In addition, methods that allow for the continuous assessment of student learning across a range of learning activities and the subsequent mapping to student competency development are also needed.
As digital technologies become increasingly ubiquitous in math and science classrooms, they can 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. Automatic analysis of these interactions allows for timely assessment of individual student performance.
The project investigates the extent to which data derived from students' interactions with digital technologies in mathematics and science classrooms can be used to 1) continuously evaluate individual students' learning, 2) reconstruct learning paths across sequences of learning activities and 3) identify those paths that are consistent with the development of competencies in mathematics and science. In this way, the project aims to provide the theoretical and methodological basis for greater individualization of mathematics and science instruction in order to help all students develop the competencies necessary for social, cultural and occupational participation and thus achieve educational goals.
Funding: Leibniz Collaborative Excellence
Duration: 04/2021 - 03/2024
Project Team: Sebastian Gombert
Contact: Hendrik Drachsler