Digital differentiation matrix

How can universities provide individualized teaching even in large groups?

A differentiation matrix is a didactic concept for use in individualized courses (and, of course, individualized school lessons). With this concept and the accompanying digital tool, courses in any subject can be designed to better match the learning opportunities available with the learning requirements of the students.

To teach with a differentiation matrix, the teacher first translates the learning objectives of a subject area into differentiated learning opportunities such as materials or tasks. In the digital tool, these learning opportunities of varying complexity are then arranged in the matrix, a grid of rows and columns. This shows students the degree of complexity of each learning opportunity (e.g., each task), and they can select those that match their prior knowledge.

Students are also given the opportunity to deepen their understanding of individual topics and receive personalized learning diagnostics through automated feedback: they can independently (re)activate, review, and expand their prior knowledge. For the target group of teacher training students, professionalization goals in the areas of “dealing with heterogeneity and inclusion” and “digitalization” can also be pursued: Teaching students using methods that they can later apply as teachers is an effective practical exercise in designing teaching and learning.

In this project, the didactic method and, to date, a plugin for the Moodle learning management system have been developed and evaluated in a control group design. Further technical development is planned, which will make the digital tool accessible beyond Moodle. 

Head: Prof. Dr. Julia Dietrich

Cooperation partners: Dr. Franziska Greiner (Leipzig), Dorit Weber-Liel, Dr. Nicole Kämpfe, and Prof. Dr. Bärbel Kracke (Jena)

Funding: Stifterverband and Thuringian Ministry of Economics, Science, and Digital Society – Fellowship for Innovationen in der digitalen Hochschullehre

Duration: 2019-2020

Publications:

Dietrich, J., Greiner, F., Weber-Liel, D., Berweger, B., Kämpfe, N., & Kracke, B. (2021). Does an individualized learning design improve university student online learning? A randomized field experiment. Computers in Human Behavior, 122, 106819. https://doi.org/10.1016/j.chb.2021.106819

Greiner, F., Kämpfe, N., Weber-Liel, D., Kracke, B., & Dietrich, J. (2019). Flexibles Lernen in der Hochschule mit Digitalen Differenzierungsmatrizen. Zeitschrift für Hochschulentwicklung, 14, 287-302. https://doi.org/10.3217/zfhe-14-03/17