Problem-Based Learning and Structured Collaboration in a machine learning class [Problembasiertes Lernen mit strukturierter Gruppenarbeit im Kurs Maschinelles Lernen]
|Duration||01/2020 - 01/2021|
|Funded by||Stifterverband, Fellowship für Innovationen in der digitalen Hochschullehre NRW|
|Researchers||Prof. Dr. Nikol Rummel, Sebastian Strauß|
|Partners||Prof. Dr. Laurenz Wiskott, Zahra Fayyaz, Robin Schiewer, Merlin Schüler (Theory of Neural Systems, Ruhr-Universität Bochum)|
In this project, we implement computer-supported problem-based learning (PBL) in a class on "Machine Learning: Unsupervised Methods". This didactical setting aims to achieve the following learning goals: (1) reduce the dropout rate from the course, (2) engage the students in active knowledge construction, (3) enable the students to flexibly apply their knowledge to authentic machine learning problems and reflect their problem solving process, (4) teach students how to engage in productive collaboration in small groups.
The course contains instructional support for individual learning as well as for the collaboration among students. For example, the course material will offer opportunities for microlearning and self-assessments. To foster effective collaboration among the students, the groups receive a group awareness tool and a collaboration script during their collaborative problem solving process.