Scaffolded Learning Assignments in University Machine Learning Education
A study into the effectiveness of assignment scaffolding
M.V. van der Weide (TU Delft - Electrical Engineering, Mathematics and Computer Science)
M.A. Migut – Mentor (TU Delft - Web Information Systems)
Mark A. Neerincx – Graduation committee member (TU Delft - Interactive Intelligence)
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Abstract
This study investigates the impact of scaffolded assignments on student learning, confidence, and the development of an empirical mindset in a Machine Learning (ML) course at TU Delft. Unlike traditional Computer Science subjects, ML requires an experimental approach, challenging students used to design-first methodologies. Through surveys of 25 students from the CSE2510 course, the study found that scaffolded assignments significantly enhance student confidence and perceived learning benefits, despite no positive correlation between the number of assignments completed and course grades. Qualitative feedback highlighted the value of scaffolded assignments in understanding the ML design process by providing structured guidance and enabling practice of specific sub-tasks. These findings suggest scaffolded learning is crucial in developing an experimental mindset and boosting student confidence in ML education. This study also proposes a second methodology for future research during an edition of the course to further explore this topic.