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A User Focused Approach to Developing a Digital Study Assistant through a Mixed Methods Design



In: Ifenthaler, D., Sampson, D.G. & Isaías, P. (Hrsg.) (2022). Open and Inclusive Educational Practice in the Digital World. Cham: Springer (Cognition and Exploratory Learning in the Digital Age), S. 83-99.


Katharina Schurz, Johannes Schrumpf, Felix Weber, Maren Lübcke, Funda Seyfeli-Özhizalan, Klaus Wannemacher


Digital study assistants (DSA) aim to support individual learning processes by designing them appropriately and efficiently based on recommendations. In this book chapter, we present a prototype of a DSA for the students of three German universities. The digital data-driven DSA is integrated into the local learning management system and consists of recommender modules with a certain kind of recommendation for a specific purpose, e.g., recommending academic contacts that fit an expressed academic interest. The modules implemented so far use a wide range of methods: classic rule-based artificial intelligence (AI) or neural networks that can detect complex features and patterns in large datasets. To evaluate the current prototype of the DSA, we used a mixed method design approach with concurrently collected user data and qualitative data. A first insight into the user data suggests that recommender modules providing personalized recommendations are more likely to be used by students. A focus group discussion with students confirmed these findings with the suggestion to make the DSA more personal, individual, interactive, supportive, and user-friendly. In conclusion, we present ideas for the further development of the prototype based on these findings.

Das Kapitel ist hier zu finden, die gesamte Ausgabe hier.