Title:	TOWARDS A USER FOCUSED DEVELOPMENT OF A DIGITAL STUDY ASSISTANT THROUGH A MIXED METHODS DESIGN
Author(s):	Katharina Schurz, Johannes Schrumpf, Felix Weber, Maren Lübcke, Funda Seyfeli and Klaus Wannemacher
ISBN:	978-989-8704-33-7
Editors:	Demetrios G. Sampson, Dirk Ifenthaler and Pedro Isaías
Year:	2021
Edition:	Single
Type:	Full
First Page:	45
Last Page:	52
Language:	English
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Full Contents:	click to dowload
Paper Abstract:	Digital Study Assistants (DSA) aim to support individual learning processes by designing them appropriately and efficiently based on recommendations. In this paper we present a prototype of a DSA for students in higher education 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 data sets. To evaluate the current prototype of the DSA we used a mixed methods design approach with concurrently collected user data and qualitative data. A first insight in 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.
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