A Web-Based Recommendation System for Higher Education: SIDDATA

History, Architecture and Future of a Digital Data-Driven Study Assistant

Authors

DOI:

https://doi.org/10.3991/ijet.v17i22.31887

Keywords:

Digital Student Assistant, E-learning, Artificial Intelligence, Innovative Learning Technologies

Abstract


The SIDDATA data-driven digital study assistant offers students various services that help them identify and achieve their personal study goals. The software’s features and infrastructure have evolved to become a universal platform for interactive self-regulated learning and digital study planning throughout three annual software development cycle iterations. The software is fully integrated into an existing learning management system (Stud.IP) and has been tested by more than 3000 students from three German universities during the last three years. This paper presents the SIDDATA software architecture, its design philosophy, and its modular, feature-centered application logic. Developed during a third-party funded research project with limited temporal scope, the web-based software is publicly available under an MIT license. We conclude with application opportunities for researchers, developers, educators, and higher education institutions.

Author Biographies

Felix Weber, University of Osnabrück

Felix Weber ([email protected]) is a research fellow at the Center for Digital Teaching, Campus Management and Higher Education Didactics at Osnabrueck University and a Ph.d. candidate at the Institute of Cognitive Science at Osnabrueck University. His research focuses on the human-machine interaction with AI-powered data-driven assistants and suitable algorithms and technologies. He is interested in building research cooperations on topics related to digital assistants for the individualization of studies in higher education.

Johannes Schrumpf, University of Osnabrück

Johannes Schrumpf ([email protected]) is a researcher at the Institute of Cognitive Science at Osnabrueck University and a Ph.d. candidate for Cognitive Science in the domain of Artificial Intelligence. In his research, he explores the capabilities of natural language processing systems to extract relevant features from educational resource descriptions for recommendation systems.

Tobias Thelen, University of Osnabrück

Tobias Thelen ([email protected]) is deput managing director of the Center for Digital Teaching, Campus Management and Higher Education Didactics at Osnabrueck University and member of the Artificial Intelligence group at the university's Institute of Cognitive Science. He is working on the joint development of Open Source software for higher education and is interested in fostering development cooperation between higher education institutions. Tobias' research focusses on recommender systems for learning and analyser component for adaptive learning systems.

Cover iJET Vol. 17 No. 22 (2022)

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Published

2022-11-28

How to Cite

Weber, F., Schrumpf, J., Dettmer, N., & Thelen, T. (2022). A Web-Based Recommendation System for Higher Education: SIDDATA: History, Architecture and Future of a Digital Data-Driven Study Assistant . International Journal of Emerging Technologies in Learning (iJET), 17(22), pp. 246–254. https://doi.org/10.3991/ijet.v17i22.31887

Issue

Vol. 17 No. 22 (2022)

Section

Short Papers

License

Copyright (c) 2022 Felix Weber, Johannes Schrumpf, Niklas Dettmer, Tobias Thelen

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.