Literaturnachweis - Detailanzeige
Autor/inn/en | Hoel, Tore; Chen, Weiqin |
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Titel | Privacy-Driven Design of Learning Analytics Applications: Exploring the Design Space of Solutions for Data Sharing and Interoperability |
Quelle | In: Journal of Learning Analytics, 3 (2016) 1, S.139-158 (20 Seiten)Infoseite zur Zeitschrift
PDF als Volltext |
Sprache | englisch |
Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
ISSN | 1929-7750 |
Schlagwörter | Privacy; Data Analysis; Computer Oriented Programs; Systems Development; Models; Information Security; Educational Research; Design |
Abstract | Studies have shown that issues of privacy, control of data, and trust are essential to implementation of learning analytics systems. If these issues are not addressed appropriately, systems will tend to collapse due to a legitimacy crisis, or they will not be implemented in the first place due to resistance from learners, their parents, or their teachers. This paper asks what it means to give priority to privacy in terms of data exchange and application design and offers a conceptual tool, a Learning Analytics Design Space model, to ease the requirement solicitation and design for new learning analytics solutions. The paper argues the case for privacy-driven design as an essential part of learning analytics systems development. A simple model defining a solution as the intersection of an approach, a barrier, and a concern is extended with a process focusing on design justifications to allow for an incremental development of solutions. This research is exploratory in nature, and further validation is needed to prove the usefulness of the Learning Analytics Design Space model. (As Provided). |
Anmerkungen | Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: http://learning-analytics.info/journals/index.php/JLA/ |
Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
Update | 2020/1/01 |