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Towards Value-Sensitive Learning Analytics Design

Towards Value-Sensitive Learning Analytics Design

Abstract

To support ethical considerations and system integrity in learning analytics, this paper introduces two cases of applying the Value Sensitive Design methodology to learning analytics design. The first study applied Value Sensitive Design methods, specifically stakeholder analysis and value analysis, to a conceptual investigation of an existing learning analytics tool. This investigation uncovered a number of values and value tensions, leading to design trade-offs to be considered in future tool refinements. The second study holistically applied Value Sensitive Design to the design of a recommendation system for the Wikipedia WikiProjects. To proactively consider values among stakeholders, we derived a multi-stage design process that included literature analysis, empirical investigations, prototype development, community engagement, iterative testing and refinement, and continuous evaluation. By reporting on these two studies, this paper responds to a need of practical means to support ethical considerations and human values in learning analytics systems. These two studies demonstrate that Value Sensitive Design could be a viable approach for balancing a wide range of human values—which encompass and go beyond ethical issues—in learning analytics design.

Publication
Proceedings of LAK ‘19
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Bodong Chen
Assistant Professor