Towards Value-Sensitive Learning Analytics Design
(2018)cite arxiv:1812.08335Comment: The 9th International Learning Analytics & Knowledge Conference (LAK19).DOI: 10.1145/3303772.3303798
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 two methods
of Value Sensitive Design, namely 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 cases, this paper responds to a need of practical means
to support ethical considerations and human values in learning analytics
systems. These two cases demonstrate that Value Sensitive Design could be a
viable approach for balancing a wide range of human values, which tend to
encompass and surpass ethical issues, in learning analytics design.