Beyond Context-Sensitive Solutions: Using Value-Sensitive Design to Identify Needed Transit Information Tools

Watkins, Kari; Ferris, Brian; Malinovskiy, Yegor; Borning, Alan

doi:10.1061/9780784413210.026

Cited by 5 publications

(1 citation statement)

References 7 publications

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“…Fourteen different methods can be drawn to support Value Sensitive Design [15]. For example, direct and indirect stakeholder analysis can lead to the identification of indirect stakeholders of transit information tools [38]; fictional value scenarios are useful for revealing values and tensions when developing mobile apps [7]; value dams and value flows could be a useful analytic method for resolving value tensions among design choices-by removing a design choice based on strong objection from a small percentage of stakeholders (the value dams) or accepting a design option based on favorable reaction from a certain portion of stakeholders (the value flow) [7]. These methods suit varied purposes of design and a project adopting Value Sensitive Design can draw from multiple methods.…”

Section: Value Sensitive Designmentioning

confidence: 99%

Towards Value-Sensitive Learning Analytics Design

Chen

Zhu

2019

Proceedings of the 9th International Conference on Learning Analytics &Amp; Knowledge

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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.

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