Towards Value-Sensitive Learning Analytics Design
This work addresses the need for practical methods to incorporate ethics and human values in learning analytics, though it is incremental as it applies an existing methodology to new contexts.
The paper tackles the challenge of integrating ethical considerations into learning analytics by applying Value Sensitive Design in two cases: analyzing an existing tool to identify value tensions and designing a Wikipedia recommendation system through a multi-stage process. It demonstrates that this approach can balance human values in system design.
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.