Eliciting students' preferences for the use of their data for learning analytics: A crowdsourcing approach

Korir, Maina; Slade, Sharon; Holmes, Wayne and Rienties, Bart (2022). Eliciting students' preferences for the use of their data for learning analytics: A crowdsourcing approach. In: Rienties, Bart; Hampel, Regine; Scanlon, Eileen and Whitelock, Denise eds. Open World Learning: Research, Innovation and the Challenges of High-Quality Education. Routledge Research in Digital Education and Educational Technology. New York, USA: Routledge, pp. 144–156.

DOI: https://doi.org/10.4324/9781003177098-13

Abstract

Research on student perspectives of learning analytics suggests that students are generally unaware of the collection and use of their data by their learning institutions, and they are often not involved in decisions about whether and how their data are used. To determine the influence of risks and benefits awareness on students’ data use preferences for learning analytics, we designed two interventions: one describing the possible privacy risks of data use for learning analytics and the second describing the possible benefits. These interventions were distributed amongst 447 participants recruited using a crowdsourcing platform. Participants were randomly assigned to one of three experimental groups – risks, benefits, and risks and benefits – and received the corresponding intervention(s). Participants in the control group received a learning analytics dashboard (as did participants in the experimental conditions). Participants’ indicated the motivation for their data use preferences. Chapter 11 will discuss the implications of our findings in relation to how to better support learning institutions in being more transparent with students about the practice of learning analytics.

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