Supporting the shift to digital with student-centered learning analytics
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Supporting the shift to digital with student-centered learning analytics Xavier Ochoa1 · Alyssa Friend Wise1 Accepted: 7 November 2020 © Association for Educational Communications and Technology 2020
Abstract This paper is in response to the manuscript entitled “Student perceptions of privacy principles for learning analytics” (Ifenthaler and Schumacher, Student perceptions of privacy principles for learning analytics. Educational Technology Research and Development, 64(5), 923–938, 2016) from a practice perspective. Learning analytics (the use of data science methods to generate actionable educational insights) have great potential to impact learning practices during the shift to digital. In particular, they can help fill a critical information gap for students created by an absence of classroom-based cues and the need for increased self-regulation in the online environment, However the adoption of learning analytics in effective, ethical and responsible ways is non-trivial. Ifenthaler and Schumacher (2016) present important findings about students’ perceptions of learning analytics’ usefulness and privacy, signaling the need for a student-centered paradigm, but stop short of addressing its implications for the creation and adoption of learning analytics tools. In this paper we address this limitation by describing the three specific shifts needed in current learning analytics practice for analytics to be accepted by and effective for students: (1) involve students in the creation of analytic tools meant to serve them; (2) develop analytics that are contextualized, explainable and configurable; and (3) empower students’ agency in using analytic tools as part of their larger process of learning. These shifts are currently in different stages of maturity and adoption in mainstream learning analytics practice. The primary implication of this work is a call to action for researchers and practitioners to rethink and reshape how students participate in the creation, interpretation and impact of learning analytics. Keywords Learning analytics · Student agency · Online learning · Ethics · Privacy · Datainformed decision-making
* Xavier Ochoa [email protected] Alyssa Friend Wise [email protected] 1
Learning Analytics Research Network, New York University, 370 Jay Street, 5th Floor, New York, NY 11201, USA
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The need for student‑centered learning analytics (SCLA) Learning analytics is an emerging technology that uses data science methods to generate actionable insights about learning (Siemens 2012; Leth Jørnø and Gynther 2018). Distinct from traditional educational research that first generates generalizable knowledge and then uses it to improve the experience of future learners, learning analytics generate new knowledge while simultaneously using it to inform current learning practices and learners (Clow 2012). This offers great potential for impact in the context of the COVID-19 pandemic where traditional practices have been upended and we need to quickly develop
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