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      Supporting the shift to digital with student-centered learning analytics

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          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.

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          Most cited references18

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          Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

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            Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

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              Student perceptions of privacy principles for learning analytics

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                Author and article information

                Contributors
                xavier.ochoa@nyu.edu
                alyssa.wise@nyu.edu
                Journal
                Educ Technol Res Dev
                Educ Technol Res Dev
                Educational Technology Research and Development
                Springer US (New York )
                1042-1629
                1556-6501
                25 November 2020
                : 1-5
                Affiliations
                GRID grid.137628.9, ISNI 0000 0004 1936 8753, Learning Analytics Research Network, , New York University, ; 370 Jay Street, 5th Floor, New York, NY 11201 USA
                Article
                9882
                10.1007/s11423-020-09882-2
                7687213
                92ad9aee-78e9-4bba-93b8-12556c766436
                © Association for Educational Communications and Technology 2020

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 7 November 2020
                Categories
                Article

                learning analytics,student agency,online learning,ethics,privacy,data-informed decision-making

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