Putting learning back into learning analytics: actions for policy makers, researchers, and practitioners
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Putting learning back into learning analytics: actions for policy makers, researchers, and practitioners Dirk Ifenthaler1,2 · David Gibson2 · Doreen Prasse3 · Atsushi Shimada4 · Masanori Yamada4 Accepted: 18 November 2020 © The Author(s) 2020
Abstract This paper is based on (a) a literature review focussing on the impact of learning analytics on supporting learning and teaching, (b) a Delphi study involving international expert discussion on current opportunities and challenges of learning analytics as well as (c) outlining a research agenda for closing identified research gaps. Issues and challenges facing educators linked to learning analytics and current research gaps were organised into four themes, the further development of which by the expert panel, led to six strategy and action areas. The four themes are 1. development of data literacy in all stakeholders, 2. updating of guiding principles and policies of educational data, 3. standards needed for ethical practices with data quality assurance, and 4. flexible user-centred design for a variety of users of analytics, starting with learners and ensuring that learners and learning is not harmed. The strategies and actions are outcomes of the expert panel discussion and are offered as provocations to organise and focus the researcher, policymaker and practitioner dialogs needed to make progress in the field. Keywords Learning analytics · Policy recommendation · Learning support · Data literacy · Data privacy
Introduction Making productive use of the growing output of educational data systems presents a novel challenge for scientists as well as educators and learners. In the context of education, learning analytics, which refers to the use of static and dynamic data from learners and their contexts for the improvement of learning processes and educational decision-making (Ifenthaler 2015), offer a range of opportunities for supporting learning and teaching, * Dirk Ifenthaler [email protected] 1
University of Mannheim, L4, 1, 68131 Mannheim, Germany
2
Curtin University, Perth, Australia
3
The Schwyz University of Teacher Education, Goldau, Switzerland
4
Kyushu University, Fukuoka, Japan
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formative and summative assessment, as well as improving learning design. For instance, making use of new modes of analysing and modelling data, data sets can be processed in real-time and presented to learners during their learning process (Ifenthaler et al. 2018a, b). Furthermore, learning analytics can be used to inform and influence decisions on different levels of the educational system (e.g. micro, meso, macro) to improve individual and organisational learning and performance. Various stakeholder groups, such as learners, teachers/educators, instructional designers, institutional leaders, scientists, and public as well as private providers already draw on learning analytics or are contemplating ways to make use of these rapid developments (Baker and Siemens 2015). Although research in recent years has shown h
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