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Open Learning Analytics: A Systematic Literature Review and Future Perspectives

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Artificial Intelligence Supported Educational Technologies

Part of the book series: Advances in Analytics for Learning and Teaching ((AALT))

Abstract

Open learning analytics (OLA) is an emerging research area that aims at improving learning efficiency and effectiveness in lifelong learning environments. OLA employs multiple methods to draw value from a wide range of educational data coming from various learning environments and contexts in order to gain insight into the learning processes of different stakeholders. As the research field is still relatively young, only a few technical platforms are available, and a common understanding of requirements is lacking. This paper provides a systematic literature review of tools available in the learning analytics literature from 2011 to 2019 with an eye on their support for openness. One hundred thirty-seven tools from nine academic databases are collected to form the base for this review. The analysis of selected tools is performed based on four dimensions, namely, “Data, Environments, Context (What?),” “Stakeholders (Who?),” “Objectives (Why?),” and “Methods (How?).” Moreover, five well-known OLA frameworks available in the community are systematically compared. The review concludes by eliciting the main requirements for an effective OLA platform and by identifying key challenges and future lines of work in this emerging field.

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Authors and Affiliations

  1. Social Computing, Department of Computer Science and Applied Cognitive Science, University of Duisburg-Essen, Duisburg, Germany

    Arham Muslim, Mohamed Amine Chatti & Mouadh Guesmi

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  1. Arham Muslim
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  2. Mohamed Amine Chatti
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  3. Mouadh Guesmi
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Correspondence to Mohamed Amine Chatti .

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  1. Institut für Informatik, Humboldt-Univ. zu Berlin, Berlin, Germany

    Niels Pinkwart

  2. National Engineering Research Center for E-learning, Central China Normal University, Wuhan, Hubei, People’s Republic of China

    Sannyuya Liu

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Muslim, A., Chatti, M.A., Guesmi, M. (2020). Open Learning Analytics: A Systematic Literature Review and Future Perspectives. In: Pinkwart, N., Liu, S. (eds) Artificial Intelligence Supported Educational Technologies. Advances in Analytics for Learning and Teaching. Springer, Cham. https://doi.org/10.1007/978-3-030-41099-5_1

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