Learning analytics and societal challenges: Capturing value for education and learning
Muukkonen, Hanni; van Leeuwen, Anouschka; Gašević, Dragan (2023-09-22)
Avaa tiedosto
Sisältö avataan julkiseksi: 22.03.2025
Routledge
22.09.2023
Muukkonen, H., Van Leeuwen, A., & Gašević, D. (2023). Learning analytics and societal challenges. In C. Damşa, A. Rajala, G. Ritella, & J. Brouwer, Re-theorising Learning and Research Methods in Learning Research (1st ed., pp. 216–233). Routledge. https://doi.org/10.4324/9781003205838-15
https://creativecommons.org/licenses/by-nc-nd/4.0/
© 2023 Informa UK Limited. This is an Accepted Manuscript version of the following article, accepted for publication in Re-theorising Learning and Research Methods in Learning Research. Muukkonen, H., Van Leeuwen, A., & Gašević, D. (2023). Learning analytics and societal challenges. In C. Damşa, A. Rajala, G. Ritella, & J. Brouwer, Re-theorising Learning and Research Methods in Learning Research (1st ed., pp. 216–233). Routledge. https://doi.org/10.4324/9781003205838-15. It is deposited under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
https://creativecommons.org/licenses/by-nc-nd/4.0/
© 2023 Informa UK Limited. This is an Accepted Manuscript version of the following article, accepted for publication in Re-theorising Learning and Research Methods in Learning Research. Muukkonen, H., Van Leeuwen, A., & Gašević, D. (2023). Learning analytics and societal challenges. In C. Damşa, A. Rajala, G. Ritella, & J. Brouwer, Re-theorising Learning and Research Methods in Learning Research (1st ed., pp. 216–233). Routledge. https://doi.org/10.4324/9781003205838-15. It is deposited under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
https://creativecommons.org/licenses/by-nc-nd/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202312073534
https://urn.fi/URN:NBN:fi:oulu-202312073534
Tiivistelmä
ABSTRACT
A complex challenge for the society is to offer equal learning opportunities at various life stages and to support students, teachers, and institutions in their various tasks and roles related to learning and teaching. Learning analytics (LA) provides an opportunity to address these societal challenges. As the LA field matures, tool development is aimed at aiding informed human decision-making and combating inequalities. For example, detecting students at risk of dropping out or supporting self-regulated learning. The inception of LA was catalysed by an increasing amount of available data and what could be done with these data to improve learner support and teaching. Simultaneously, an increase in the computational power, machine learning methods, and tools at hand offer renewing affordances to analyse and visualise data both retrospectively and for predictive purposes. Employing LA as a solution also brings potential problems, such as unequal treatment, privacy concerns, and unethical practices. Through selected example cases, this chapter presents and addresses these potentials and risks.
A complex challenge for the society is to offer equal learning opportunities at various life stages and to support students, teachers, and institutions in their various tasks and roles related to learning and teaching. Learning analytics (LA) provides an opportunity to address these societal challenges. As the LA field matures, tool development is aimed at aiding informed human decision-making and combating inequalities. For example, detecting students at risk of dropping out or supporting self-regulated learning. The inception of LA was catalysed by an increasing amount of available data and what could be done with these data to improve learner support and teaching. Simultaneously, an increase in the computational power, machine learning methods, and tools at hand offer renewing affordances to analyse and visualise data both retrospectively and for predictive purposes. Employing LA as a solution also brings potential problems, such as unequal treatment, privacy concerns, and unethical practices. Through selected example cases, this chapter presents and addresses these potentials and risks.
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