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Learning Analytics as AI Extenders in Education: Multimodal Machine Learning versus Multimodal Learning Analytics

Cukurova, M; (2019) Learning Analytics as AI Extenders in Education: Multimodal Machine Learning versus Multimodal Learning Analytics. In: Mitchell, Tom, (ed.) Proceedings of AIAED 2019. AIAED: Beijing, China. Green open access

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Abstract

The nature of the appropriate role for AI is a topic of great interest in many disciplines, and Education is no exception. Perhaps, the initial focus of AI in Education research was on attempts to create systems that are as perceptive as human teachers [1]. Therefore, the majority of early research in the field focused on designing autonomous intelligent tutoring systems. However, more recently, there have been AI technologies embedded in non-autonomous systems, used by educators to support their practice. Non-autonomous systems, in which AI is used to extend human cognition and enhance teacher and learner capabilities, differ significantly from approaches that aim to create fully automated AI systems. These non-autonomous approaches might even be considered as ‘incomplete’ or ‘inadequate’ in AI research. Here, I argue that, in educational contexts, AI systems should be considered a continuum with regards to the extent they are decoupled from humans, rather than only an approach to provide full-automation. AI can be used to externalize, internalize or extend human cognition [2], and these different conceptualizations and implementations of AI can each have a valuable role to play in the support of learning and teaching. In this paper, I will briefly describe the distinctions between AI as a fully autonomous system versus AI as part of a non-autonomous supportive system, and present examples of both in educational contexts. I will particularly focus on multimodal learning analytics where the human cognition is internalised or extended with AI tools, rather than externalised. For educational research, where the ultimate purpose is to improve education rather than improving the state-of-the-field in AI, AI extenders as exemplified in learning analytics research should be distinct from research on fully autonomous AI designs; and they require attention from the field at least in equal measure.

Type: Proceedings paper
Title: Learning Analytics as AI Extenders in Education: Multimodal Machine Learning versus Multimodal Learning Analytics
Event: AIAED 2019
Open access status: An open access version is available from UCL Discovery
Publisher version: https://www.aiaed.net/
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Education
UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education
UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education > IOE - Culture, Communication and Media
URI: https://discovery.ucl.ac.uk/id/eprint/10078181
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