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Artificial Intelligence to Enhance Learning Design in UOW Online, a Unified Approach to Fully Online Learning

Sie, Rory L. L.; Delahunty, Janine; Bell, Kenton; Percy, Alisa; Rienties, Bart; Cao, Trinh and de Laat, Maarten (2019). Artificial Intelligence to Enhance Learning Design in UOW Online, a Unified Approach to Fully Online Learning. In: 2018 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE), IEEE, pp. 761–767.

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DOI (Digital Object Identifier) Link: https://doi.org/10.1109/TALE.2018.8615283
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Abstract

The current article presents a framework for the design and support of UOW Online, an entirely new unified university strategy for fully online learning. To aid teachers in the learning design process, we aim to create more awareness for teachers by determining the underlying learning design of their subject. To ensure the approach can be scaled up to cater for potentially hundreds of subjects, the manual labeling serves as input for an Artificial Intelligence (AI) algorithm that will train a model to label intended learning activities automatically. In addition to student demographics and behavior, the learning design and subject content will be used to augment an AI model that predicts future student outcomes. Future work focuses on the collection of necessary learning activities and manual encoding of these learning activities.

Item Type: Conference or Workshop Item
Copyright Holders: 2018 IEEE
ISBN: 1-5386-6522-0, 978-1-5386-6522-0
ISSN: 2470-6698
Keywords: artificial intelligence; learning design; online learning; networked learning
Academic Unit/School: Faculty of Wellbeing, Education and Language Studies (WELS)
Item ID: 59057
Depositing User: Bart Rienties
Date Deposited: 11 Feb 2019 10:46
Last Modified: 12 Feb 2019 15:09
URI: http://oro.open.ac.uk/id/eprint/59057
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