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Unravelling the dynamics of learning design within and between disciplines in higher education using learning analytics

Nguyen, Quan (2017). Unravelling the dynamics of learning design within and between disciplines in higher education using learning analytics. In: Learning Analytics and Knowledge (LAK) doctoral consortium, LAK 17.

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Abstract

Designing effective learning experience in virtual learning environment (VLE) can be supported by learning analytics (LA) through explicit feedback on how learning design (LD) influences students’ engagement, satisfaction and performance. Marrying LA with LD not only puts existing pedagogical theories in instructional design to the test with actual learning data, but also provides the context of learning which helps educators translate established LA findings to direct interventions. My dissertation aims at unpacking the complexity of LD and its impact on students’ engagement, satisfaction and performance on VLE using LA. The context of this study is 400+ online and blended learning modules at the Open University (OU) UK. This research combines multiple sources of data from the OU Learning Design Initiative (OULDI), system log data, self-reported surveys, and performance data. Given the scope of this study, a wide range of visualization techniques, social network analysis, multi-level modelling, and machine learning will be used.

Item Type: Conference or Workshop Item
Keywords: Learning analytics; learning design; engagement; satisfaction; retention; performance
Academic Unit/School: Learning Teaching and Innovation (LTI) > Institute of Educational Technology (IET)
Learning Teaching and Innovation (LTI)
Item ID: 48839
Depositing User: Quan Nguyen
Date Deposited: 17 Mar 2017 10:42
Last Modified: 18 Mar 2017 17:18
URI: http://oro.open.ac.uk/id/eprint/48839
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