A proposed learner activity taxonomy and a framework for analysing learner engagement versus performance using big educational data

Konstantinidis, Stathis and Fecowycz, Aaron and Coolin, Kirstie and Wharrad, Heather and Konstantinidis, George and Bamidis, Panagiotis (2017) A proposed learner activity taxonomy and a framework for analysing learner engagement versus performance using big educational data. In: 30th IEEE International Symposium on Computer-Based Medical Systems (IEEE CBMS 2017), 22-24 Jun 2017, Thessaloniki, Greece. (In Press)

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Abstract

The inclusion of information and communication technologies in Healthcare and Medical Education is a fact nowadays. Furthermore numerous virtual learning environments have been established in order to host both educational material and learner’s online activities. Online modules in a VLE can be designed in very different ways being part of different types of courses, while different models can be used to design the course based on what the creator aims to achieve. Thus, the types and the importance of the different elements of the online course may vary a lot. At the same time the need of a global approach to gather big educational data in order to provide valid meaning to the data through learning analytics and educational data mining is urgent. In order this to be achievable we propose a Learner Activity Taxonomy in which the different elements of the learners activity data can be categorised and a Learner Engagement Framework in which the importance of the different elements is vital in order for an analysis of the big educational data to provide a meaningful result. The initial application to practice of the Taxonomy and the Framework are presented based on data from 3 modules at 2 Universities, while the impact of them along with its limitations are discussed.

Item Type: Conference or Workshop Item (Paper)
Additional Information: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: Learning Analytics; Big Data; learner engagement; online learning analysis, activity data; paradata;
Schools/Departments: University of Nottingham, UK > Faculty of Medicine and Health Sciences > School of Health Sciences
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Depositing User: Konstantinidis, Stathis
Date Deposited: 11 Sep 2017 13:28
Last Modified: 13 Oct 2017 04:20
URI: http://eprints.nottingham.ac.uk/id/eprint/45607

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