A proposed learner activity taxonomy and a framework for analysing learner engagement versus performance using big educational data
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 desi...
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| Format: | Article |
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IEEE
2017
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| Online Access: | https://eprints.nottingham.ac.uk/45607/ |
| _version_ | 1848797164649578496 |
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| author | Konstantinidis, Stathis Fecowycz, Aaron Coolin, Kirstie Wharrad, Heather Konstantinidis, George Bamidis, Panagiotis |
| author_facet | Konstantinidis, Stathis Fecowycz, Aaron Coolin, Kirstie Wharrad, Heather Konstantinidis, George Bamidis, Panagiotis |
| author_sort | Konstantinidis, Stathis |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:59:32Z |
| format | Article |
| id | nottingham-45607 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:59:32Z |
| publishDate | 2017 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-456072020-05-04T19:17:31Z https://eprints.nottingham.ac.uk/45607/ A proposed learner activity taxonomy and a framework for analysing learner engagement versus performance using big educational data Konstantinidis, Stathis Fecowycz, Aaron Coolin, Kirstie Wharrad, Heather Konstantinidis, George Bamidis, Panagiotis 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. IEEE 2017-11-13 Article PeerReviewed Konstantinidis, Stathis, Fecowycz, Aaron, Coolin, Kirstie, Wharrad, Heather, Konstantinidis, George and Bamidis, Panagiotis (2017) A proposed learner activity taxonomy and a framework for analysing learner engagement versus performance using big educational data. Proceedings of the IEEE International Symposium on Computer-Based Medical Systems . pp. 429-434. ISSN 2372-9198 Learning Analytics; Big Data; learner engagement; online learning analysis activity data; paradata; http://ieeexplore.ieee.org/document/8104232/ |
| spellingShingle | Learning Analytics; Big Data; learner engagement; online learning analysis activity data; paradata; Konstantinidis, Stathis Fecowycz, Aaron Coolin, Kirstie Wharrad, Heather Konstantinidis, George Bamidis, Panagiotis A proposed learner activity taxonomy and a framework for analysing learner engagement versus performance using big educational data |
| title | A proposed learner activity taxonomy and a framework for analysing learner engagement versus performance using big educational data |
| title_full | A proposed learner activity taxonomy and a framework for analysing learner engagement versus performance using big educational data |
| title_fullStr | A proposed learner activity taxonomy and a framework for analysing learner engagement versus performance using big educational data |
| title_full_unstemmed | A proposed learner activity taxonomy and a framework for analysing learner engagement versus performance using big educational data |
| title_short | A proposed learner activity taxonomy and a framework for analysing learner engagement versus performance using big educational data |
| title_sort | proposed learner activity taxonomy and a framework for analysing learner engagement versus performance using big educational data |
| topic | Learning Analytics; Big Data; learner engagement; online learning analysis activity data; paradata; |
| url | https://eprints.nottingham.ac.uk/45607/ https://eprints.nottingham.ac.uk/45607/ |