Title
A Proposed Learner Activity Taxonomy and a Framework for Analysing Learner Engagement versus Performance Using Big Educational Data
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 learners 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.
Year
DOI
Venue
2017
10.1109/CBMS.2017.160
2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)
Keywords
Field
DocType
Learning Analytics,Big Data,learner engagement,online learning analysis,activity data,paradata
Data science,Data mining,Virtual learning environment,Data modeling,Learning analytics,Computer science,Knowledge management,Online course,Learner engagement,Information and Communications Technology,Educational data mining
Conference
ISSN
ISBN
Citations 
2372-9198
978-1-5386-1711-3
0
PageRank 
References 
Authors
0.34
4
6