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Unsupervised learning for understanding student achievement in a distance learning setting

Authors: 

Shuangyan Liu, Mathieu d'Aquin

Publication Type: 
Refereed Conference Meeting Proceeding
Abstract: 
Many factors could affect the achievement of students in distance learning settings. Internal factors such as age, gender, previous education level and engagement in online learning activities can play an important role in obtaining successful learning outcomes, as well as external factors such as regions where they come from and the learning environment that they can access. Identifying the relationships between student characteristics and distance learning outcomes is a central issue in learning analytics. This paper presents a study that applies unsupervised learning for identifying how demographic characteristics of students and their engagement in online learning activities can affect their learning achievement. We utilise the K-Prototypes clustering method to identify groups of students based on demographic characteristics and interactions with online learning environments, and also investigate the learning achievement of each group. Knowing these groups of students who have successful or poor learning outcomes can aid faculty for designing online courses that adapt to different students' needs. It can also assist students in selecting online courses that are appropriate to them.
Conference Name: 
EDCUON 2017
Proceedings: 
Global Engineering Education Conference (EDUCON), 2017 IEEE
Digital Object Identifer (DOI): 
10.1109/EDUCON.2017.7943026
Publication Date: 
25/04/2017
Conference Location: 
Greece
Research Group: 
Institution: 
National University of Ireland, Galway (NUIG)
Open access repository: 
No
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