Unsupervised learning for understanding student achievement in a distance learning setting
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Liu, Shuangyan, & d'Aquin, Mathieu. (2017). Unsupervised learning for understanding student achievement in a distance learning setting. Paper presented at the IEEE Global Engineering Education Conference (EDUCON), Athens. doi: 10.1109/EDUCON.2017.7943026
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.
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