An Analysis of Online Learner Types Applicable to Lifelong Learning Environments

Abstract

Based on the perspective of Sustainable Development Goals (SDGs) Quality Education and lifelong learning, it is necessary to respect the learning opportunities and quality for all individuals. Online learning can provide more opportunities for lifelong learning, but due to the significant differences in students’ backgrounds and characteristics, personalized and timely support becomes more crucial. Learning analytics (LA) in online learning environment is a way to facilitate understanding of the potential meaningful information and relationships of students. One of the main functions of LA is to monitor the learning performance and identify potential learning problems early. In this study, 𝑘-means clustering is performed to determine the types of learning in lifelong online learning environments, based on students’ personal traits (background factors), learning behavior paths, and interactive perspectives on learning performance. Moreover, statistical analysis is used to further evaluate the linear correlation coefficients as well as the characteristics of each group of students, who ranged in age from 18 to 73, with a total of 2386 participants from five courses, in the interactive perspective. The result shows a significant correlation between learning performance and persistence across the three learning clusters, with a tendency towards continuous learning, thus providing educators an understanding of the learning behavior characteristics of those types of online learners.