Exploring non-traditional learner motivations and characteristics in online learning: A learner profile study

Abstract

The growth and uptake of educational technology has significantly reshaped the delivery of distance and online learning. With an unprecedented number of learners engaging with online modes of education, there is a growing need to understand the underlying student enrolment motivations, goals and learning behaviours evolving from a highly diverse student population. Research in learning analytics has advanced the use of digital data to understand student learning processes. However, there remains a limited understanding of how non-traditional learner characteristics, needs and motivational factors influence their learning behaviour and engagement strategies. Survey data from 232 students enrolled in fully online degree programs at a large public research university in Australia has been examined and used to represent 1687 students that have not completed the survey. To characterise the larger population of students, we combined their demographics, digital trace data, and course performance to provide richer insights of non-traditional learners in online learning. Data science approaches are applied, including an unsupervised machine learning technique that revealed the results of six unique learner profiles, clearly differentiated by their motivation, demographic, engagement and performance. While the findings show that each learner profile faces unique study challenges, there are also unique opportunities associated with each profile that could be utilised to improve their learning outcomes. The practical implications of the study on teaching practices are further discussed. • Non-traditional learners enrolment motivation is valuable for personalisation when combined with heterogeneous online data. • Moderate to high engagement do not necessarily translate to better academic outcomes. • Non-traditional learners defined by motivation for enrolment, engagement and demographics may be prone to poorer outcomes. • Online programs tend to attract several distinct types of non-traditional learners. • We provide a methodology for profiling learners utilising a diverse set of student data.