The use of technology-based training and the growing adoption of Learning Management systems have more than doubled in the past decade (Brown, Charlier, & Pierotti, 2012). Higher education have seen paradigm shifts from the traditional classrooms and tangible learning resources to asynchronous e-learning environments. This paradigm shift has fundamentally changed how learners are engaged. Technology enhanced learning has many challenges, one of which is the learners’ engagement. Learner engagement is a critical issue to the success of both online education and professional learning. Bruner (2013) asserted that “engagement is the ultimate test” (p. 34) to successful e-Learning adoption in learning.
As economies of the world continue to evolve, there is a continuum of educational and training needs for adult learners in lifelong learning. According to OECD statistics, 57% of the population aged 25-64 years old in Singapore, participated in formal and/or non-formal education in 2015. From an adult education survey, this statistic saw an upward trend in EU-28, from 2011 to 2015. Increasingly, course developers and instructional designers have to grapple with the learning needs of this growing group of adult learners (Kankaraš, Montt, Paccagnella, Quintini, & Thorn, 2016).. As formal learning take place in an increasingly networked environment over e-learning platforms and learning management systems, in order to cater to the changing demographics of learners. The shift towards digital environments in learning make it possible to capture, store, manage and retrieve increasingly large amounts of data, over the cloud. This provides an unprecedented opportunity to capture data related to learner engagement. Aided by data mining methods, the analysis and sense-making of the interaction data between the learner, learning environment and learning activities has become less cumbersome than before, and this can support a better understanding of the engagement process (Gašević, Dawson & Siemens, 2015). The importance of understanding these interactions and what might increase effectiveness of such interactions in online education is paramount for meaningful learning. In particular, this research focuses on discovering meaningful patterns of engagement and disengagement in learning activities from traces of the adult learners’ online engagement behavioural data.
Learner engagement has been defined in several different ways. Learner engagement is made up of the interaction that a learner has with their instructor, course content, and other learners. According to Fredricks and MsColskey (2012), “researchers, educators, and policymakers are increasingly focused on student engagement as the key to address problems of low achievement, high levels of student boredom, alienation, and high dropout rates” (p.763). The definition and measurement of learner engagement become more complex in the case of online learning environments.
Measuring learner engagement and its influence on learning is challenging. Online learning invariably leaves behind rich data trails from the learners’ interactions with the learning resources (e.g. study notes, course content, quizzes, recorded lectures and readings), peers and instructors (e.g. discussions). In an online environment, learners’ timely learning behaviours can be observed by accessing log data. While the definition of learner engagement should stay consistent with more traditional learning environments, the measurement of learner engagement should be unique to the data availability of the online learning environment. Identifying proxies of online learner engagement can provide a degree of measurability that can be used to inform and improve upon existing teaching and learning practices. Hence, the purpose of this study is twofold: (1) to explore the potential of reconstructing a variation of the RFM (a marketing segmentation technique based on customers’ recency, frequency and monetary purchasing behaviour) analysis, as a framework to codify and quantify the adult learners’ online engagement; and (2) to explore the online engagement patterns of adult learners using data mining techniques.