Modelling adult learners’ online engagement behaviour: proxy measures and its application

With teaching and learning taking place in an increasingly networked environment over e-learning platforms, voluminous data are logged in databases. These can be mined and processed to support teaching and learning practices. To determine the impact of these practices, meaningful measures of learners’ online engagement are needed. In particular, this research focuses on discovering useful and meaningful data features of online engagement in learning activities from traces of adult learners’ online behavioural data. Whilst vast data from learning activities may be available over a technology-enhanced learning environment, it becomes significantly important to identify a methodical approach to transform data features in a modus that is useful for analysis. Hence, the purpose of this study is twofold: (1) to adapt and reconstruct the RFM model (a marketing segmentation technique on customers’ recency, frequency and monetary purchasing behaviour), as a common framework to codify and quantify learners’ online study behaviour, in the learning analytics context; and (2) to explore the online engagement patterns of adult learners using data-mining techniques. We show examples of its applications using real-world data from an online course—by modelling adult learners’ online engagement patterns and discovering learners’ segments based on immediacy, frequency and duration.

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