LEARNING APPETITE: VISUALIZING HOW LEARNERS CONSUME MANDATORY ONLINE LEARNING MATERIALS

Abstract

In workplace learning, mandatory learning presents a dilemma. It is essential for organizations to meet their objectives, and such training is typically designed based on specific work-based requirements. However, this type of learning is often not favorable for adult learners, who have their own preferences and motivations. According to adult learning theory, adults learn in a self-regulated manner, which can differ from organizational goals. Adult learners in online learning often engage based on specific needs and interests. They may access only parts of the course and specific activities or spend less time on materials that they are already skilled in. Consequently, there will be range of interaction patterns. Due to this variation in engagement, it is beneficial to further explore how are those patterns and its impact on learning outcomes. To explore that, we analysed online learner interactions on learning management systems (Jo et al.), recorded and used as a proxy of learning. This study analyses data from 75,712 learners participating in a mandatory fourteen-day online learning program, focusing on two main data points: the learning time, which measures the duration learners spend on the LMS, and the spacing time, which tracks the duration of breaks learners take before accessing the next learning material. Through data analysis, we created visualizations and groupings to identify learning interaction patterns. Subsequently, we drew an analogy between these patterns and different types of appetites, introducing four distinct learner categories: big eaters, who spend more time learning but take shorter breaks; picky eaters, who spend less time learning but take longer breaks; nibblers, who spend more time learning and take longer breaks; and gulpers, who spend less time learning and take shorter breaks. The visualizations and analogies presented in this study illustrate how learners consume learning materials based on their timing and spacing interactions.