Data-Driven analysis of engagement in gamified learning environments: A methodology for real-time measurement of MOOCs

Abstract

Welfare and economic development is directly dependent on the availability of highly skilled and educated individuals in society. In the UK, higher education is accessed by a large percentage of high school graduates (50% in 2017). Still, in Brazil, a limited number of pupils leaving high schools continue their education (up to 20%). Initial pioneering efforts of universities and companies to support pupils from underprivileged backgrounds, to be able to succeed in being accepted by universities include personalised learning solutions. However, initial findings show that typical distance learning problems occur with the pupil population: isolation, demotivation, and lack of engagement. Thus, researchers and companies proposed gamification. However, gamification design is traditionally exclusively based on theory-driven approaches and usually ignore the data itself. This paper takes a different approach, presenting a large-scale study that analysed, statistically and via machine learning (deep and shallow), the first batch of students trained with a Brazilian gamified intelligent learning software (called CamaleOn), to establish, via a grassroots method based on learning analytics, how gamification elements impact on student engagement. The exercise results in a novel proposal for real-time measurement on Massive Open Online Courses (MOOCs), potentially leading to iterative improvements of student support. It also specifically analyses the engagement patterns of an underserved community.