Abstract
Massive open online courses (MOOCs) are described as disruptive and democratising. It is claimed MOOCs have characteristics that challenge traditional forms of education. This chapter critiques these claims, arguing that MOOCs do not always allow for the diverse motivations of masses of learners. This brings into question forms of data-based support based on and in response to learner behaviours. The chapter interrogates narrative accounts of MOOC learner experiences to pinpoint four distinct ways people learn in MOOCs. Factors critical to learning are motivation, self-regulation, environment and socialisation. Developing analytic tools that address these are important. However, analytics systems tend to personalise learner support in relation to pre-defined course goals, rather than focusing on the goals of the learner. Next generation systems are already focusing on empowering learners to follow their own goals and flexing course designs to fit the goals of each learner. These are more powerful than systems where the students have to adapt to a course designed for the masses.
TopIntroduction
Massive open online courses (MOOCs) have been signaled as a disruptive and democratising force in online, distance education. It is claimed that MOOCs have unique characteristics that challenge traditional parameters of learning (and education). These characteristics act as a leveler by offering equal access to Higher Education to billions of learners worldwide, reducing a huge divide between approximately 7% of the world’s population who have a degree and 93% who do not (Bloomberg, 2011).
Advocates of MOOCs also promote their ability to provide forms of teaching and learning more relevant to a hyper-connected, digital age, and which are more attuned to the needs of people, particularly those wishing to up-skill and engage in life-long learning. As Andre Dua (2013), a Senior Partner at McKinsey & Company, claims:
What most people—including university leaders—don’t yet realize is that this new way of teaching and learning, together with employers’ growing frustration with the skills of graduates, is poised to usher in a new credentialing system that may compete with college degrees within a decade. This emerging delivery regime is more than just a distribution mechanism; done right, it promises students faster, more consistent engagement with high-quality content, as well as measurable results. This innovation therefore has the potential to create enormous opportunities for students, employers, and star teachers even as it upends the cost structure and practices of traditional campuses (n.p.).
Indeed, MOOCs have become an industry in their own right. ClassCentral, a website aggregating data and information on MOOCs, listed 30 MOOC providers in 2017. These providers collaborate with over 800 universities and companies to offer over 9,400 courses, more than 500 MOOC-based credentials, and more than a dozen graduate degrees. 20 million new learners signed up for their first MOOC in 2017, bringing the total number of MOOC learners to 78 million.
The four dimensions comprising the acronym MOOC – massive, open, online, course – simultaneously represent valuable assets that underpin the promise of MOOCs as well as posing challenges for MOOC designers and facilitators and leading to a number of contradictions associated with MOOCs. Massive refers to the scale of the course and alludes to the large number of learners who participate in some MOOCs. It is closely connected to open, which can refer to access; anyone, no matter his or her background, prior experience or current context may enroll in a MOOC as well as to cost – that a MOOC is available free of charge. New technological infrastructure and digital technologies are not only providing wide-scale access, but also enabling new approaches to learning and the repositioning – if not in practice then theoretically – of learners, educators and institutions. As Selwyn observes: ‘the ever-expanding connectivity of digital technology is recasting social arrangements and relations in a more open, democratic, and ultimately empowering manner.’ (Selwyn, 2012, p. 2).
However, the current reality of learning in MOOCs remains distant from this alluring promise. There continues to be considerable variation both in the nature of learning that MOOCs offer and the ways in which individuals choose to engage with them. As the authors have previously noted:
The specific nature and composition of individual MOOCs are profoundly shaped and ultimately the product of their designers and instructors, the platform and platform provider, and the participants, all of whom bring their own frames of reference and contextual frameworks. (Hood & Littlejohn, 2016, p. 5).
Successful and effective large-scale online education is expensive and notoriously challenging to produce and deliver (Ferguson & Sharples, 2014). Furthermore, the number and potential diversity of learners, in terms of background, geography, motivation, previous experience and ability to learn, pose substantial challenges for MOOC designers and facilitators. The corollary is that the outcomes of a particular learning experience will differ considerably depending on the student and his or her ability to learn, leading to what Selwyn (2016) describes as ‘inequalities of participation’ (p.31).
Key Terms in this Chapter
Open Education: Can refer to learning that is free to access or it can refer to an educational opportunity that has not pre-requisites so that anyone can access, no matter their background or past experiences.
Learning Environment: The multiple contexts, both online and offline, in which a learner learns.
Self-Regulation: The ability to manage and regulate one’s own learning processes.
Data Analytics: Quantitative and qualitative techniques and analysis processes to extract online data that relates to learners’ behaviours, patterns of engagement, and backgrounds to enhance learning experiences.
Socialisation: The patterns of behaviour and interaction among learners in a learning environment
MOOC: MASSIVE, open, online, course.
Motivation: The reason or reasons for why one behaviours in a particular way.
Learning Analytics: The measurement, collection, analysis and application of data about learners, including their learning and contexts, and is used to help to optismise learning experiences.