Access to education is widely considered a critically important mechanism to aid social mobility. The substantial increase in the use of free or “low-cost” online educational tools in recent years has represented a major step forward in the education of people who are less able to enrol in more traditional educational activities (e.g. paid University courses) due to, for example, cost, geographic location or caring responsibilities (Tsai et al. 2018). Within this in recent years we have seen the emergence of massive open online courses (MOOCs) who are defined as online courses with unlimited student participation. They are typically offered by Universities for a wide range of rationales, including as a marketing tool, to widen research impact or to prepare incoming students (Kaplan and Haenlein 2016). Furthermore there are also many different motives for MOOC enrolment ranging from career development to simply satisfying curiosity (Kaplan and Haenlein 2016).
In 2018 over 80 million students enrolled in approximately 10,000 MOOCs offered by more than 800 different universities. In comparison approximately 35 million students enrolled in MOOCs provided by approximately 500 universities in 2015 (Colas et al. 2016). As such MOOC availability and student participation is currently undergoing major global expansion and further information is required in order to understand how MOOCs can be created for maximum personal and societal benefit. MOOC creation is typically a relatively expensive process and as such it is critical that each MOOC is both economically sustainable and pedagogically effective (i.e. deliver the intended learning outcomes to a sufficient number of students) (van de Oudeweetering and Agirdag 2018). MOOC funding is often provided by the MOOC creator (i.e. the academic via a research grant rather than from the institution) (Richter and Krishnamurthi 2014). This is a relatively sustainable business model because each MOOC is funded by a specific grant in order to deliver a specific academic and/or educational impact. Care must be taken, however, that each MOOC is created with an inbuilt capability to enable students to maintain enthusiasm throughout in order to deliver the intended learning outcomes (Vitiello et al. 2018).
Social learning in MOOCs
There are two main types of MOOC: xMOOCs and cMOOCs (Mohamed and Hammond 2018). The former is characterised by the use of conventional e-learning platforms which are typically individually orientated, well defined and instructional, whilst the latter is characterised as more focussed on social learning (i.e. connectivity and cooperation between learners). Whilst these two distinct types of MOOC serve different purposes the multifaceted functionality of most MOOCs dictates, however, that they can often be a blend of the two (Conole 2016). It is therefore apparent that whilst particular MOOCs can be considered as ideally suited for certain students they can simultaneous be considered poorly suited for other types of students (Hew and Cheung 2014). MOOCs generally exhibit low completion rates (currently averaging approximately 10% (Fidalgo-Blanco et al. 2016; Jordan 2013) which highlights the need to investigate and refine the current model. Many explanations have been postulated, however, one of the most common arguments regards the fact that MOOCs are typically free and relatively quick and easy to enrol onto, which in addition to their “attractive layout” (i.e. high quality graphics), dictates that individuals can be attracted to enrol without the full capacity, motivation and/or intention to continue to the end (Aguaded-Gómez 2013; Wong 2016).
Social learning (i.e. connectivity and cooperation between learners) is a key aspect of many MOOC platforms because it can enable scalable peer-based learning and as such is likely to be a highly useful tool to maintain continued student motivation (Brinton et al. 2014). Indeed social learning has also been demonstrated as effective at raising student satisfaction via alleviating feelings of isolation and lack of impersonal interactions (Lee et al. 2011; Li et al. 2014). Social learning in a MOOC can be instigated by a call to action that will prompt learners to engage in discussion with the wider cohort, share experiences or reflect on their learning with others.
Whilst anecdotally the feedback around these social learning activities has been positive, and the platform provider encourages a social learning approach there is very little data, that currently exists on the impact social learning has on the completion rate of MOOCs (Onah et al. 2014). An obvious unanswered question is therefore: Do social learners generally complete more steps within a MOOC than non-social learners?
This work has been established in order to provide preliminary data in order to answer this question. The University of Exeter currently offers 12 MOOCs which are all hosted via the FutureLearn platform (www.futurelearn.com), and several more are currently in preparation. They cover a range of topics, with titles ranging from: “Who Made My Clothes?” to “Genomic Medicine: Transforming Patient Care in Diabetes” to “Learn About Weather.” Such MOOCs have been created using a diverse range of funding mechanisms and for various different (interconnected) rationales, however, they all include inbuilt tools for students to undertake social learning. Herein the completion rate (i.e. number of steps accessed) by students who are defined as social learners (i.e. individuals which have left one comment or more on the MOOC forum) has been compared to those who are defined as non-social learners (i.e. individuals which have left no comments on the MOOC forum) has been analysed. Results are intended to inform the development of future MOOCs in order to maximise their utility as next generation learning platforms.
Two data files were downloaded for each of the MOOCs offered by the University of Exeter in 2018: one containing data on the total number of “steps accessed” (hereafter called the “steps accessed files”) by each student and one containing data on which specific step the students have each commented on (hereafter called the “comments files”). Within this “steps” are defined as discrete sections of each MOOC (e.g. a slide or panel) which requires the student to then click a button to then access the next “step.” Eight MOOCs were analysed: Empire: the Controversies of British Imperialism; Who Made My Clothes?; Genomic Medicine: Transforming Patient Care in Diabetes; Climate Change: The Science; Climate Change: Solutions; Learn About Weather; Valuing Nature: Should We Put a Price on Ecosystems?; and Tipping Points: Climate Change and Society. Individual users were identifiable with a unique 32 digit identification code (hereafter their “user ID”), however, no link was made between this, their personal details, or the specific comments they made and as such their identity remained anonymous. Moreover all comments in the “comments files” were identified by a date and time signature only (i.e. the actual comment was not viewed).
Social learners (i.e. students who have commented once or more) were first isolated from the “comments files” by removing all of the “user ID” duplicates, using the automated “remove duplicates” function in Microsoft Excel.
The number of steps accessed for each user in the “steps accessed files” was then sorted in descending order. Each MOOC followed a different notation for each steps (e.g. 1.1, 1.2, 1.3, 1.4, 2.1, 2.2, etc. or 1.1, 1.2, 2.1, 2.2, 2.3, 2.4). In order to normalise this, each step was assigned an integer, ascending from 1. The “remove duplicates function” was then used to delete all duplicate data and thus leave the data correlating to the highest step accessed by all students (i.e. the both Social Learners and the Non-Social Learners). The highest step accessed by each Social Learner was then determined using a VLOOKUP function (i.e. by correlating with the “user ID” from the “comments file”). The total number of users completing each step was then determined using a COUNTIF function. The highest step accessed by each Non-Social Learner was then determined by using a IFERROR function (i.e. to determine which users were not included in the Social Learner list) followed by an IF function (i.e. to state the number of steps accessed for each user that was not included in the Social Learning list). The total number of users accessing each step was then determined using a COUNTIF function. Data were then normalised to percentage of each cohort, with Social Learners and Non-Social Learners treated as being within separate cohorts.