Building Engagement for MOOC Students - Introducing Support for Time Management on Online Learning Platforms
Résumé
University jointly have allocated 60 million dollars for edX development) [22, 24]. Nevertheless, MOOC providers are businesses and as such they bear the costs of the goods' and services' production and distribution. In this case, it would comprise inter alia the costs of content creation, platform implementation, and the purchase and maintenance of in-frastructure (eg. servers). Hence, sooner or later MOOC providers will have to adapt a business model and monetize the services they provide. Thus far, the following revenue models have been consid-ered: 1) introduction of small charges for certification or selected courses registration; 2) head hunting service offers; 3) creation of products networking students with potential recruiters and employers (eg. conferences or workshops or-ganization); 4) selling data on students' performance in dif-ferent tasks [22, 24]. MOOC platforms can make available globally Ivy-League quality education, provided there is access to the Internet. Sound pedagogical foundations underlie their design. Fur-thermore, MOOC platforms take advantage of the newest re-search findings to provide their students with as engaging an experience as possible (eg. retrieval questions in videos [19, 18] or peer assessment [25, 36, 34]). Nevertheless, MOOC platforms struggle with the problem of an exceedingly high 90% drop-out rate [33, 22]. Following the acquisition of information about a course offer (awareness phase) only a fraction of people decide to enroll in a course (registration phase). Then, only a number of them engage in any activity in the course (activity phase) and even less produce meaningful progress (progress phase). Finally, only a small fraction finish the course (about 27% of high school, 8% of undergraduate and 5% of graduate participants [21]). This drastic decrease of engagement in a course through time and different phases, due to its sim-ilarity to marketing purchasing model, is called funnel of participation [8]. The described phenomenon of the low completion rate presents a problem for MOOC platforms which have to mon-etize their services. Taking into consideration the huge scale of MOOCs (a course can attract thousands or even hun-dreds of thousands of people [13, 31]), they can exploit scale benefits [22]. However, solving the problem at its root, by reducing the withdrawal rate, would create a more sustain-able solution. Thus, we argue to analyze the problem closely before turning to economies of scale. 3. WITHDRAWAL REASONS ANALYSIS Due to the relative novelty of MOOCs and resulting data scarcity, the problem of MOOCs' low completion rate has not been widely examined in literature. To the best of our knowledge, thus far only one extensive study in this area has been performed [1]. Nevertheless, the applicability of its results might be limited in our context as the authors focused on advantageous MOOC course features. Thus, they could not account for some external (not related to the courses) aspects influencing participants' final decision of dropping out from courses. As a consequence, we decided to collect our own dataset to analyze the problem. In order to study MOOC participants' withdrawal reasons and the rate of occurrence of each reason in their overall population we carried out a survey. The survey was run in an online scenario. The participants were recruited via CrowdFlower 1 (a crowdsourcing platform). It included 508 participants who, apart from providing basic demographic information (gender, education level etc.) and information on MOOC experience (eg. MOOC provider name), were asked to indicate the reasons for their MOOC withdrawal decision. The participants were provided with 12 sample reasons chosen based on great MOOC features indicated by Adamopoulos [1], readers of a Web site devoted to open edu-cation [9] and adapted to our context. To minimize the pos-sible bias related to the order of reasons in the question, the suggested possibilities were randomized (order counterbal-ancing). The participants were also encouraged to indicate other reasons for their withdrawal decision. It is impossible to effectively verify if the participants re-cruited through the crowdsourcing platform were ever fully engaged in any MOOC or used any MOOC platform. Nev-ertheless, their responses reflect their opinion formed based on the experience they gained in other learning processes throughout their lives. Moreover, the scale of the study al-lows us to draw conclusions from the data [5]. The results demonstrate (Table 3) that the main reason for the high MOOC withdrawal rate is bad time manage-ment. This reason was indicated by as much as 68.9% of the survey participants (the Time and Lost rhythm responses were grouped together). Other significant factors influenc-ing participants' MOOC withdrawal decisions were mainly related to the attractiveness and suitability, or lack thereof, for a given student. Poor time management being the main factor causing high MOOC withdrawal rate is not surprising. Temporal dimen-sions set the conditions in which people have to operate. The amount of time available each day is inelastic. Time can be neither transferred nor stored. A substantial part of it must be dedicated to sleep. What is more, scaling the amount of time available a day at the expense of sleep is ineffective. Sleep deprivation has been proven to affect cognitive functions [20, 2] and subsequently memory consol-idation and recall [35, 3, 38]. Moreover, such deprivation may cause serious health problems [12, 6, 27]. Simultane-ously, the pace of life is constantly accelerating [40]. People maintain increasingly busy lives, which makes time one of the most prominent constraints they have to deal with.
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