Investigating Variation in Learning Processes in a FutureLearn MOOC

Studies on engagement and learning design in Massive Open Online Courses (MOOCs) have laid the groundwork for understanding how people learn in this relatively new type of informal learning environment. To advance our understanding of how people learn in MOOCs, we investigate the intersection between learning design and the temporal process of engagement in the course. This study investigates the detailed processes of engagement using educational process mining (EPM) in a FutureLearn science course (N = 2086 learners) and applying an established taxonomy of learning design to classify learning activities. The analyses were performed on three groups of learners categorised based upon their clicking behaviour. The process-mining results show at least one dominant pathway in each of the three groups, though multiple popular additional pathways were identified within each group. All three groups remained interested and engaged in the various learning and assessment activities. The findings from this study suggest that in the analysis of voluminous MOOC data there is value in first clustering learners and then investigating detailed progressions within each cluster that take the order and type of learning activities into account. The approach is promising because it provides insight into variation in behavioural sequences based on learners’ intentions for earning a course certificate. These insights can inform the targeting of analytics-based interventions to support learners and inform MOOC designers about adapting learning activities to different groups of learners based on their goals

Supporting professional learning in a massive open online course

Professional learning, combining formal and on the job learning, is important for the development and maintenance of expertise in the modern workplace. To integrate formal and informal learning, professionals have to have good self-regulatory ability. Formal learning opportunities are opening up through massive open online courses (MOOCs), providing free and flexible access to formal education for millions of learners worldwide. MOOCs present a potentially useful mechanism for supporting and enabling professional learning, allowing opportunities to link formal and informal learning. However, there is limited understanding of their effectiveness as professional learning environments. Using self-regulated learning as a theoretical base, this study investigated the learning behaviours of health professionals within Fundamentals of Clinical Trials, a MOOC offered by edX. Thirty-five semi-structured interviews were conducted and analysed to explore how the design of this MOOC supported professional learning to occur. The study highlights a mismatch between learning intentions and learning behaviour of professional learners in this course. While the learners are motivated to participate by specific role challenges, their learning effort is ultimately focused on completing course tasks and assignments. The study found little evidence of professional learners routinely relating the course content to their job role or work tasks, and little impact of the course on practice. This study adds to the overall understanding of learning in MOOCs and provides additional empirical data to a nascent research field. The findings provide an insight into how professional learning could be integrated with formal, online learning

MOOCs as part of the university curriculum: A case study

Massive Open Online Courses (MOOCs) sind seit 2012 ein fester Bestandteil der Bildungslandschaft. In den letzten zehn Jahren haben sie einerseits eine massive Entwicklung erfahren, die mit dem Aufkommen von Plattformen wie Coursera, edX, Udemy oder FutureLearn verbunden ist. Gleichzeitig ist jedoch klar geworden, dass sie nicht als Ersatz für die traditionelle formale Universitätsausbildung angesehen werden können. Am Fachbereich für Informationsstudien und Bibliothekswesen der Masaryk-Universität werden MOOCs den Studierenden als Teil eines bestimmten Kurses angeboten, in dem sie Unterstützung und Feedback erhalten. Das Lernen ist auch mit Credits verbunden, was die Motivation der Studierenden, den Kurs zu absolvieren, erhöht. Die Forschung wird mit Daten aus Fragebögen in der ersten Woche und am Ende des Kurses arbeiten (n=18). Auf der Grundlage der Daten werden wir Erkenntnisse für die Durchführung anderer ähnlicher Kurse anbieten. Die Unterstützung durch die Universität in Form von Motivation und einem Gefühl der Sicherheit ist entscheidend. Die Studierenden weisen hohe Abschlussquoten auf, wenn sie den Kurs als Teil ihres Lehrplans belegen. Andererseits nennen sie ihre Unfähigkeit, gut mit der Zeit umzugehen und ihre Aufgaben zu organisieren, als ein wesentliches Hindernis.Massive Open Online Courses (MOOCs) have been widely part of the educational landscape since 2012. Over the last decade, they have seen, on the one hand, a massive development associated with the emergence of platforms such as Coursera, edX, Udemy or FutureLearn. Still, at the same time, it has become clear that they cannot be considered as a substitute for traditional formal university education. At the Department of Information Studies and Library Science at Masaryk University, MOOCs are offered to students as part of a particular course in which they receive support and feedback. The learning is also linked to credits, which increases students' motivation to complete the course. The research will work with data from questionnaires in the first week and at the end of the course (n=18). The research will offer insights for running other similar courses based on the data. University support in terms of motivation and a sense of security is crucial. Students show high completion rates if they study the course as part of their curriculum. On the other hand, they name their inability to work well with time and organise their tasks as a significant barrier

Inclusiveness in Online Learning Designs: Geo-Cultural and Socioeconomic Perspectives

Initially, there was a strong expectation amongst some in the online learning and teaching community that free, widely advertised, massive, open, online courses (MOOCs) would potentially address the global disparity in educational attainment. However, it turned out that most popular MOOC providers and the majority of active learners still originate from developed countries, mainly in the Global North. Moreover, how successful online learners are in achieving their learning goals found to vary along geo-cultural and socioeconomic dimensions as well as with learning design features. Despite diverse enrolments, most MOOCs adopt a one-size-fits-all design that presents the same set and sequence of learning activities to all learners. This PhD project firstly sets out to study the role of demographic contexts in success in online learning using state of the art predictive modelling methods and data from four large online courses. Then to evaluate the potential link between learners’ geo-cultural and socioeconomic contexts and their successful progression. In total around 60,000 learners from ten courses were included in the analyses. Secondly, the research moves on to study how the learning designs can be adapted at scale in various contexts to improve learners’ persistence. The research leveraged data from the largest MOOC platform in Europe, FutureLearn. In addition, the qualitative data were collected using semi-structured interviews and artefact-mediated questions. The analysis methods included a broad range of algorithms primarily affiliated with Learning Analytics (LA) and Educational Data Mining (EDM), such as decision trees, sequence mining, and cross-validated interactions in survival analysis. Finally, the research investigates the contextual differences in MOOC learners’ perception about various elements of learning design. Therefore, the final mixed-method study used an innovative approach and combined a qualitative method (thematic analysis) with sentiment mining. Overall, the research clearly demonstrated that in comparison to subgroup/interaction analyses, an overall analysis of online learning data can mask geo-cultural and socioeconomic heterogeneity in the correlations between learning design factors and learner persistence. Consequently, overarching data analysis results primarily reflect the behavioural patterns of the largest subgroup, which can stand in contrast to patterns of other, smaller subgroups. Suppose overall data analysis findings are used to guide course design and iterative improvement. In that case, it can lead to improved outcomes for the majority group while leaving behind members of underrepresented groups. This research has therefore made a valuable contribution in solving part of the jigsaw and outlining new directions for the future research as well as highlighting the broader implications that go beyond the domain of learning technologies

Large scale analytics of global and regional MOOC providers: Differences in learners' demographics, preferences, and perceptions

[EN] Massive Open Online Courses (MOOCs) remarkably attracted global media attention, but the spotlight has been concentrated on a handful of English-language providers. While Coursera, edX, Udacity, and FutureLearn received most of the attention and scrutiny, an entirely new ecosystem of local MOOC providers was growing in parallel. This ecosystem is harder to study than the major players: they are spread around the world, have less staff devoted to maintaining research data, and operate in multiple languages with university and corporate regional partners. To better understand how online learning opportunities are expanding through this regional MOOC ecosystem, we created a research partnership among 15 different MOOC providers from nine countries. We gathered data from over eight million learners in six thousand MOOCs, and we conducted a large-scale survey with more than 10 thousand participants. From our analysis, we argue that these regional providers may be better positioned to meet the goals of expanding access to higher education in their regions than the better-known global providers. To make this claim we highlight three trends: first, regional providers attract a larger local population with more inclusive demographic profiles; second, students predominantly choose their courses based on topical interest, and regional providers do a better job at catering to those needs; and third, many students feel more at ease learning from institutions they already know and have references from. Our work raises the importance of local education in the global MOOC ecosystem, while calling for additional research and conversations across the diversity of MOOC providers.We would like to thank support from the MIT-SPAIN program sponsored by "la Caixa" Foundation SEED FUND. Jose A. Ruiperez-Valiente acknowledges support from the Spanish Ministry of Science and Innovation through the Juan de la Cierva Incorporacion program (IJC2020-044852-I). Xitong Li acknowledges funding support from the French National Research Agency (ANR) [Grants ANR AAPG iMOOC-18-CE28-0020-01 and Investissements d'Avenir LabEx Ecodec Grant ANR-11-LABX-0047].Ruipérez-Valiente, JA.; Staubitz, T.; Jenner, M.; Halawa, S.; Zhang, J.; Despujol, I.; Maldonado-Mahauad, J.... (2022). Large scale analytics of global and regional MOOC providers: Differences in learners' demographics, preferences, and perceptions. Computers & Education. 180:1-17. https://doi.org/10.1016/j.compedu.2021.10442611718

Predicting Paid Certification in Massive Open Online Courses

Massive open online courses (MOOCs) have been proliferating because of the free or low-cost offering of content for learners, attracting the attention of many stakeholders across the entire educational landscape. Since 2012, coined as “the Year of the MOOCs”, several platforms have gathered millions of learners in just a decade. Nevertheless, the certification rate of both free and paid courses has been low, and only about 4.5–13% and 1–3%, respectively, of the total number of enrolled learners obtain a certificate at the end of their courses. Still, most research concentrates on completion, ignoring the certification problem, and especially its financial aspects. Thus, the research described in the present thesis aimed to investigate paid certification in MOOCs, for the first time, in a comprehensive way, and as early as the first week of the course, by exploring its various levels. First, the latent correlation between learner activities and their paid certification decisions was examined by (1) statistically comparing the activities of non-paying learners with course purchasers and (2) predicting paid certification using different machine learning (ML) techniques. Our temporal (weekly) analysis showed statistical significance at various levels when comparing the activities of non-paying learners with those of the certificate purchasers across the five courses analysed. Furthermore, we used the learner’s activities (number of step accesses, attempts, correct and wrong answers, and time spent on learning steps) to build our paid certification predictor, which achieved promising balanced accuracies (BAs), ranging from 0.77 to 0.95. Having employed simple predictions based on a few clickstream variables, we then analysed more in-depth what other information can be extracted from MOOC interaction (namely discussion forums) for paid certification prediction. However, to better explore the learners’ discussion forums, we built, as an original contribution, MOOCSent, a cross- platform review-based sentiment classifier, using over 1.2 million MOOC sentiment-labelled reviews. MOOCSent addresses various limitations of the current sentiment classifiers including (1) using one single source of data (previous literature on sentiment classification in MOOCs was based on single platforms only, and hence less generalisable, with relatively low number of instances compared to our obtained dataset;) (2) lower model outputs, where most of the current models are based on 2-polar iii iv classifier (positive or negative only); (3) disregarding important sentiment indicators, such as emojis and emoticons, during text embedding; and (4) reporting average performance metrics only, preventing the evaluation of model performance at the level of class (sentiment). Finally, and with the help of MOOCSent, we used the learners’ discussion forums to predict paid certification after annotating learners’ comments and replies with the sentiment using MOOCSent. This multi-input model contains raw data (learner textual inputs), sentiment classification generated by MOOCSent, computed features (number of likes received for each textual input), and several features extracted from the texts (character counts, word counts, and part of speech (POS) tags for each textual instance). This experiment adopted various deep predictive approaches – specifically that allow multi-input architecture - to early (i.e., weekly) investigate if data obtained from MOOC learners’ interaction in discussion forums can predict learners’ purchase decisions (certification). Considering the staggeringly low rate of paid certification in MOOCs, this present thesis contributes to the knowledge and field of MOOC learner analytics with predicting paid certification, for the first time, at such a comprehensive (with data from over 200 thousand learners from 5 different discipline courses), actionable (analysing learners decision from the first week of the course) and longitudinal (with 23 runs from 2013 to 2017) scale. The present thesis contributes with (1) investigating various conventional and deep ML approaches for predicting paid certification in MOOCs using learner clickstreams (Chapter 5) and course discussion forums (Chapter 7), (2) building the largest MOOC sentiment classifier (MOOCSent) based on learners’ reviews of the courses from the leading MOOC platforms, namely Coursera, FutureLearn and Udemy, and handles emojis and emoticons using dedicated lexicons that contain over three thousand corresponding explanatory words/phrases, (3) proposing and developing, for the first time, multi-input model for predicting certification based on the data from discussion forums which synchronously processes the textual (comments and replies) and numerical (number of likes posted and received, sentiments) data from the forums, adapting the suitable classifier for each type of data as explained in detail in Chapter 7

MOOCs for Development? A Study of Indian Learners and their Experiences in Massive Open Online Courses

The study outlined in this thesis provides an account of the demographics, motivations and experiences of Indian learners in Massive Open Online Courses (MOOCs) comparing the UK-based platform FutureLearn and the Indian platform NPTEL (The National Programme on Technology Enhanced Learning). A sequential mixed-methods approach was adopted. A web-based survey (n=2373) was used to collect demographical data and evidence of respondents’ perceptions about their motivations for taking a MOOC, their learning experiences, and any challenges they may have faced while taking a MOOC. The survey phase was followed by 30 semi-structured interviews with learners from both platforms, adding a rich level of qualitative data to the study, revealing the varied experiences and backgrounds of MOOC learners from India. Analysis of the collected data suggests that learners from India tend to be male, younger, more likely to be in formal education, and more educated than participants featured in many existing studies on MOOC learner demographics. Further, the current study outlined several demographic and motivational differences between learners on FutureLearn and NPTEL, likely to be attributable to the distinct objectives of the two platforms. A more in-depth exploration of learners’ experiences suggested that a diverse group of people, particularly on the FutureLearn platform, are using MOOCs to learn more about areas of personal interest, and, in some cases, using FutureLearn resources to assist in their teaching practice. Conversely, learners on the NPTEL platform, who tended to experience more technical challenges such as connectivity issues, were using MOOCs as a supplement to their formal studies, to make up for some of the systemic lack of quality education in many Indian universities. This thesis suggests that educational technology, in the form of MOOCs, might not necessarily be widening participation in education in a Global South context like India. However, it offers a unique insight into the experiences of learners from India, and provides practical recommendations on how best to serve the needs of the varied Indian learners that make use of MOOCs