Quality in MOOCs: Surveying the Terrain

The purpose of this review is to identify quality measures and to highlight some of the tensions surrounding notions of quality, as well as the need for new ways of thinking about and approaching quality in MOOCs. It draws on the literature on both MOOCs and quality in education more generally in order to provide a framework for thinking about quality and the different variables and questions that must be considered when conceptualising quality in MOOCs. The review adopts a relativist approach, positioning quality as a measure for a specific purpose. The review draws upon Biggs’s (1993) 3P model to explore notions and dimensions of quality in relation to MOOCs — presage, process and product variables — which correspond to an input–environment–output model. The review brings together literature examining how quality should be interpreted and assessed in MOOCs at a more general and theoretical level, as well as empirical research studies that explore how these ideas about quality can be operationalised, including the measures and instruments that can be employed. What emerges from the literature are the complexities involved in interpreting and measuring quality in MOOCs and the importance of both context and perspective to discussions of quality

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

EU–originated MOOCs, with focus on multi- and single-institution platforms

No abstract available

Characterization of Highly Active Teacher Learners’ Participation and TPACK Knowledge While Engaging in a Teaching Mathematics with Technology MOOC for Mathematics Educators

As the demand for integrating educational technologies within the mathematics teaching curriculum increases, there is a growing need for teachers to develop the competencies and skills required for effective technology integration into their teaching practices. Massive Open Online Courses for Educators (MOOC-Eds) offer teachers opportunities for professional development. Consequently, it is worthwhile to explore the impact of active participation in these professional learning courses. The purpose of this study was to gain a deeper understanding of the distinct types of knowledge teachers gained from active participation in the Teaching Mathematics with Technology (TMT) MOOC-Ed using discussion forums as a space to assess teacher learning. A concurrent embedded mixed methods design (QUAN + QUAL) was employed for this study. Both quantitative and qualitative data was collected and analyzed separately then data was mixed for joint analysis. Two theoretical frameworks were employed to frame this study and support data collection, analysis, and interpreting results. The Productive Online Discussion Model served as an a priori coding frame employed to analyze the dispositions and learner actions of the discussion forum contributions of active teacher learners. A pre- and post- TPACK survey measuring technological knowledge (TK), pedagogical knowledge (PK), content knowledge (CK), and technological pedagogical content knowledge (TPACK) was administered to evaluate active teacher learners change in knowledge before and after the MOOC-Ed experience. Qualitative results from the study indicated that overall, discussions to comprehend occurred most frequently in the discussion forums. Results also showed the frequency of forum contributions categorized as discussing to critique, construct knowledge, and share improved understanding increased during the MOOC-Ed while discussions to comprehend decreased. Quantitative results showed statistically significant growth with large effect size from pre- to post- survey in the TPACK domain for active and highly active teacher learners. Teachers reported the greatest effect on their professional learning experience was increased knowledge of combining pedagogical techniques with technological tools and their content knowledge to teach student-centered lessons. Integrated results indicated that there was a meaningful relationship between highly active teacher learners TPACK growth and their distinct forum contributions that sought to critique, construct knowledge, and share improved understanding. Implications for research emphasize the importance of understanding the different contexts in which teachers teach and designing online courses to meet their diverse learning needs using research-based principles. Additionally, teachers of online learning should consider implementing authentic tasks that contain relevant content, discussion forum questioning that elicits higher order thinking, and opportunities for reflection. Future research is suggested in the areas of employing language technologies (i.e., text mining tools) to explore online interactions and gain insight into user satisfaction and increasing learner engagement and examining to impact of duration of how duration affects participation and attrition rates in professional learning courses

Learning to code in class with MOOCs: Process, factors and outcomes

Problem: Python became the most popular programming language in recent years, beating Java, the programming language still widely used as the main programming language in many undergraduate degrees on computer science related areas. Students from those degrees often do not get Python in their syllabus, but the job market is demanding it increasingly. Objective: To assess if learning a new programming language by following a MOOC is feasible in a fully dedicated mode and allows achieving a learning outcome comparable to the traditional in-class learning process. Proposal: Students from undergraduate degrees lacking Python skills followed a dedicated and intensive learning process on that language based on an in-class MOOC. The latter is suitable for students with some background in programming, as is the case, allowing a faster learning pace. Participants’ subjective perception of the corresponding workload was monitored. Validation: A programming contest, using an automatic judge, was used as a validation for this proposal. Two groups of students participated: those from three degrees lacking Python, which followed the proposed MOOC (experimental group), and those from the degree that includes Python programming, which had a traditional in-class learning process (control group). Conclusions: The experiment results were analysed and it was inferred that the proposed in-class MOOC learning approach is as effective as the traditional learning approach. Furthermore, it was identified that the students’ average grades obtained in the previous programming courses taken as part of their degree’s syllabus and the number of MOOC modules finished in the context of this experiment directly influence the number of points obtained in the contest.Problema: Nos últimos anos, Python tornou-se a linguagem de programação mais popular, ultrapassando o Java, que continua a sermuito usada como principal linguagem de programação em muitas licenciaturas relacionadas com informática. Estas licenciaturas acabam muitas vezes por não oferecer esta competência aos estudantes, no entanto o mercado de trabalho procura-a cada vez mais. Objectivo: Avaliar a possibilidade de aprender uma nova linguagem de programação através de um MOOC num regime de total dedicação. E por fim, perceber se este permite obter resultados comparáveis ao ensino tradicional. Proposta: Os estudantes com falta de conhecimentos de Python realizaram um processo de aprendizagem intensivo desta linguagem através de um MOOC em sala de aula. Este último é adequado a estudantes com alguns conhecimentos de programação, permitindo assim um ritmo mais rápido de aprendizagem. A perceção subjetiva dos participantes sobre a respetiva carga de trabalho foi monitorizada. Validação: Realização de um concurso de programação recorrendo a um juiz automático. Dois grupos de estudantes participaram neste concurso: estudantes das 3 licenciaturas sem conhecimentos de Python, que realizaram o MOOC (grupo experimental), e os estudantes da licenciatura que inclui Python e que teve uma aprendizagem tradicional (grupo de controlo). Conclusões: Os resultados deste experimento foram analisados e inferiu-se que a aprendizagem de um MOOC em sala de aula é tão eficaz quanto o ensino tradicional. Para além disso, foi também verificado que a média de notas dos estudantes obtida nas unidades curriculares de programação que já frequentaram no seu curso e o número de módulos feitos no MOOC no contexto desta experiência influenciam diretamente os pontos obtidos no concurso de programação

Influence of employer support for professional development on MOOCs enrolment and completion: Results from a cross-course survey

Although the potential of open education and MOOCs for professional development is usually recognized, it has not yet been explored extensively. How far employers support non-formal learning is still an open question. This paper presents the findings of a survey-based study which focuses on the influence of employer support for (general) professional development on employees’ use of MOOCs. Findings show that employers are usually unaware that their employees are participating in MOOCs. In addition, employer support for general professional development is positively associated with employees completing MOOCs and obtaining certificates for them. However, the relationship between employer support and MOOC enrollment is less clear: workers who have more support from their employers tend to enroll in either a low or a high number of MOOCs. Finally, the promotion of a minimum of ICT skills by employers is shown to be an effective way of encouraging employee participation in the open education ecosystem.JRC.J.3-Information Societ

Digital Pedagogy, New Media Literacies and Open Educational Resources. Exploring the Impact of Digital Resources in Higher Education.

Mastergradsoppgave i digital kulturMAHF-DIKULDIKULT35