A Social Learning Space Grid for MOOCs: Exploring a FutureLearn Case

Collaborative and social engagement promote active learning through knowledge intensive interactions. Massive Open Online Courses (MOOCs) are dynamic and diversified learning spaces with varying factors like flexible time frames, student count, demographics requiring higher engagement and motivation to continue learning and for designers to implement novel pedagogies including collaborative learning activities. This paper looks into available and potential collaborative and social learning spaces within MOOCs and proposes a social learning space grid that can aid MOOC designers to implement such spaces, considering the related requirements. Furthermore, it describes a MOOC case study incorporating three collaborative and social learning spaces and discusses challenges faced. Interesting lessons learned from the case give an insight on which spaces to be implemented and the scenarios and factors to be considered

What can innovation in engineering education do for you as a student and what can you do as a student for Innovation in engineering education?

Innovation in education in general and innovation in engineering education in particular must be supported by properly collected and analyzed data to guide decisionmaking processes. Today it is possible to collect data from many more stakeholders (not just students), and also to collect much more data from each stakeholder. Nevertheless, low-level data collected by monitoring the interactions of the multiple stakeholders with learning platforms and other computing systems must be transformed into meaningful high-level indicators and visualizations that guide decision-making processes. The aim of this paper is to discuss some notable trends in data-driven innovation in engineering education, including 1) improvement of educational content; 2) improvement of learners’ social interactions; 3) improvement of learners’ self-regulated learning skills; and 4) prediction of learners’ behavior. However, there are also significant risks associated with data collection and processing, such as privacy, transparency, biases, misinterpretations, etc., which must also be taken into account, and require creating specialized units and training the personnel in data management.La innovación en la educación, en general, y la innovación en la educación de ingeniería, en particular, deben estar respaldadas por datos, debidamente recopilados y analizados para guiar los procesos de toma de decisiones. Hoy es posible recopilar datos de muchos grupos de interés (no solo estudiantes), y también recopilar muchos más datos de cada interesado. Sin embargo, los datos de bajo nivel recopilados al monitorear las interacciones de los múltiples interesados con las plataformas de aprendizaje y otros sistemas informáticos deben transformarse en indicadores y visualizaciones de alto nivel que guíen los procesos de toma de decisiones. El objetivo de este documento es discutir algunas tendencias notables en la innovación basada en datos en la educación de ingeniería, que incluyen: 1) mejora del contenido educativo; 2) mejora de las interacciones sociales de los alumnos; 3) mejora de las habilidades de aprendizaje autorreguladas de los alumnos; y 4) predicción del comportamiento de los alumnos. Sin embargo, también existen riesgos significativos asociados con la recopilación y el procesamiento de datos, que incluyen privacidad, transparencia, sesgos, malas interpretaciones, etc., que también deben tenerse en cuenta y que requieren la creación de unidades especializadas y la capacitación del personal en la gestión de datos

What Can You Do with Educational Technology that is Getting More Human?

Proceeding of: Tenth IEEE Global Engineering Education Conference (EDUCON 2019), 9-11 April, 2019, Dubai, UAE.Technology is advancing at an ever-increasing speed. The backend capabilities and the frontend means of interaction are revolutionizing all kinds of applications. In this paper, we analyze how the technological breakthroughs seem to make educational interactions look smarter and more human. After defining Education 4.0 following the Industry 4.0 idea, we identify the key breakthroughs of the last decade in educational technology, basically revolving around the concept cloud computing, and imagine a new wave of educational technologies supported by machine learning that allows defining educational scenarios where computers interact and react more and more like humans.The authors would like to primarily acknowledge the support of the eMadrid Network, which is funded by the Madrid Regional Government (Comunidad de Madrid) with grant No. S2018/TCS-4307. This work has also received partial support from FEDER/Ministerio de Ciencia, Innovación y Universidades-Agencia Estatal de Investigación through Project RESET (TIN2014-53199-C3-1-R) and Project Smartlet (TIN2017-85179-C3-1-R). Partial support has also been received from the European Commission through Erasmus+ projects, in particular, projects COMPASS (Composing Lifelong Learning Oppor-tunity Pathways through Standards-based Services, 2015-1-EL01-KA203-014033), COMPETEN-SEA (Capacity to Organize Massive Public Educational Opportunities in Universities in Southeast Asia, 574212-EPP-1-2016-1-NL-EPPKA2-CBHE-JP), LALA (Building Capacity to use Learning Analytics to Improve Higher Education in Latin America, 586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP), and InnovaT (Innovative Teaching across Continents: Universities from Europe, Chile, and Peru on an Expedition, 598758-EPP-1-2018-1-AT-EPPKA2-CBHE-JP). UNESCO Chair "Scalable Digital Education for All" at Universidad Carlos III de Madrid is also gratefully acknowledged.Publicad

Les MOOCs en période de pandémie : un outil pour les enseignants ? Retour d'expériences des MOOCs de l'ULiège (Belgique) et perspectives

Lorsque des mesures de confinement et de fermeture des établissements d'enseignement ont été décidées, en mars 2020, l’ULiège a décidé (comme d’autres universités), via ses MOOCs, de contribuer à l’effort de crise et au partage des connaissances de différentes manières ; soit en repoussant la date de clôture de MOOCs en cours de diffusion, soit en donnant accès uniquement aux ressources pédagogiques (« mode archivé ouvert » ), ou encore en proposant un accompagnement pédagogique minimum mais un accès complet au forum et aux activités pédagogiques permettant l’obtention d’une attestation de suivi (« mode session animée »). Ainsi, le 31 mars 2020, l’ULiège annonçait la réouverture de 5 MOOCs en plus des 10 MOOCs déjà disponibles sur la plateforme FUN (France Université Numérique) en sessions « régulières ». S’il ne fait aucun doute que le confinement imposé par les gouvernements de nombreux pays francophones a littéralement fait exploser le nombre d’inscriptions, on peut toutefois s’interroger sur l’utilisation concrète des MOOCs par les apprenants. Pour cette étude, nous avons souhaité nous intéresser plus particulièrement à l'usage des MOOCs par les enseignants du secondaire et du supérieur inscrits aux MOOCs ULiège durant la période de confinement. En effet, beaucoup d’entre eux ont dû mettre en place rapidement un « enseignement à distance » (visioconférences, travaux à réaliser à la maison et feedbacks communiqués par e-mail ou via une plateforme « en ligne » etc.), que l'on connaît mieux aujourd'hui sous le terme de "Emergency Remote Teaching". Nos questions de recherche ont été les suivantes : Quel est le profil des enseignants inscrits aux MOOCs ULiège pendant la période de confinement ? Quelles sont les raisons principales de leur inscription ? Ont-ils utilisé le MOOC comme moyen de mettre à jour leurs connaissances ? Ont-ils utilisé les MOOCs comme « substitut » de leurs cours ? Et dans ce cas, ont-ils accompagné leurs apprenants ? Ont-ils intégré certains éléments des MOOCs (vidéos, schémas, chapitres particuliers) comme illustration et/ou support de leur cours « à distance » ? Les résultats de notre étude laissent penser que les MOOCS ULiège ont servi principalement au développement professionnel des enseignants, notamment dans l’acquisition de compétences et/ou connaissances spécialisées liées à la discipline enseignée. L’utilisation des MOOCs au sein même des programmes de cours reste encore marginale. Il semble y avoir un manque d’information et de connaissance sur les droits d’utilisation des ressources des MOOCs, ou tout simplement des barrières technologiques ou organisationnelles (accessibilité réduite des MOOCs, planification inadaptée par rapport au programme des cours, difficulté pour un enseignant extérieur à l’institution qui a produit le MOOC de suivre le parcours de ses élèves au sein même du cours ...)

Sentiment analysis in MOOCs: a case study

Proceeding of: 2018 IEEE Global Engineering Education Conference (EDUCON2018), 17-20 April, 2018, Santa Cruz de Tenerife, Canary Islands, Spain.Forum messages in MOOCs (Massive Open Online Courses) are the most important source of information about the social interactions happening in these courses. Forum messages can be analyzed to detect patterns and learners' behaviors. Particularly, sentiment analysis (e.g., classification in positive and negative messages) can be used as a first step for identifying complex emotions, such as excitement, frustration or boredom. The aim of this work is to compare different machine learning algorithms for sentiment analysis, using a real case study to check how the results can provide information about learners' emotions or patterns in the MOOC. Both supervised and unsupervised (lexicon-based) algorithms were used for the sentiment analysis. The best approaches found were Random Forest and one lexicon based method, which used dictionaries of words. The analysis of the case study also showed an evolution of the positivity over time with the best moment at the beginning of the course and the worst near the deadlines of peer-review assessments.This work has been co-funded by the Madrid Regional Government, through the eMadrid Excellence Network (S2013/ICE-2715), by the European Commission through Erasmus+ projects MOOC-Maker (561533-EPP-1-2015-1-ESEPPKA2-CBHE-JP), SHEILA (562080-EPP-1-2015-1-BEEPPKA3-PI-FORWARD), and LALA (586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP), and by the Spanish Ministry of Economy and Competitiveness, projects SNOLA (TIN2015-71669-REDT), RESET (TIN2014-53199-C3-1-R) and Smartlet (TIN2017-85179-C3-1-R). The latter is financed by the State Research Agency in Spain (AEI) and the European Regional Development Fund (FEDER). It has also been supported by the Spanish Ministry of Education, Culture and Sport, under a FPU fellowship (FPU016/00526).Publicad

Who are the top contributors in a MOOC? Relating participants' performance and contributions

The role of social tools in massive open online courses (MOOCs) is essential as they connect participants. Of all the participants in an MOOC, top contributors are the ones who more actively contribute via social tools. This article analyses and reports empirical data from five different social tools pertaining to an actual MOOC to characterize top contributors and provide some insights aimed at facilitating their early detection. The results of this analysis show that top contributors have better final scores than the rest. In addition, there is a moderate positive correlation between participants' overall performance (measured in terms of final scores) and the number of posts submitted to the five social tools. This article also studies the effect of participants' gender and scores as factors that can be used for the early detection of top contributors. The analysis shows that gender is not a good predictor and that taking the scores of the first assessment activities of each type (test and peer assessment in the case study) results in a prediction that is not substantially improved by adding subsequent activities. Finally, better predictions based on scores are obtained for aggregate contributions in the five social tools than for individual contributions in each social tool.This work has been partially funded by the Madrid Regional Government eMadrid Excellence Network (S2013/ICE-2715), the Spanish Ministry of Economy and Competitiveness Project RESET (TIN2014-53199-C3-1-R) and the European Erasmus+ projects MOOC-Maker (561533-EPP-1-2015-1-ES-EPPKA2-CBHE-JP) and SHEILA (562080-EPP-1-2015-BE-EPPKA3-PI-FORWARD).Publicad

A collaborative digital pedagogy experience in the tMOOC “Step by step”

This research analysed social MOOCs (sMOOCs), which are characterised by the involvement and the interaction of participants in a model based on intercreativity, with the final objective of transferring knowledge by an agile replicating process. The fieldwork focused on the analysis of the sMOOC “Step by Step” of the European Commission-funded Elearning, Communication and Open-data (ECO) Project, which aims to build and apply an innovative pedagogical model for the the training of e-teachers. This sMOOC reaches out to a specific academic community, providing learners with digital competences in order to transform them in e-teachers. The quantitative analysis was done via an online questionnaire. One of the most significant conclusions, which answers the research questions regarding why and how to make a successful sMOOC, is that the design of collaborative activities increases the involvement of learners with the course and the interaction between participants, independent of age but dependent on area of work. This formative process in turn generates transfer of learning together with the embedded pedagogical transformation in e-teachers. This validates the addition of the transferMOOC (tMOOC) model to the existing typologies of MOOCs

To reward and beyond: Analyzing the effect of reward-basedstrategies in a MOOC

Producción CientíficaDespite the benefits of MOOCs (e.g., open access to education offered by prestigious universities), the low level of student engagement remains as an important issue causing massive dropouts in such courses. The use of reward-based gamification strategies is one approach to promote student engagement and prevent dropout. However, there is a lack of solid empirical studies analyzing the effects of rewards in MOOC environments. This paper reports a between-subjects design study conducted in a MOOC to analyze the effects of badges and redeemable rewards on student retention and engagement. Results show that the implemented reward strategies had not significant effect on student retention and behavioral engagement measured through the number of pageviews, task submissions, and student activity time. However, it was found that learners able to earn badges and redeemable rewards participated more in gamified tasks than those learners in the control group. Additionally, results reveal that the participants in the redeemable reward condition requested and earned earlier the rewards than those participants in the badge condition. The potential implications of these findings in the instructional design of future gamified MOOCs are also discussed.Ministerio de Ciencia, Innovación y Universidades (projects TIN2017-85179-C3-2-R / TIN2014-53199-C3-2-R)Junta de Castilla y León (project VA257P18)European Commission (project 588438-EPP-1-2017-1-EL- EPPKA2-KA

Creating collaborative groups in a MOOC: a homogeneous engagement grouping approach

Producción CientíficaCollaborative learning can improve the pedagogical effectiveness of MOOCs. Group formation, an essential step in the design of collaborative learning activities, can be challenging in MOOCs given the scale and the wide variety in such contexts. We discuss the need for considering the behaviours of the students in the course to form groups in MOOC contexts, and propose a grouping approach that employs homogeneity in terms of students’ engagement in the course. Two grouping strategies with different degrees of homogeneity are derived from this approach, and their impact to form successful groups is examined in a real MOOC context. The grouping criteria were established using student activity logs (e.g. page-views). The role of the timing of grouping was also examined by carrying out the intervention once in the first and once in the second half of the course. The results indicate that in both interventions, the groups formed with a greater degree of homogeneity had higher rates of task-completion and peer interactions, Additionally, students from these groups reported higher levels of satisfaction with their group experiences. On the other hand, a consistent improvement of all indicators was observed in the second intervention, since student engagement becomes more stable later in the course.Agencia Estatal de Investigación Española - Fondo Europeo de Desarrollo Regional (grants TIN2017-85179-C3-2-R / TIN2014-53199-C3-2-RJunta de Castilla y León - Fondo Europeo de Desarrollo Regional (grant VA257P18)Comisión Europea (grant 588438-EPP-1-2017-1-EL-EPPKA2-KA

Creating collaborative groups in a MOOC: a homogeneous engagement grouping approach

Collaborative learning can improve the pedagogical effectiveness of MOOCs. Group formation, an essential step in the design of collaborative learning activities, can be challenging in MOOCs given the scale and the wide variety in such contexts. We discuss the need for considering the behaviours of the students in the course to form groups in MOOC contexts, and propose a grouping approach that employs homogeneity in terms of students? engagement in the course. Two grouping strategies with different degrees of homogeneity are derived from this approach, and their impact to form successful groups is examined in a real MOOC context. The grouping criteria were established using student activity logs (e.g. page-views). The role of the timing of grouping was also examined by carrying out the intervention once in the first and once in the second half of the course. The results indicate that in both interventions, the groups formed with a greater degree of homogeneity had higher rates of task-completion and peer interactions, Additionally, students from these groups reported higher levels of satisfaction with their group experiences. On the other hand, a consistent improvement of all indicators was observed in the second intervention, since student engagement becomes more stable later in the course