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MOOCs (Massive Open Online Courses) have changed the way in which OER (Open EducationalResources) are bundled by teachers and consumed by learners. MOOCs represent an evolution towards the production and offering of structured quality OER. Many institutions that were initially reluctant to providing OER have, however, joined the MOOC wave. Nevertheless, MOOCs detractors strongly criticize their high dropout rates. The dropout rate is a commonly accepted metric of success for traditional education, but it may not be as suitable when dealing with OER, in general, and with MOOCs, in particular, since learners' motivations to take a course are very diverse, and certain selfregulated learning strategies are required to tackle the lack of personalized tutoring and keep pace in the course. This paper presents an empirical study on the motivation and learning strategies of MOOC learners. Six thousand three hundred and thirty-five learners from 160 countries answered a selfreport 7-point Likert-type questionnaire based on the Motivated Strategies for Learning Questionnaire (MSLQ) as part of a MOOC titled Introduction to Programming with Java. Results indicate that learners were highly motivated and confident to do well in the course. Learning strategies, however, can be improved, especially regarding time management.

SUMMARYService-based approach has been successfully applied to distributed environments, modelling them as pieces of functionality that exchange information by means of messages in order to achieve a common goal. The advantages of this approach can be also be applied to distributed real-time systems, increasing their flexibility and allowing the creation of new brand applications from existing services in the system. If this is an online process, then time-bounded composition algorithms are needed to not jeopardize the performance of the whole system. Different composition algorithms are studied and proposed, two of them optimal and another two based on heuristics. This paper presents an analytical solution that selects, depending on the structure of the application and on the load of the whole system, the most suitable composition algorithm to be executed in order to obtain a composed application in bounded time.

The learning process in a MOOC (Massive Open Online Course) can be improved from knowing in advance learners' grades on different assignments.This would be very useful to detect problems with enough time to take corrective measures. In this work, the aim is to analyse how different course scores can be predicted, what elements or variables affect the predictions, and how much and in which way it is possible to anticipate scores. To do that, data from a MOOC about Java programming have been used. Results show the importance of indicators over the algorithms and that forum-related variables do not add power to predict grades, unlike previous scores. Furthermore, the type of task can vary the results. Regarding the anticipation, it was possible to use data from previous topics but with worse performance, although values were better than those obtained in the first seven days of the current topic.

Self-regulated learning in MOOCs: Lessons learned from a literature reviewLearners in Massive Open Online Courses (MOOCs) are required to be autonomous during their learning process, and thus, they need to self-regulate their learning to achieve their goals. According to existing literature, Selfregulated learning (SRL) research in MOOCs is still scarce. More studies which build on past works regarding SRL in MOOCs are required, as well as literature reviews that help to identify the main challenges and future research directions in relation to this area. In this paper, we present the results of a systematic literature review on SRL in MOOCs, covering all the related papers published until the end of 2017. The papers considered in this review include real experiences with at least a MOOC (other learning scenarios sometimes claimed as MOOCs, such as blended courses, or online courses with access restrictions, are out of the scope of this analysis). Most studies on SRL in MOOCs share some common features: they are generally exploratory, based on one single MOOC and tend not to specify in which SRL model are grounded. The results reveal that high selfregulators engage in non-linear navigation and approach MOOCs as an informal learning opportunity. In general, they prefer setting specific goals based on knowledge development and control their learning through assignments.