MOOCs (Massive Open Online Courses) have changed the way in which OER (Open Educational Resources) 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 self-regulated 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 self-report 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.

Learners 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, self-regulated 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, the authors 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 they are grounded. The results reveal that high self-regulators 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.

The emergence of Large Language Models (LLMs) has marked a significant change in education. The appearance of these LLMs and their associated chatbots has yielded several advantages for both students and educators, including their use as teaching assistants for content creation or summarisation. This paper aims to evaluate the capacity of LLMs chatbots to provide feedback on student exercises in a university programming course. The complexity of the programming topic in this study (concurrency) makes the need for feedback to students even more important. The authors conducted an assessment of exercises submitted by students. Then, ChatGPT (from OpenAI) and Bard (from Google) were employed to evaluate each exercise, looking for typical concurrency errors, such as starvation, deadlocks, or race conditions. Compared to the ground-truth evaluations performed by expert teachers, it is possible to conclude that none of these two tools can accurately assess the exercises despite the generally positive reception of LLMs within the educational sector. All attempts result in an accuracy rate of 50%, meaning that both tools have limitations in their ability to evaluate these particular exercises effectively, specifically finding typical concurrency errors.

This paper presents a model for quality-of-service (QoS)-aware service composition in distributed systems with real-time and fault-tolerance requirements. This model can be applied in application domains like, for example, remote monitoring, control and surveillance. Classic approaches to real-time systems do not provide the flexibility and fault-tolerance required in new emerging environments that need to combine a high degree of dynamism with temporal predictability. Our approach addresses these new challenges by combining concepts from the service oriented paradigm and distributed real-time systems. We propose a concrete system model based on a holistic time-triggered-based approach for design and configuration. Based on this model, we propose two algorithms for the composition of QoS-aware service-based applications with temporal requirements: an exhaustive algorithm that computes the optimal service combination in terms of a figure of merit, suitable for offline composition; and an improved algorithm based on heuristics and partial figures of merit, suitable for online composition. Experimental results show that the latter reduces dramatically the number of combinations explored with a minimal degradation in the quality of the solution, making it feasible for online execution in dynamic environments.

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.

The emergence of MOOCs has boosted the use of educational technology in all possible contexts. How can MOOC affordances, such as rich multimedia content, interactive exercises, and social networks, be harnessed in face-to-face education? In this paper, we present some experiences carried out at Universidad Carlos III de Madrid that combine MOOC-like content in on-campus courses. We believe that this is the most promising and useful way to use the affordances of technology in education.