Técnicas de soporte a la flexibilidad funcional en sistemas embarcados distribuidos de tiempo real

Durante la última década, gracias a los avances en diversos campos tecnológicos, se han diversificado el conjunto de entornos en los que es necesario desarrollar sistemas que ofrezcan garantías temporales. Muchos de estos son entornos dinámicos, donde las características de la carga computacional no siempre pueden ser predecibles, y donde ya no es aplicable la aproximación clásica de diseño, habitualmente pesimista, que asegura garantías temporales pero que puede implicar en entornos dinámicos un gasto de recursos prohibitivo. Así, se plantea el reto de adaptar las metodologías empleadas en diferentes niveles arquitecturales a estos nuevos entornos, y explorar nuevas vías y paradigmas que permitan conjugar flexibilidad funcional y dinamismo con predictibilidad temporal. Esta tesis aborda este reto mediante la exploración de la aplicabilidad a sistemas de tiempo real de conceptos propios del paradigma de orientación a servicios, con el fin de ofrecer flexibilidad, y, al mismo tiempo, beneficiarse de algunas de las ventajas que éste ofrece. Para conseguir tal fin, se propone un modelo concreto de sistema basado en una aproximación holística al diseño y configuración, donde las aplicaciones están gobernadas por tiempo. Fijado el modelo de sistema, se propone un modelo de aplicación basada en servicios y se analizan desde diferentes perspectivas las entidades y procesos que estarán presentes en una arquitectura que le dé soporte, diferenciando dos posibles aproximaciones a la composición que influirán en el diseño de dicha arquitectura: estática, que una vez realizada no admite reconfiguraciones, o dinámica, en la cual una aplicación puede reconfigurarse en tiempo de ejecución. Se proponen, además, algoritmos para la composición de aplicaciones, tanto exhaustivos, aplicables en la aproximación estática, como mejorados, con un tiempo de ejecución acotado, apropiados para su empleo en tiempo de ejecución. Finalmente, se realiza la validación del modelo y de las ideas propuestas mediante la implementación de un prototipo sobre un protocolo concreto de comunicaciones de tiempo real, al que se le realizaron pequeñas adaptaciones y sobre el cual se definió una arquitectura adecuada. ____________________________________________During the last decade, due to the advances in several technology fields, the application domains where the development of systems with temporal guarantees is needed has increased. The majority of such domains are dynamic; the characteristics of their computational load cannot always be predicted in advance. Although the classical design approach provides temporal guarantees, it is no longer applicable since it is too pessimistic, and it implies prohibitive resource consumption. Thus, new challenges raise. On one side, it is required to adapt the current methodologies used in different architectural levels to these new environments. Also, it is needed to explore new directions and paradigms that allow combining functional flexibility and dynamism with temporal predictability. The current work addresses these new challenges through the exploration of the applicability of concepts from the service oriented paradigm to distributed real-time systems. The introduction of some of the characteristics of the service oriented paradigm will allow to provide support for dynamic flexibility. Therefore, the current work proposes a concrete system model based on a holistic time-triggered-based approach for design and configuration. Based on this system model, a service-based application model is proposed. Also, it analyses the architectural entities and processes from different points of view, distinguishing between two different architectural design approaches: static and dynamic. The former applies when no reconfigurations at run-time are admitted, and the latter where these reconfigurations are possible. Several application composition algorithms have been proposed: (1) an exhaustive algorithm, applicable to the static approach and (2) an improved algorithm, with bounded execution times, suitable for its usage at run-time. Eventually, to validate the feasibility of the model and the proposed ideas, an architecture has been defined and a prototype of it has been implemented on top of a concrete real-time communications protocol

Lostrego: a distributed stream-based infrastructure for the real-time gathering and analysis of heterogeneous educational data

The quick technological evolution of the last decades has also reached learning environments, where the use of networked computing devices such as laptops, smartphones, tablets, IoT devices, servers, etc. is continuously growing. In particular, those computerized learning environments have the potential to track the activity of teachers and students in them, which enables the development of innovative applications that enrich the learning process by analyzing the collected data. The majority of related work in this field has been centered on batch gathering and analysis of the data. However, in order to integrate more reactive applications, there is a need for an infrastructure that enables the real-time collection and analysis of data in learning environments. Such an infrastructure should be scalable and flexible enough to cope with heterogeneous data coming from different types of learning settings. This paper presents Lostrego, a stream-based, modular, scalable and flexible distributed infrastructure that allows the gathering and analysis of educational data from heterogeneous data sources in a real-time fashion. Lostrego applications are composed by interconnected services that can be reused in different courses. The results of the evaluation of Lostrego in two editions of a computer programming course with 233 students and 384,702 gathered events are also reported.This work was partially funded by: the Spanish Competitiveness and Economy Ministry through projects “RESET UC3M: Reformulando Ecosistemas Escalables Educativos” (TIN 2014 53199 C3 1 R) and “Hermes Smartdriver. Conducción eficiente y procesamiento semántico de la información” (TIN2013 46801 C4 2 R); and by the Community of Madrid through its regional project “eMadrid” (S2013/ICE 2715)

An Algorithm for Peer Review Matching in Massive Courses for Minimising Students' Frustration

Traditional pedagogical approaches are no longer sufficient to cope with the increasing challenges of Massive Open On-line Courses (MOOCs). Consequently, it is necessary to explore new paradigms. This paper describes an exploration of the adaptation of the peer review methodology for its application to MOOCs. Its main goal is to minimise the students' frustration through the reduction of the number of committed students that receive no feedback from their peers. In order to achieve this objective, we propose two algorithms for the peer review matching in MOOCs. Both reward committed students by prioritising the review of their submissions. The first algorithm uses sliding deadlines to minimise the probability of a submission not being reviewed. Our experiments show that it reduces dramatically the number of submissions from committed students that do not receive any review. The second algorithm is a simplification of the former. It is easier to implement and, despite performing worse than the first one, it also improves with respect to the baseline.This work was partially funded by the Spanish Competitiveness and Economy Ministry (Ministerio de Economía y Competitividad) project “EEE – Espacios Educativos Especulares” (TIN2011-28308-C03-01), and by the Madrid regional project “eMadrid” (S2009/TIC-1650)

Course Quality Improvement using Mid-semester Feedback

Quality control mechanisms are becoming more important in higher educational institutions. Student evaluation of teaching is typically used to obtain feedback from students about a learning experience but its effect in the course may take too long. Fast feedback mechanisms, in exchange, look at obtaining feedback in a way that corrective measures can be applied quickly. In this paper, a process is described to obtain feedback from the students about a course, analyse the received results, and identify the most significant aspects. The process has been applied to a course and led to some adjustments that had immediate impact on the course.Work partially funded by the Learn3 project, "Plan Nacional de I+D+I TIN2008-05163/TSI", the Best Practice Network ICOPER (Grant No. ECP-2007-EDU-417007), the Flexo Project "Plan Nacional de Investigación Científica, Desarrollo e Innovación Tecnolóogica" (Ref. TSI-020301-2008-19), and the "Emadrid: Investigación y desarrollo de tecnologías para el e-learning en la Comunidad de Madrid" project (S2009/TIC-1650).Publicad

Analysing self-regulated learning strategies of MOOC learners through self-reported data

Massive open online courses (MOOCs) require registered learners to be autonomous in their learning. Nevertheless, prior research studies showed that many learners lack the necessary self-regulated learning (SRL) skills to succeed in MOOCs. This research study aimed to gain insights into the relationships that exist between SRL and background information from MOOC learners. To this end, a series of three MOOCs on computer programming offered through edX were used to collect self-reported data from learners using an adaptation of the Motivated Strategies for Learning Questionnaire. Results show significant differences in general learning strategies and motivation by continent, prior computing experience and percentage of completed MOOCs. Men reported higher motivation than women, whereas pre-university learners needed further guidance to improve their learning strategies.This work was supported in part by the FEDER/Ministerio de Ciencia, Innovación y Universidades-Agencia Estatal de Investigación, through the Smartlet Project under Grant TIN2017-85179-C3-1-R, and in part by the Madrid Regional Government through the e-Madrid-CM Project under Grant S2018/TCS-4307, a project which is co-funded by the European Structural Funds (FSE and FEDER). Partial support has also been received from the European Commission through Erasmus+ Capacity Building in the Field of Higher Education projects, more specifically through projects LALA, InnovaT and PROF-XXI (586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP), (598758-EPP-1-2018-1-AT-EPPKA2-CBHE-JP), (609767-EPP-1-2019-1-ES-EPPKA2-CBHE-JP). This publication reflects the views only of the authors, and funders cannot be held responsible for any use which may be made of the information contained therein

Uncovering flipped-classroom problems at an engineering course on Systems Architecture through data-driven learning design

Flipped classroom is a student-centered methodology that can help engineering students to acquire the cross-curricular skills demanded by society. However, its effectiveness relies on the commitment of both instructors and students. In particular, this strategy requires students to work on a number of proposed activities before face-to-face classes. Then, in order to follow the most appropriate path in those classes, instructors need a reliable way to know at which degree their students worked on those proposed activities, what issues they encountered while doing them and which concepts need to be reinforced in class. This paper presents a case study of a flipped-classroom undergraduate engineering course. By using data-driven learning design and learning analytics techniques we show that: (1) by delaying their work on the course activities our students actually drove the course towards the traditional approach; (2) despite directly asking students at the beginning of a face-to-face class might seem to be an appropriate way of getting reliable information about their previous work, it may lead instructors to erroneous conclusions; (3) our students were strongly mark- and deadline-oriented, but even a small grade encouraged them to work on the assignments; (4) the gathering and checking of students' learning data before the class can help instructors to tailor the lesson design; and (5) if students did not work on pre-class activities, dedicating a small amount of time of the in-class lesson to explain the most difficult concepts can help students to be more efficient with their work, at the cost of losing some of the spirit of the flipped classroom.This work was partially funded by: the Madrid Regional Government (Comunidad de Madrid), through the eMadrid Network (S2013/ICE-2715); by the Spanish Ministry of Competitiveness and Economy, through projects RESET (TIN2014-53199-C3-1-R) and AUDACity (TIN2016-77158-C4-1-R) and through the thematic network of excellence, SNOLA (TIN2015-71669-REDT); and 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), COMPASS (2015-1-EL01-KA203-014033), and COMPETEN-SEA (574212-EPP-1-2016-1-NL-EPPKA2-CBHE-JP)

Understanding Learners' Motivation and Learning Strategies in MOOCs

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.This work has been co-funded by the Erasmus+ Programme of the European Union, projects MOOC-Maker (561533-EPP-1-2015-1-ES-EPPKA2-CBHE-JP), SHEILA (562080-EPP-1-2015-BE-EPPKA3-PI-FORWARD) and COMPETEN-SEA (574212-EPP-1-2016-1- NL-EPPKA2-CBHE-JP), by the Madrid Regional Government, through the eMadrid Excellence Network (S2013/ICE-2715), and by the Spanish Ministry of Economy and Competitiveness, project RESET (TIN2014-53199-C3-1-R) and fellowships FPDI-2013-17411 and PTQ-15-07505

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

MyLearningMentor: a mobile App to support learners participating in MOOCs

MOOCs have brought a revolution to education. However, their impact is mainly benefiting people with Higher Education degrees. The lack of support and personalized advice in MOOCs is causing that many of the learners that have not developed work habits and self-learning skills give them up at the first obstacle, and do not see MOOCs as an alternative for their education and training. My Learning Mentor (MLM) is a mobile application that addresses the lack of support and personalized advice for learners in MOOCs. This paper presents the architecture of MLM and practical examples of use. The architecture of MLM is designed to provide MOOC participants with a personalized planning that facilitates them following up the MOOCs they enroll. This planning is adapted to learners' profiles, preferences, priorities and previous performance (measured in time devoted to each task). The architecture of MLM is also designed to provide tips and hints aimed at helping learners develop work habits and study skills, and eventually become self-learners.This work has been funded by the Spanish Ministry of Economy and Competitiveness Project TIN2011-28308-C03-01, the Regional Government of Madrid project S2013/ICE-2715, and the postdoctoral fellowship Alliance 4 Universities. The authors would also like to thank Israel Gutiérrez-Rojas for his contributions to the ideas behind MLM and Ricardo García Pericuesta and Carlos de Frutos Plaza for their work implementing different parts of the architecture

Validación por la Comunidad Docente de una Metodología de Aprendizaje Activo para Cursos de Programación

Actas de: III Jornadas de Innovación Educativa en Ingeniería Telemática (JIE). Granada, 28-30 Octubre 2013.En este artículo se presenta y evalúa una metodología para cursos de programación, basada en el aprendizaje activo y el aprendizaje basado en proyectos. Esta metodología se centra en el trabajo continuo, día a día, del alumno. Por un lado, ofrece pautas para que los alumnos organicen su tiempo, promoviendo el autoaprendizaje y el trabajo individual. Por otro, durante el proyecto los alumnos desarrollan sus capacidades de trabajo en equipo, fomentando el desarrollo de competencias transversales como el aprendizaje colaborativo. La metodología descrita se ha aplicado durante varios cursos en una asignatura de programación en C de segundo curso de los cuatro grados de Ingeniería de Telecomunicaciones. Para poder evaluar, ajustar y mejorar el proceso de enseñanzaaprendizaje propuesto, se utilizan mecanismos de realimentación y seguimiento del alumnado y del profesorado. En este artículo, la implantación de la metodología es evaluada por 40 profesores de distintas universidades españolas que imparten asignaturas en cursos de ingeniería, con objeto de validar su aplicabilidad en otros contextos.Este trabajo ha sido financiado parcialmente por el proyecto nacional del Ministerio de Economía y Competitividad, “Espacios Educativos Especulares” - EEE (TIN2011-28308-C03-01), por el proyecto regional de la Comunidad de Madrid, “eMadrid” (S2009/TIC-1650) y por el programa de estancias postdoctorales Alianza 4 Universidades.Publicad