An approach to build in situ models for the prediction of the decrease of academic engagement indicators in Massive Open Online Courses

Producción CientíficaThe early detection of learners who are expected to disengage with typical MOOC tasks such as watching lecture videos or submitting assignments is necessary to enable timely interventions aimed at preventing it. This can be done by predicting the decrease of academic engagement indicators that can be derived for di_erent MOOC tasks and computed for each learner. A posteriori prediction models can yield a good performance but cannot be built using the information that is available in an ongoing course at the moment the predictions are required. This paper proposes an approach to build in situ prediction models using such information. Models were derived following both approaches and employed to predict the decrease of three indicators that quantify the engagement of learners with the main tasks typically proposed in a MOOC: watching lectures, solving _nger exercises, and submitting assignments. The results show that in situ models yielded a good performance for the prediction of all engagement indicators, thus showing the feasibility of the proposed approach. This performance was very similar to that of a posteriori models, which have the clear disadvantage that they cannot be used to make predictions in an ongoing course based on its data.Ministerio de Economía, Industria y Competitividad (Projects TIN2014-53199-C3-2-R (AEI, FEDER), TIN2017-85179-C3-2-R)Junta de Castilla y León (programa de apoyo a proyectos de investigación - Ref. VA277U14)European Commission (Proyect 588438-EPP-1-2017-1-EL-EPPKA2-KA

The added value of implementing the Planet Game scenario with Collage and Gridcole

This paper discusses the suitability and the added value of Collage and Gridcole when contrasted with other solutions participating in the ICALT 2006 workshop titled “Comparing educational modelling languages on a case study.” In this workshop each proposed solution was challenged to implement a Computer-Supported Collaborative Learning situation (CSCL) posed by the workshop’s organizers. Collage is a pattern-based authoring tool for the creation of CSCL scripts compliant with IMS Learning Design (IMS LD). These IMS LD scripts can be enacted by the Gridcole tailorable CSCL system. The analysis presented in the paper is organized as a case study which considers the data recorded in the workshop discussion as well the information reported in the workshop contributions. The results of this analysis show how Collage and Gridcole succeed in implementing the scenario and also point out some significant advantages in terms of design reusability and generality, user-friendliness, and enactment flexibility

Una experiencia de formación colaborativa y práctica real entre la universidad y un centro educativo generando un espacio CSCL

Esta experiencia refleja la respuesta que le hemos dado en la necesidad de contextualizar los procesos de enseñanza en la formación de maestros y la fundamental coordinación de los trabajos que se realizan en la universidad para llevar a cabo nuestra labor. La unión de dos asignaturas troncales en el plan de estudios de magisterio (Didáctica General y Nuevas Tecnologías aplicadas a la Educación), junto con la participación de un Centro Escolar (C.P. Vicente Aleixandre de Valladolid), ha posibilitado la interconexión de contenidos teóricos de ambas asignaturas y la perspectiva práctica que aporta el Colegio. Todo ello tomando como eje un entorno colaborativo de trabajo en red, Basic Support for Cooperative Work (BSCW) que nos ha permitido generar una dinámica de formación CSCL (Computer Support for Colaborative Learnin). En este trabajo se incluye una descripción del proyecto con su intrincado cúmulo de relaciones e interconexiones, así como la valoración del primer año de trabajo.This experience shows the answer that we give to the need of relating theory and practice in maestriʹs formation. The union of two main subjects of study in the curriculum (Didáctica General y Nuevas Tecnologías aplicadas a la Educación) , along with the participation of an school (C.P. Vicente Aleixandre) , has made the interconnection of theoretic contentses of both subjects of study and the practical perspective that the School contributes . All of it taking like axle a collaborative surroundings of net work, Basic Support for Cooperative Work (BSCW), that has permitted us generating a dynamics of formation (Computer Supported Collaborative Learning). In this work a description of project with his intricate relational accumulation and interface are included, as well as the evaluation of first year of workEsta experiência reflete a resposta que nós temos dado à necessidade de contextualizar os processos de ensino na formação de professores e a fundamental coordenação dos trabalhos que realizam‐se na universidade para levar a cabo nosso labor. A união de duas disciplinas troncais no plano de estudos de Magistério (Didática Geral e Novas Tecnologías aplicadas à Educação) junto com a participação de um Centro Escolar (C. P. Vicente Aleixandre de Valladolid), tem possibilitado a interconexão dos conteúdos teóricos de ambas disciplinas e a perspectiva prática que aporta o Colégio. Tudo isto tomando como eixo um meio colaborativo de trabalho em rede, Basic Support for Cooperativ Work (BSCW) que permitiu‐nos gerar uma dinâmica de formação CSCL (Computer Support for Colaborative Learning). Neste trabalho se inclui uma descrição do projeto com seu enredado conjunto de relações e interconexões, bem como a valoração do primeiro ano de trabalho

Estimation of Web Proxy Response Times in Community Networks Using Matrix Factorization Algorithms

Producción CientíficaIn community networks, users access the web using a proxy selected from a list, normally without regard to its performance. Knowing which proxies offer good response times for each client would improve the user experience when navigating, but would involve intensive probing that would in turn cause performance degradation of both proxies and the network. This paper explores the feasibility of estimating the response times for each client/proxy pair by probing only a few of the existing pairs and then using matrix factorization. To do so, response times are collected in a community network emulated on a testbed platform, then a small part of these measurements are used to estimate the remaining ones through matrix factorization. Several algorithms are tested; one of them achieves estimation accuracy with low computational cost, which renders its use feasible in real networks.Ministerio de Ciencia, Innovación y Universidades - Fondo Europeo de Desarrollo Regional (grants TIN2017-85179-C3-2-R and TIN2016-77836-C2-2-R)Generalitat de Catalunya (contract AGAUR SGR 990

Online machine learning algorithms to predict link quality in community wireless mesh networks

Producción CientíficaAccurate link quality predictions are key in community wireless mesh networks (CWMNs) to improve the performance of routing protocols. Unlike other techniques, online machine learning algorithms can be used to build link quality predictors that are adaptive without requiring a predeployment effort. However, the use of these algorithms to make link quality predictions in a CWMN has not been previously explored. This paper analyses the performance of 4 well-known online machine learning algorithms for link quality prediction in a CWMN in terms of accuracy and computational load. Based on this study, a new hybrid online algorithm for link quality prediction is proposed. The evaluation of the proposed algorithm using data from a real large scale CWMN shows that it can achieve a high accuracy while generating a low computational load.Ministerio de Economía, Industria y Competitividad (Project TIN2014-53199-C3-2-R)Junta de Castilla y León (programa de apoyo a proyectos de investigación - Ref. VA082U16

A self-scalable distributed network simulation environment based on cloud computing

Producción CientíficaWhile parameter sweep simulations can help undergraduate students and researchers to understand computer networks, their usage in the academia is hindered by the significant computational load they convey. This paper proposes DNSE3, a service oriented computer network simulator that, deployed in a cloud computing infrastructure, leverages its elasticity and pay-per-use features to compute parameter sweeps. The performance and cost of using this application is evaluated in several experiments applying different scalability policies, with results that meet the demands of users in educational institutions. Additionally, the usability of the application has been measured following industry standards with real students, yielding a very satisfactory user experience.Ministerio de Economía, Industria y Competitividad (Projects TIN2014-53199-C3-2-R and TIN2017-85179-C3-2-R)Junta de Castilla y León (programa de apoyo a proyectos de investigación - Ref. VA082U16

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

Technologies for Data-Driven Interventions in Smart Learning Environments [Editorial]

Smart Learning environments (SLEs) are defined [1] as learning ecologies where students engage in learning activities, or where teachers facilitate such activities with the support of tools and technology. SLEs can encompass physical or virtual spaces in which a system senses the learning context and process by collecting data, analyzes the data, and consequently reacts with customized interventions that aim at improving learning [1]. In this way, SLEs may collect data about learners and educators’ actions and interactions related to their participation in learning activities as well as about different aspects of the formal or informal context in which they can be carried out. Sources from these data may include learning management systems, handheld devices, computers, cameras, microphones, wearables, and environmental sensors. These data can then be transformed and analyzed using different computational and visualization techniques to obtain actionable information that can trigger a wide range of automatic, human-mediated, or hybrid interventions, which involve learners and teachers in the decision making behind the interventions.This work was supported in part by the Spanish Ministry of Science and Innovation through Smartlet and the H2OLearn Projects under Grant MICIN/AEI/10.13039/501100011033, and in part by the Fondo Europeo de Desarrollo Regional (FEDER) under Grant TIN2017-85179-C3-1-R, Grant TIN2017-85179-C3-2-R, Grant TIN2017-85179-C3-30R, Grant PID2020-112584RB-C31, Grant PID2020-112584RB C32, and Grant GPID2020-112584RB-C33. The work of Davinia Hernández-Leo (Serra Húnter) was supported by ICREA through the ICREA Academia Program.Publicad

Generating actionable predictions regarding MOOC learners' engagement in peer reviews

Peer review is one approach to facilitate formative feedback exchange in MOOCs; however, it is often undermined by low participation. To support effective implementation of peer reviews in MOOCs, this research work proposes several predictive models to accurately classify learners according to their expected engagement levels in an upcoming peer-review activity, which offers various pedagogical utilities (e.g. improving peer reviews and collaborative learning activities). Two approaches were used for training the models: in situ learning (in which an engagement indicator available at the time of the predictions is used as a proxy label to train a model within the same course) and transfer across courses (in which a model is trained using labels obtained from past course data). These techniques allowed producing predictions that are actionable by the instructor while the course still continues, which is not possible with post-hoc approaches requiring the use of true labels. According to the results, both transfer across courses and in situ learning approaches have produced predictions that were actionable yet as accurate as those obtained with cross validation, suggesting that they deserve further attention to create impact in MOOCs with real-world interventions. Potential pedagogical uses of the predictions were illustrated with several examples

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