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

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

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

Generating actionable predictions regarding MOOC learners’ engagement in peer reviews

Producción CientíficaPeer 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.European Union’s Horizon 2020 research and innovation programme (Marie Sklodowska-Curie grant 793317)Ministerio de Ciencia, Innovación y Universidades (projects TIN2017-85179-C3-2-R / TIN2014-53199-C3-2-R)Junta de Castilla y León (grant VA257P18)Comisión Europea (grant 588438-EPP-1-2017-1-EL-EPPKA2-KA

Towards the Enactment of Learning Situations Connecting Formal and Non-Formal Learning in SLEs

Producción CientíficaSmart Learning Environments hold promise of adapting learning processes to the individual context of students and connecting formal with non-formal learning. To do so, SLEs need to know the current context of the students, regardless of the physical or virtual space where learning takes place. This paper presents an architecture that assists in the deployment and enactment of learning situations across-spaces, able to sense and react to changes in the students’ context in order to adapt the learning process.ICSLE 2019: International Conference on Smart Learning EnvironmentsAgencia Estatal de Investigación - Fondo Europeo de Desarrollo Regional (projects TIN2014-53199-C3-2-R / TIN2017-85179-C3-2-R)Comisión Europea (project 588438-EPP-1-2017-1-EL-EPPKA2-KA

Understanding student behavior and perceptions toward earning badges in a gamified MOOC

Producción CientíficaDespite the advantages of MOOCs, such as the open and free access to education, these courses are criticized for students’ lack of motivation and their high dropout rates. Gamification is a technique used to increase student motivation and engagement in small-scale educational contexts. However, the effects of gamification on student engagement have been scarcely explored in MOOC environments, and the findings so far are inconsistent. To address this gap, this research work examines the students’ behavior toward earning badges and how it relates to their engagement in a gamified MOOC. According to the results, the behaviors toward badges of the active students were generally positive and significantly correlated with other variables measuring their engagement (e.g., pageviews, submitted tasks, forum posts), although this positive behavior seems to decrease throughout the course. Additionally, students that reported high motivation by badges at the end of the course showed a higher engagement level than those that were not appealed by badges.European Regional Development Fund, under project grants TIN2014-53199-C3-2-R and TIN2017-85179-C3-2-RJunta de Castilla y León (programa de apoyo a proyectos de investigación - Ref. Project VA082U16 and VA257P18)European Commission, under project grant 588438-EPP-1-2017-1-EL-EPPKA2-KA