DIT - GAST - Comunicaciones en Congresos

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Predicting admission test success using SPOC interactions
(Society for Learning Analytics Research (Solar), 2019-03-04) Moreno Marcos, Pedro Manuel; De Laet, Tinne; Muñoz Merino, Pedro José; Van Soom, Carolien; Broos, Tom; Verbert, Katrien; Delgado Kloos, Carlos; Ministerio de Ciencia, Innovación y Universidades (España); Comunidad de Madrid
In order to start Medical or Dentistry studies in Flemish universities, prospective students have to pass a central admission test to guarantee they have the proper level of proficiency. To support those learners, a blended program with a SPOC (Small Private Online Course) was designed on Edge edX. The logs from the platform provide a great opportunity to delve into the behavior of learners and to try to predict their success in the test based on students' interactions with the SPOC. This article has the following objectives: (1) analyze the differences of user interactions between learners based on their background, (2) develop and analyze predictive models to forecast who will pass the admission test, (3) discover which variables have more effect on success in this test, and (4) discuss about the generalizability of the solution. The results show that the SPOC learning behavior differs significantly between students with different background; it is not possible to predict success the admission test until the last months; and the average grade using only first attempts stands out as the best predictor.
Analyzing Students' Persistence using an Event-Based Model
(CEUR-WS.org, 2019-06-27) Moreno Marcos, Pedro Manuel; Muñoz Merino, Pedro José; Alario Hoyos, Carlos; Delgado Kloos, Carlos; Agencia Estatal de Investigación (España); Comunidad de Madrid; Ministerio de Ciencia, Innovación y Universidades (España)
In education, persistence can be defined as the students' ability to keep on working on the assigned tasks (e.g., exercises) despite the difficulties. From previous studies, persistence might be an important factor in students' performance. However, these studies were limited because they only relied on students' self-reported data to measure persistence. This article aims to contribute with a novel model to measure persistence from students' logs, which is general enough to be applied to different educational platforms. In this work, persistence is measured taking students' interactions with automatic correction exercises. Simple metrics such as the average of students' attempts are not valid for a precise calculation of persistence since some exercises should count more for persistence as they have been done incorrectly many times but with some limit so that a single exercise cannot bias the indicator; or when a student answers correctly we should not add new attempts. In this paper, we propose a model to measure persistence on exercises which is valid to many digital online educational platforms. The analysis of students' persistence shows that there are not statistically significant differences of persistence between students who drop out the course or not, although persistence is shown to have a positive relationship with average grades in most of the cases. In contrast, persistence is not related to engagement with videos. These results provide an initial exploration about students' persistence, which can be important to understand how students behave and to properly adapt the course to students' needs.
An early warning dropout model in higher education degree programs: A case study in Ecuador
(CEUR-WS.org, 2020-09-14) Heredia-Jimenez, Vanessa; Jiménez Macías, Alberto Alejandro; Ortiz Rojas, Margarita; Imaz Marín, Jon; Moreno-Marcos, Pedro Manuel; Muñoz Merino, Pedro José; Delgado Kloos, Carlos; Ministerio de Economía, Comercio y Empresa (España); Comunidad de Madrid; European Commission
Worldwide, a significant concern of universities is to reduceacademic dropout rate. Several initiatives have been made to avoid thisproblem; however, it is essential to recognize at-risk students as soon aspossible. In this paper, we propose a new predictive model that can iden-tify the earliest moment of dropping out of a student of any semester inany undergraduate course. Unlike most available models, our solution isbased on academic information alone, and our evidence suggests that byignoring socio-demographics or pre-college entry information, we obtainmore reliable predictions, even when a student has only one academicsemester finished. Therefore, our prediction can be used as part of anacademic counseling tool providing the performance factors that couldinfluence a student to leave the institution. With this, the counselorscan identify those students and take better decisions to guide them andfinally, minimize the dropout in the institution. As a case study, we usedthe students¿ data of all undergraduate programs from 2000 until 2019from a public high education university in Ecuador.
An Initial Analysis of Prediction Techniques as a Support for the Flipped Classroom
(CEUR-WS.org, 2020-09-01) Rubio Fernández, Aarón; Moreno-Marcos, Pedro Manuel; Muñoz Merino, Pedro José; Delgado Kloos, Carlos; Comunidad de Madrid; Ministerio de Ciencia, Innovación y Universidades (España)
With the increasing use of active learning methodologies such as the Flipped Classroom (FC), many approaches have been taken to enhance the students' learning in such contexts. Prediction techniques can be used in combination with Learning Analytics (LA) dashboards for the improvement of the FC model. In this direction, we analyze some theoretical cases in which this approach can provide academic benefits (e.g. providing additional resources or re-designing the class). Furthermore, we present several initial ideas on how to combine two existing software tools, one which provides LA dashboards and the other that implements prediction techniques, that can be used successfully in such scenarios for the FC. This is a preliminary work for the joint use of prediction techniques and LA dashboards in FC contexts.
Challenges of End-to-End Testing with Selenium WebDriver and How to Face Them: A Survey
(IEEE, 2023-05-26) Leotta, Maurizio; García Gutiérrez, Boni; Ricca, Filippo; Whitehead, Jim; Comunidad de Madrid
Modern web applications are complex and used for tasks of primary importance, so their quality must be guaranteed at the highest levels. For this reason, testing techniques (e.g., end-to-end) are required to validate the overall behavior of web applications. One of the most popular tools for testing web applications is Selenium WebDriver. Selenium WebDriver automates the browser to mimic real user actions on the web.While Selenium has made testing easier for many Teams worldwide, it still has its share of challenges. To better understand the challenges and the corresponding solutions adopted we decided to undertake a personal opinion survey from the industry (in total with 78 highly skilled participants) with a focus on the Selenium ecosystem.The results allow understanding which challenges are consid-ered more relevant by professionals in their daily practice and which are the techniques, approaches, and tools they adopt to face them. Therefore, this study is useful to (1) practitioners interested in understanding how to solve the problems they face every day and (2) researchers interested in proposing innovative solutions to problems having a solid industrial impact.
Designing your first MOOC from scratch: recommendations after teaching "Digital Education of the Future"
(Open Education Europa, 2014-03) Alario-Hoyos, Carlos; Pérez Sanagustín, Mar; Delgado Kloos, Carlos; Gutiérrez Rojas, Israel; Leony Arreaga, Derick Antonio; Parada Gélvez, Hugo Alexer; Comunidad de Madrid; Ministerio de Economía y Competitividad (España)
Massive Open Online Courses (MOOCs) have been a very promising innovation in higher education for the last few months. Many institutions are currently asking their staff to run high quality MOOCs in a race to gain visibility in an education market that is beginning to be full of choices. Nevertheless, designing and running a MOOC from scratch is not an easy task and requires a high workload. This workload should be shared among those generating contents, those fostering discussion in the community around the MOOC, those supporting the recording and subtitling of audiovisual materials, and those advertising the MOOC, among others. Sometimes the teaching staff has to assume all these tasks (and consequently the associated workload) due to the lack of adequate resources in the institution. This is just one example of the many problems that teachers need to be aware of before riding the MOOC wave. This paper offers a set of recommendations that are expected to be useful for those inexperienced teachers that now face the challenge of designing and running MOOCs. Most of these recommendations come from the lessons learned after teaching a nine-week MOOC on educational technologies, called “Digital Education of the Future”, at the Universidad Carlos III in Madrid, Spain.
Finite-blocklength results for the A-channel: applications to unsourced random access and group testing
(IEEE, 2022-09-27) Lancho Serrano, Alejandro; Fengler, Alexander; Polyanskiy, Yury; European Commission
We present finite-blocklength achievability bounds for the unsourced A-channel. In this multiple-access channel, users noiselessly transmit codewords picked from a common codebook with entries generated from a q -ary alphabet. At each channel use, the receiver observes the set of different transmitted symbols but not their multiplicity. We show that the A-channel finds applications in unsourced random-access (URA) and group testing. Leveraging the insights provided by the finite-blocklength bounds and the connection between URA and non-adaptive group testing through the A-channel, we propose improved decoding methods for state-of-the-art A-channel codes and we showcase how A-channel codes provide a new class of structured group testing matrices. The developed bounds allow to evaluate the achievable error probabilities of group testing matrices based on random A-channel codes for arbitrary numbers of tests, items and defectives. We show that such a construction asymptotically achieves the optimal number of tests. In addition, every efficiently decodable A-channel code can be used to construct a group testing matrix with sub-linear recovery time.
A finite-blocklength analysis for URLLC with Massive MIMO
(IEEE, 2021-06-14) Lancho Serrano, Alejandro; Östman, Johan; Giuseppe, Durisi; Sanguinetti, Luca
This paper presents a rigorous finite-blocklength framework for the characterization and the numerical evaluation of the packet error probability achievable in the uplink and downlink of Massive MIMO for ultra-reliable low-latency communications (URLLC). The framework encompasses imperfect channel-state information, pilot contamination, spatially correlated channels, and arbitrary linear signal processing. For a practical URLLC network setup involving base stations with M = 100 antennas, we show by means of numerical results that a target error probability of 10 −5 can be achieved with MMSE channel estimation and multicell MMSE signal processing, uniformly over each cell, only if orthogonal pilot sequences are assigned to all the users in the network. For the same setting, an alternative solution with lower computational complexity, based on least-squares channel estimation and regularized zero-forcing signal processing, does not suffice unless M is increased significantly.
From software engineering to courseware engineering
(IEEE, 2016-04-10) Delgado Kloos, Carlos; Ibáñez Espiga, María Blanca; Alario-Hoyos, Carlos; Muñoz Merino, Pedro José; Estévez Ayres, Iria Manuela; Fernández Panadero, María Carmen; Villena Román, Julio; Comunidad de Madrid; Ministerio de Economía y Competitividad (España)
The appearance of MOOCs has contributed to the use of educational technology in new contexts. As a consequence, many teachers face the challenge of creating educational content (courseware) to be offered in MOOCs. Although some best practices exist, it is true that most of the content is being developed without much thought about adequacy, reusability, maintainability, composability, etc. The main thesis at this paper is that we are facing a "courseware crisis" in the same way as there was a "software crisis" 50 years ago, and that the way out is to identify good engineering discipline to aid in the development of courseware. We need Courseware Engineering in the same way as at those times we needed Software Engineering. Therefore, the challenge is now to define and develop fundamentals, tools, and methods of Courseware Engineering, as an analogy to the fundamentals, tools, and methods that were developed in Software Engineering.
PROF-XXI: Teaching and Learning Centers to Support the 21st Century Professor
(IEEE, 2021-11-15) Delgado Kloos, Carlos; Alario-Hoyos, Carlos; Morales, Miguel; Hernández, Rocael; Jerez, Óscar; Pérez Sanagustín, Mar; Kotorov, Louri; Fernández Recinos, Alejandra; Oliva Córdova, Luis Magdiel; Solarte, Mario; Jaramillo, Daniel; Moreira Teixeira, Antonio; González López, Astrid Helena
PROF-XXI is a European-funded project whose aim is the creation of Teaching and Learning Centers (TLCs) for Latin American Higher Institutions in an effort to promote the development of competences for university professors and foster teaching innovation in onsite, but also in online and hybrid education. PROF-XXI includes a partnership of seven higher education institutions, three from European countries (Spain, France, and Portugal), and four from Latin American countries (two from Guatemala, and two from Colombia). This article presents the main results of the first part of the project, including the diagnosis of institutional practices, the state of the art of TLCs around the world, the framework on 21st century professors in Latin America, and the PROF-XXI framework.
Can Feedback based on Predictive Data Improve Learners' Passing Rates in MOOCs? A Preliminary Analysis
(Perez-Sanagustin, M., Pérez-Álvarez, R., Maldonado-Mahauad, J., Villalobos, E., Hilliger, I., Hernández, J., Sapunar, D., Moreno-Marcos, P. M., Muñoz-Merino, P. J., Delgado Kloos, C. & Imaz, J. (2021). Can Feedback based on Predictive Data Improve Learners’ Passing Rates in MOOCs? A Preliminary Analysis. Proceedings of the Eighth ACM Conference on Learning @ Scale., 2021-06-22) Pérez Sanagustín, Mar; Pérez Álvarez, Ronald; Maldonado Mahauad, Jorge; Villalobos, Esteban; Hilliger, Isabel; Hernández, Josefina; Sapunar, Diego; Moreno-Marcos, Pedro Manuel; Muñoz Merino, Pedro José; Delgado Kloos, Carlos; Imaz, Jon; European Commission
This work in progress paper investigates if timely feedback increases learners passing rate in a MOOC. An experiment conducted with 2,421 learners in the Coursera platform tests if weekly messages sent to groups of learners with the same probability of dropping out the course can improve retention. These messages can contain information about: (1) the average time spent in the course, or (2) the average time per learning session, or (3) the exercises performed, or (4) the video-lectures completed. Preliminary results show that the completion rate increased 12% with the intervention compared with data from 1,445 learners that participated in the same course in a previous session without the intervention. We discuss the limitations of these preliminary results and the future research derived from them.
Challenges in using sensors to track users health and wellbeing on a daily basis
(2021-10-01) Muñoz Organero, Mario
Despite the many technological advances in sensor devices, there are still many challenges that hinder their end to end deployment and use in health and wellbeing monitoring and selfmanagement systems. This talk provides an overview of the different pieces in such a system and identifies some of the major challenges that have to be addressed before their mass adoption by the national health services.
What Can You Do with Educational Technology that is Getting More Human?
(IEEE, 2019-04-09) Delgado Kloos, Carlos; Alario-Hoyos, Carlos; Muñoz Merino, Pedro José; Ibáñez Espiga, María Blanca; Estévez Ayres, Iria Manuela; Crespo García, Raquel; Comunidad de Madrid; Ministerio de Ciencia, Innovación y Universidades (España)
Technology is advancing at an ever-increasing speed. The backend capabilities and the frontend means of interaction are revolutionizing all kinds of applications. In this paper, we analyze how the technological breakthroughs seem to make educational interactions look smarter and more human. After defining Education 4.0 following the Industry 4.0 idea, we identify the key breakthroughs of the last decade in educational technology, basically revolving around the concept cloud computing, and imagine a new wave of educational technologies supported by machine learning that allows defining educational scenarios where computers interact and react more and more like humans.
Chrome Plug-in to Support SRL in MOOCs
(Springer, 2019-05-20) Alonso Mencía, María Elena; Alario-Hoyos, Carlos; Delgado Kloos, Carlos; Comunidad de Madrid; European Commission; Ministerio de Economía y Competitividad (España)
Massive Open Online Courses (MOOCs) have gained popularity over the last years, offering a learning environment with new opportunities and challenges. These courses attract a heterogeneous set of participants who, due to the impossibility of personal tutorship in MOOCs, are required to create their own learning path and manage one’s own learning to achieve their goals. In other words, they should be able to self-regulate their learning. Self-regulated learning (SRL) has been widely explored in settings such as face-to-face or blended learning environments. Nevertheless, research on SRL in MOOCs is still scarce, especially on supporting interventions. In this sense, this document presents MOOCnager, a Chrome plug-in to help learners improve their SRL skills. Specifically, this work focuses on 3 areas: goal setting, time management and selfevaluation. Each area is included in one of the 3 phases composing Zimmerman’s SRL Cyclical Model. In this way, the plug-in aims to support enrolees’ self-regulation throughout their complete learning process. Finally, MOOCnager was uploaded to the Chrome Web Store, in order to get a preliminary evaluation with real participants from 6 edX Java MOOCs designed by the Universidad Carlos III de Madrid (UC3M). Results were not conclusive as the use of the plug-in by the participants was very low. However, learners seem to prefer a seamless tool, integrated in the MOOC platform, which is able to assist them without any learner-tool interaction.
Predicting Learners' Success in a Self-paced MOOC Through Sequence Patterns of Self-regulated Learning
(Springer, 2018-09-03) Maldonado-Mahauad, Jorge; Perez Sanagustin, Maria Del Mar; Moreno-Marcos, Pedro Manuel; Alario-Hoyos, Carlos; Muñoz Merino, Pedro José; Delgado Kloos, Carlos; Ministerio de Economía y Competitividad (España); Ministerio de Educación, Cultura y Deporte (España)
In the past years, predictive models in Massive Open Online Courses (MOOCs) have focused on forecasting learners' success through their grades. The prediction of these grades is useful to identify problems that might lead to dropouts. However, most models in prior work predict categorical and continuous variables using low-level data. This paper contributes to extend current predictive models in the literature by considering coarse-grained variables related to Self-Regulated Learning (SRL). That is, using learners' self-reported SRL strategies and MOOC activity sequence patterns as predictors. Lineal and logistic regression modelling were used as a first approach of prediction with data collected from N = 2,035 learners who took a self-paced MOOC in Coursera. We identified two groups of learners: (1) Comprehensive, who follow the course path designed by the teacher; and (2) Targeting, who seek for the information required to pass assessments. For both type of learners, we found a group of variables as the most predictive: (1) the self-reported SRL strategies 'goal setting', 'strategic planning', 'elaboration' and 'help seeking'; (2) the activity sequences patterns 'only assessment', 'complete a video-lecture and try an assessment', 'explore the content' and 'try an assessment followed by a video-lecture'; and (3) learners' prior experience, together with the self-reported interest in course assessments, and the number of active days and time spent in the platform. These results show how to predict with more accuracy when students reach a certain status taking in to consideration not only low-level data, but complex data such as their SRL strategies.
SmartLET: learning analytics to enhance the design and orchestration in scalable, IoT-enriched, and ubiquitous Smart Learning Environments
(Association For Computing Machinery (ACM), 2018-10) Delgado Kloos, Carlos; Dimitriadis, Yannis; Hernández-Leo, Davinia; Muñoz Merino, Pedro José; Bote-Lorenzo, Miguel Luis; Carrió, Mar; Alario-Hoyos, Carlos; Gómez-Sánchez, Eduardo; Santos Rodríguez, Patricia
This paper presents the SmartLET project, a coordinated research project funded by the Spanish Ministry of Science, Innovation and Universities, which just started in 2018. The main aim of this project is to provide support for the design and orchestration of Smart Learning Environments (SLEs) with the support of learning analytics and the Internet of Things. This paper gives an overview of our conception of SLEs based on previous works, provides some ideas about the connection of learning design and orchestration with SLEs, and analyses different ethical and privacy issues for SLEs. In addition, an initial hypothesis and some specific objectives for a support environment for SLEs are proposed.
Sentiment analysis in MOOCs: a case study
(IEEE, 2018-05-24) Moreno-Marcos, Pedro Manuel; Alario-Hoyos, Carlos; Muñoz Merino, Pedro José; Estévez Ayres, Iria Manuela; Delgado Kloos, Carlos; Comunidad de Madrid; Ministerio de Economía y Competitividad (España); Ministerio de Educación, Cultura y Deporte (España)
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.
Boosting interaction with educational technology
(IEEE, 2017-04-25) Delgado Kloos, Carlos; Alario-Hoyos, Carlos; Estévez Ayres, Iria Manuela; Muñoz Merino, Pedro José; Ibáñez Espiga, María Blanca; Crespo García, Raquel; Comunidad de Madrid; Ministerio de Economía y Competitividad (España)
The MOOC movement has helped faculty in focusing on how to lecture. However, once this is done, it would not make sense not to use this content for on-campus classes. In this paper, we will explain how to harness top content created for MOOCs to improve on-campus classes, where the personal interaction is a key added feature. Interactive practices and on-site interaction, especially in-class interaction, are of particular relevance in the evolution of Higher Education towards a more effective learning.
Assessing gait impairments based on auto-encoded patterns of mahalanobis distances from consecutive steps
(IOS Press, 2017) Muñoz Organero, Mario; Davies, Richard; Mawson, Sue
Insole pressure sensors capture the force distribution patterns during the stance phase while walking. By comparing patterns obtained from healthy individuals to patients suffering different medical conditions based on a given similarity measure, automatic impairment indexes can be computed in order to help in applications such as rehabilitation. This paper uses the data sensed from insole pressure sensors for a group of healthy controls to train an auto-encoder using patterns of stochastic distances in series of consecutive steps while walking at normal speeds. Two experiment groups are compared to the healthy control group: a group of patients suffering knee pain and a group of post-stroke survivors. The Mahalanobis distance is computed for every single step by each participant compared to the entire dataset sensed from healthy controls. The computed distances for consecutive steps are fed into the previously trained autoencoder and the average error is used to assess how close the walking segment is to the autogenerated model from healthy controls. The results show that automatic distortion indexes can be used to assess each participant as compared to normal patterns computed from healthy controls. The stochastic distances observed for the group of stroke survivors are bigger than those for the people with knee pain.
Publication of RDF streams with Ztreamy
(Springer International Publishing, 2014-05-25) Arias Fisteus, Jesús; Fernández García, Norberto; Sánchez Fernández, Luis; Fuentes Lorenzo, Damaris
There is currently an interest in the Semantic Web community for the development of tools and techniques to process RDF streams. Implementing an effective RDF stream processing system requires to address several aspects including stream generation, querying, reasoning, etc. In this work we focus on one of them: the distribution of RDF streams through the Web. In order to address this issue, we have developed Ztreamy, a scalable middleware which allows to publish and consume RDF streams through HTTP. The goal of this demo is to show the functionality of Ztreamy in two different scenarios with actual, heterogeneous streaming data.

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