Optimización del flujo de programa en software de gestión para microelectrónica

La empresa Computadoras Redes e Ingeniería, S.A. (Crisa), dentro de su sección de microelectrónica necesita una herramienta que se encargue de instalar paquetes, que incluyen diseños de hardware que pueden ser reutilizados en varios proyectos. El propósito de este Trabajo Fin de Grado es el de implementar la aplicación a medida que satisfaga las necesidades planteadas. Para ello, se pretende analizar las características que ofrecen los sistemas de gestión de paquetes actuales, dado que el software especificado se puede considerar como tal, para poder extraer las características que pueden ser reaprovechadas y conocer cuáles son las estructuras de datos que permiten el funcionamiento de estas herramientas. El desarrollo se separa en dos versiones diferenciadas y consistentes, pero que utilizan un núcleo común. Por una parte, se encuentra la versión en línea de comandos y por otra una versión gráfica. Ambas proporcionan la misma funcionalidad pero los distintos usuarios que prueban el programa tienden más al uso de una u otra y por ello, debe garantizarse que no haya diferencia entre usar una u otra. Del mismo modo, un aspecto importante es el de la compatibilidad, puesto que no todos los desarrolladores que utilizan la herramienta utilizan la misma plataforma y el objetivo es que sea accesible a todos.Computadoras, Redes e Ingeniería, S.A. (Crisa) needs a tool for its microelectronic section which handles the problem of installing packages, which include hardware designs that can be reused in other projects. The purpose of this Bachelor Thesis is to implement a custom-made application which satisfies the exposed necessities. To do that, it is intended to analyze the features other package manager systems offer, as this software can be considered as such, to be able to extract the reusable features and to know what the common data structures, which allows those tools working, are. In order to develop the tool, work has been separated in two consistent and diffenced versions, which use the same kernel. On the one hand, there is a command-line version and on the other hand there is a graphic one. Both provide the same functionality but users tend to user one more than another and there must be guaranteed that there is no difference between using any of those versions. At the same time, an important aspect is the compatibility between platforms since each developer which uses the tool can use a different one and the main objective is that all of them can use the program independently on where they run it.Ingeniería en Tecnologías de Telecomunicació

Re-Defining, Analyzing and Predicting Persistence Using Student Events in Online Learning

This article belongs to the Special Issue Smart LearningIn education, several studies have tried to track student persistence (i.e., students' ability to keep on working on the assigned tasks) using di fferent definitions and self-reported data. However, self-reported metrics may be limited, and currently, online courses allow collecting many low-level events to analyze student behaviors based on logs and using learning analytics. These analyses can be used to provide personalized and adaptative feedback in Smart Learning Environments. In this line, this work proposes the analysis and measurement of two types of persistence based on students' interactions in online courses: (1) local persistence (based on the attempts used to solve an exercise when the student answers it incorrectly), and (2) global persistence (based on overall course activity/completion). Results show that there are different students' profiles based on local persistence, although medium local persistence stands out. Moreover, local persistence is highly a ffected by course context and it can vary throughout the course. Furthermore, local persistence does not necessarily relate to global persistence or engagement with videos, although it is related to students' average grade. Finally, predictive analysis shows that local persistence is not a strong predictor of global persistence and performance, although it can add some value to the predictive models.This work was partially funded by FEDER/Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación/project Smartlet (TIN2017-85179-C3-1-R), and by the Madrid Regional Government, through the project e-Madrid-CM (S2018/TCS-4307). The latter is also co-financed by the Structural Funds (FSE and FEDER). This work received also partial support by Ministerio de Ciencia, Innovación y Universidades, under an FPU fellowship (FPU016/00526)

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

Generalizing predictive models of admission test success based on online interactions

This article belongs to the Special Issue Sustainability of Learning AnalyticsTo start medical or dentistry studies in Flanders, prospective students need to pass a central admission test. A blended program with four Small Private Online Courses (SPOCs) was designed to support those students. The logs from the platform provide an opportunity to delve into the learners' interactions and to develop predictive models to forecast success in the test. Moreover, the use of different courses allows analyzing how models can generalize across courses. This article has the following objectives: (1) to develop and analyze predictive models to forecast who will pass the admission test, (2) to discover which variables have more effect on success in different courses, (3) to analyze to what extent models can be generalized to other courses and subsequent cohorts, and (4) to discuss the conditions to achieve generalizability. The results show that the average grade in SPOC exercises using only first attempts is the best predictor and that it is possible to transfer predictive models with enough reliability when some context-related conditions are met. The best performance is achieved when transferring within the same cohort to other SPOCs in a similar context. The performance is still acceptable in a consecutive edition of a course. These findings support the sustainability of predictive models.This work was partially funded by the LALA project (grant no. 586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP). The LALA project has been funded with support from the European Commission. In addition, this work has been partially funded by FEDER/Ministerio de Ciencia, Innovación y Universidades—Agencia Estatal de Investigación/project Smartlet (TIN2017-85179-C3-1-R) and by the Madrid Regional Government through the project e-Madrid-CM (S2018/TCS-4307). The latter is also cofinanced by the Structural Funds (FSE and FEDER). It has also been supported by the Spanish Ministry of Science, Innovation, and Universities, under an FPU fellowship (FPU016/00526

An algorithm and a tool for the automatic grading of MOOC learners from their contributions in the discussion forum

MOOCs (massive open online courses) have a built-in forum where learners can share experiences as well as ask questions and get answers. Nevertheless, the work of the learners in the MOOC forum is usually not taken into account when calculating their grade in the course, due to the difficulty of automating the calculation of that grade in a context with a very large number of learners. In some situations, discussion forums might even be the only available evidence to grade learners. In other situations, forum interactions could serve as a complement for calculating the grade in addition to traditional summative assessment activities. This paper proposes an algorithm to automatically calculate learners' grades in the MOOC forum, considering both the quantitative dimension and the relevance in their contributions. In addition, the algorithm has been implemented within a web application, providing instructors with a visual and a numerical representation of the grade for each learner. An exploratory analysis is carried out to assess the algorithm and the tool with a MOOC on programming, obtaining a moderate positive correlation between the forum grades provided by the algorithm and the grades obtained through the summative assessment activities. Nevertheless, the complementary analysis conducted indicates that this correlation may not be enough to use the forum grades as predictors of the grades obtained through summative assessment activities.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 (586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP), InnovaT (598758-EPP-1-2018-1-AT-EPPKA2-CBHE-JP), and PROF-XXI (609767-EPP-1-2019-1-ES-EPPKA2-CBHE-JP)

Improving the learning of engineering students with interactive teaching applications

Over decades, Mechanical Engineering students often find some difficulties to grasp some contents and/or struggle with some parts of the course. With the increasing development of new technologies, promising innovations can be implemented enhancing learning and improving success rates. In this study, a new learning interactive method is proposed and evaluated using the experience of over 600 students of Mechanical Engineering. This study describes a 4-year experiment based on new interactive applications for education. The experiment has been implemented using E-learning techniques and new technologies (a combination of remote and virtual examples, videos, quizzes, and theory). Specifically, several applications have been programmed to be executed on different devices, such as mobile phones and PC/laptops (Android and Windows). The experiment is applied using small applications that help the students identify the most challenging contents and guide them throughout step-by-step. The main objective of this interactive method is to help students find their lack of knowledge and offer them contents to cover it. These didactic applications are portable and intuitive. Thanks to these interactive applications, it is possible to accomplish better practices of “E-learning” and “Computer Simulation and Animation” together. Since they are portable applications, they allow the student to interact and check conceptual understandings at any place. Students really appreciate this aspect. The results of the course titled Mechanism and Machine Theory have been analyzed during these four last years in which these interactive applications have been offered to the students.The authors wish to thank the "Convocatoria De Innovación Docente 2017-2018" of the UC3M

Predicting Learners' Success in a Self-paced MOOC Through Sequence Patterns of Self-regulated Learning

Proceeding of: 13th European Conference on Technology Enhanced Learning, EC-TEL 2018, Leeds, UK, September 3-5, 2018.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.This work was supported by FONDECYT (Chile) under project initiation grant No.11150231, the MOOC-Maker Project (561533-EPP-1-2015-1-ES-EPPKA2-CBHE-JP), the LALA Project (586120-EPP-1-2017-1-ES-EPPKA2-CBHE-JP), and CONICYT/DOCTORADO NACIONAL 2016/21160081, the Spanish Ministry of Education, Culture and Sport, under an FPU fellowship (FPU016/00526) and the Spanish Ministry of Economy and Competiveness (Smartlet project, grant number TIN2017-85179-C3-1-R) funded by the Agencia Estatal de Investigación (AEI) and Fondo Europeo de Desarrollo Regional (FEDER).Publicad

Evaluation of an Algorithm for Automatic Grading of Forum Messages in MOOC Discussion Forums

This article belongs to the Special Issue E-learning, Digital Learning, and Digital Communication Used for Education Sustainability.Discussion forums are a valuable source of information in educational platforms such as Massive Open Online Courses (MOOCs), as users can exchange opinions or even help other students in an asynchronous way, contributing to the sustainability of MOOCs even with low interaction from the instructor. Therefore, the use of the forum messages to get insights about students’ performance in a course is interesting. This article presents an automatic grading approach that can be used to assess learners through their interactions in the forum. The approach is based on the combination of three dimensions: (1) the quality of the content of the interactions, (2) the impact of the interactions, and (3) the user’s activity in the forum. The evaluation of the approach compares the assessment by experts with the automatic assessment obtaining a high accuracy of 0.8068 and Normalized Root Mean Square Error (NRMSE) of 0.1799, which outperforms previous existing approaches. Future research work can try to improve the automatic grading by the training of the indicators of the approach depending on the MOOCs or the combination with text mining techniques.This research was funded by the FEDER/Ministerio de Ciencia, Innovación y Universidades-Agencia Estatal de Investigación, through the Smartlet and H2O Learn Projects under Grants TIN2017-85179-C3-1-R and PID2020-112584RB-C31, and in part by the Madrid Regional Government through the e-Madrid-CM Project under Grant S2018/TCS-4307 and under the Multiannual Agreement with UC3M in the line of Excellence of University Professors (EPUC3M21), and in the context of the V PRICIT (Regional Programme of Research and Technological Innovation), 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 InnovaT (598758-EPP-1-2018-1-AT-EPPKA2-CBHE-JP), and PROF-XXI (609767-EPP-1-2019-1-ES-EPPKA2-CBHE-JP)

Learning analytics in European higher education–trends and barriers

Learning analytics (LA) as a research field has grown rapidly over the last decade. However, adoption of LA is mostly found to be small in scale and isolated at the instructor level. This paper presents an exploratory study on institutional approaches to LA in European higher education and discusses prominent challenges that impede LA from reaching its potential. Based on a series of consultations with senior managers from 83 different higher education institutions in 24 European countries, we observe that LA is primarily perceived as a tool to enhance teaching and institutional management. As a result, teaching and support staff are found to be the main users of LA and the target audience of training support. In contrast, there is little evidence of active engagement with students or using LA to develop self-regulated learning skills. We highlight the importance of grounding LA in learning sciences and including students as a key stakeholder in the design and implementation of LA. This paper contributes to our understanding of the development of LA in European higher education and highlights areas to address in both practice and research. © 2020 Elsevier LtdThis work was supported by the Erasmus+ Programme of the European Union [562080-EPP- 1-2015-1-BE-EPPKA3-PI-FORWARD]. The European Commission support for the production of this publication does not constitute an endorsement of the contents which reflects the views only of the authors, and the Commission will not be held responsible for any use which may be made of the information contained therein. We would like to thank the participant of this study for their generous contributions