Personalized Service-Oriented E-Learning Environments

6 pages, 4 figures.The social component of Web 2.0-related services is providing a new open and personal approach to how we expect things to solve problems in our information-driven world. In particular, students' learning needs require open, personal e-learning systems adapted to life-long learning needs in a rapidly changing environment. It therefore shouldn't be surprising that a new wave of ideas centered on pervasive systems has drawn so much attention. This article analyzes current trends in the evolution of e-learning architectures and describes a new architecture that captures the needs of both formal (instructor-led) and informal (student-led) learning environments.Spain’s Programa Nacional de Tecnologías de la Sociedad de la Información supported this research through projects TSI2005-08225-C07-01 and -02.Publicad

SPOCs for Remedial Education: Experiences at the Universidad Carlos III de Madrid

The Universidad Carlos III de Madrid has been offering several face-to-face remedial courses for freshmen to review or learn concepts and practical skills that they should know before starting their degree programme. During the last two years, our University has adopted MOOC-like technologies to support some of these courses so that a "fipping the classroom" methodology can be applied to a particular small educational context. This paper gathers a list of issues and challenges encountered when using Khan Academy technologies for small private online courses (SPOCs). These issues and challenges include the absence of a single platform that supports all the requirements, the need for integration of different learning platforms, the complexity of the authoring process, the need for an adaptation of gamifcation during the learning process and the adjustment of the learning analytics functionality. In addition, some lessons learned are presented, as well as specifc actions taken in response, where MOOCs do not replace teachers and classrooms for these remedial courses, but improve their effectiveness.This work was partially funded by the EEE project, “Plan Nacional de I+D+i TIN2011-28308-C03-01” and the “eMadrid: Investigación y desarrollo de tecnologías para el e-learning en la Comunidad de Madrid” project (S2009/TIC-1650)”. The last author wishes to acknowledge support from Fundación CajaMadrid to visit Harvard University and MIT in the academic year 2012-13

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)

Design, Implementation and Evaluation of SPOCs at the Universidad Carlos III de Madrid

The Universidad Carlos III de Madrid has been offering several face-to-face remedial courses for new students to review or learn concepts and practical skills that they should know before starting their degree program. During 2012 and 2013, our University adopted MOOC-like technologies to support some of these courses so that a blended learning methodology could be applied in a particular educational context, i.e. by using SPOCs (Small Private Online Courses). This paper gathers a list of issues, challenges and solutions when implementing these SPOCs. Based on these challenges and issues, a design process is proposed for the implementation of SPOCs. In addition, an evaluation is presented of the different use of the offered courses based on indicators such as the number of videos accessed, number of exercises accessed, number of videos completed, number of exercises correctly solved or time spent on the platform.Work partially funded by the RESET project under grant no. TIN2014-53199-C3-1-R (funded by the Spanish Ministry of Economy and Competitiveness), the REMEDISS project under grant no. IPT-2012-0882-430000 (funded by the Spanish Ministry of Economy and Competitiveness) and the “eMadrid” project (funded by the Regional Government of Madrid) under grant no. S2013/ICE-2715. Carlos Delgado Kloos wishes to acknowledge support from Fundación CajaMadrid to visit Harvard University and MIT in the academic year 2012-13

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

Evaluating emotion visualizations using AffectVis, an affect-aware dashboard for students

Purpose: The purpose of this paper is to evaluate four visualizations that represent affective states of students. Design/methodology/approach: An empirical-experimental study approach was used to assess the usability of affective state visualizations in a learning context. The first study was conducted with students who had knowledge of visualization techniques (n=10). The insights from this pilot study were used to improve the interpretability and ease of use of the visualizations. The second study was conducted with the improved visualizations with students who had no or limited knowledge of visualization techniques (n=105). Findings: The results indicate that usability, measured by perceived usefulness and insight, is overall acceptable. However, the findings also suggest that interpretability of some visualizations, in terms of the capability to support emotional awareness, still needs to be improved. The level of students" awareness of their emotions during learning activities based on the visualization interpretation varied depending on previous knowledge of information visualization techniques. Awareness was found to be high for the most frequently experienced emotions and activities that were the most frustrating, but lower for more complex insights such as interpreting differences with peers. Furthermore, simpler visualizations resulted in better outcomes than more complex techniques. Originality/value: Detection of affective states of students and visualizations of these states in computer-based learning environments have been proposed to support student awareness and improve learning. However, the evaluation of visualizations of these affective states with students to support awareness in real life settings is an open issue.The work is partially supported by the eMadrid project (funded by the Regional Government of Madrid) under grant no S2013/ICE-2715, and the RESET project (Ministry of Economy and Competitiveness) under grant RESET TIN2014-53199-C3-1-R. The research is partially financed by the SURF Foundation of the Netherlands and the KU Leuven Research Council (Grant Agreement No C24/16/017, PDM16/044)

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

Conversational agent for supporting learners on a MOOC on programming with Java

One important problem in MOOCs is the lack of personalized support from teachers. Conversational agents arise as one possible solution to assist MOOC learners and help them to study. For example, conversational agents can help review key concepts of the MOOC by asking questions to the learners and providing examples. JavaPAL, a voice-based conversational agent for supporting learners on a MOOC on programming with Java offered on edX. This paper evaluates JavaPAL from different perspectives. First, the usability of JavaPAL is analyzed, obtaining a score of 74.41 according to a System Usability Scale (SUS). Second, learners’ performance is compared when answering questions directly through JavaPAL and through the equivalent web interface on edX, getting similar results in terms of performance. Finally, interviews with JavaPAL users reveal that this conversational agent can be helpful as a complementary tool for the MOOC due to its portability and flexibility compared to accessing the MOOC contents through the web interface.This work was supported in part by the FEDER/Ministerio de Ciencia, Innovación y Universidades-Agencia Estatal de Investigación, through the Smartlet and H2O Learning projects under Grant 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 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