Contributions to affective learning through the use of data analysis, visualizations and recommender sytems

Student modeling is an important issue in telematics learning environments, e.g. learning resources can be adapted based on the students' information. An emergent area of student modeling is the inclusion of affective information. The improvement of emotion detectors based on the students' events in different telematics learning environments is an open issue. Moreover, there is a need of proposing and evaluating new visualizations involving affective information, and proposing generic solutions for the recommendation of learning materials based on the affective information. This PhD proposes two different models for the detection of emotions in two different telematics learning environments. The first model uses a Hidden Markov Model to infer the emotions in a programming learning environment in which students should use different tools to learn how to program. The second model uses a set of rules to infer the emotions in a Massive Open Online Course platform in which students should solve exercises and watch videos. An evaluation of the first model for the detection of emotions was performed using a controlled experiment, comparing the results of the model with the students' answers regarding their emotions in different instants of times. The results showed that the model was not able to detect accurately the students' answers regarding their emotions. Other models of the literature applied in other learning environments were tested and they were not able to predict accurately the students' answers regarding their emotions. Therefore, the detection of emotions based on students' events in these types of environments might not be feasible, or the reference data of students' answers to a survey with different questions about emotions should be redefined. Moreover, this PhD proposes a set of affective-related visualizations for learning environments. Some of these visualizations only involve affective information, while others combine this affective information with other related to the students' activities with the learning platforms. Some of these visualizations were evaluated with real students and results showed a good usability, usefulness and effectiveness. Finally, this work proposes a generic framework for enabling the recommendation of learning resources based on affective information. The solution includes an Application Programming Interface for the definition of the different possible events. A specific implementation of this recommender has been developed as a plugin of the ROLE SDK platform.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: Carlos Enrique Palau Salvador.- Secretario: Eva María Méndez Rodríguez, Eva Maria.- Vocal: Ruth Cobos Pére

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)

Architecture for Collaborative Learning Activities in Hybrid Learning Environments

3D virtual worlds are recognized as collaborative learning environments. However, the underlying technology is not sufficiently mature and the virtual worlds look cartoonish, unlinked to reality. Thus, it is important to enrich them with elements from the real world to enhance student engagement in learning activities. Our approach is to build learning environments where participants can either be in the real world or in its mirror world while sharing the same hybrid space in a collaborative learning experience. This paper focuses on the system architecture and a usability study of a proof-of-concept for these hybrid learning environments. The architecture allows the integration of the real world and its 3D virtual mirror; the exchange and geolocalization of multimodal information, and also the orchestration of learning activities. The results of the usability evaluation show positive engagement effects on participants in the mirror world and, to a lesser extent, on those in the real world.This research has been partially supported by the following projects: “España Virtual” within the Ingenio 2010 program, subcontracted by Elecnor Deimos, "EEE" (TIN2011-28308-C03-01) funded by the Spanish National Plan of Research, Development and Innovation, and "eMadrid", S2009/TIC-1650 “Investigación y Desarrollo de tecnologías para el e-learning en la Comunidad de Madrid” funded by the Region of Madrid.Publicad

A Generic Architecture for Emotion-based Recommender Systems in Cloud Learning Environments

Cloud technology has provided a set of tools to learners and tutors to create a virtual personal learning environment. As these tools only support basic tasks, users of learning environments are looking for specialized tools to exploit the uncountable learning elements available on the internet. Thus, one of the most common functionalities in cloud-based learning environments is the recommendation of learning elements and several approaches have been proposed to deploy recommender systems into an educational environment. Currently, there is an increasing interest in including affective information into the process to generate the recommendations for the learner; and services offering this functionality on cloud environments are scarce. Hence in this paper, we propose a generic cloud-based architecture for a system that recommends learning elements according to the affective state of the learner. Furthermore, we provide the description of some use cases along with the details of the implementation of one of them. We also provide a discussion on the advantages and disadvantages of the proposal.Work partially funded by the EEE project, “Plan Nacional de I+D+I TIN2011-28308-C03-01”, the “Emadrid: Investigación y desarrollo de tecnologías para el e-learning en la Comunidad de Madrid” project 2009/TIC-1650), and “Consejo Social - Universidad Carlos III de Madrid”.Publicad

MyLearningMentor: a mobile App to support learners participating in MOOCs

MOOCs have brought a revolution to education. However, their impact is mainly benefiting people with Higher Education degrees. The lack of support and personalized advice in MOOCs is causing that many of the learners that have not developed work habits and self-learning skills give them up at the first obstacle, and do not see MOOCs as an alternative for their education and training. My Learning Mentor (MLM) is a mobile application that addresses the lack of support and personalized advice for learners in MOOCs. This paper presents the architecture of MLM and practical examples of use. The architecture of MLM is designed to provide MOOC participants with a personalized planning that facilitates them following up the MOOCs they enroll. This planning is adapted to learners' profiles, preferences, priorities and previous performance (measured in time devoted to each task). The architecture of MLM is also designed to provide tips and hints aimed at helping learners develop work habits and study skills, and eventually become self-learners.This work has been funded by the Spanish Ministry of Economy and Competitiveness Project TIN2011-28308-C03-01, the Regional Government of Madrid project S2013/ICE-2715, and the postdoctoral fellowship Alliance 4 Universities. The authors would also like to thank Israel Gutiérrez-Rojas for his contributions to the ideas behind MLM and Ricardo García Pericuesta and Carlos de Frutos Plaza for their work implementing different parts of the architecture

Contributions to affective learning through the use of data analysis, visualizations and recommender sytems

El modelado del estudiante es una cuestión importante en los entornos de aprendizaje telemático, por ejemplo, los recursos de aprendizaje pueden adaptarse en función de la información de los estudiantes. Un área emergente del modelado de estudiantes es la inclusión de información afectiva. La mejora de los detectores de emociones basados en los eventos de los estudiantes en diferentes entornos de aprendizaje telemático es una cuestión abierta. Además, es necesario proponer y evaluar nuevas visualizaciones que incluyan información afectiva, y proponer soluciones genéricas para la recomendación de materiales de aprendizaje basados en la información afectiva. Este doctorado propone dos modelos diferentes para la detección de emociones en dos entornos de aprendizaje telemático diferentes. El primer modelo utiliza un Modelo de Markov Oculto para inferir las emociones en un entorno de aprendizaje de programación en el que los estudiantes deben utilizar diferentes herramientas para aprender a programar. El segundo modelo utiliza un conjunto de reglas para inferir las emociones en una plataforma de Curso Online Masivo y Abierto en el que los estudiantes deben resolver ejercicios y ver vídeos. Se realizó una evaluación del primer modelo para la detección de emociones mediante un experimento controlado, comparando los resultados del modelo con las respuestas de los estudiantes sobre sus emociones en diferentes instantes de tiempo. Los resultados mostraron que el modelo no fue capaz de detectar con precisión las respuestas de los estudiantes sobre sus emociones. Se probaron otros modelos de la literatura aplicados en otros entornos de aprendizaje y no fueron capaces de predecir con exactitud las respuestas de los alumnos sobre sus emociones. Por lo tanto, la detección de emociones basada en los eventos de los estudiantes en este tipo de entornos podría no ser factible, o bien habría que redefinir los datos de referencia de las respuestas de los estudiantes a una encuesta con diferentes preguntas sobre las emociones. Además, este doctorado propone un conjunto de visualizaciones relacionadas con la afectividad para entornos de aprendizaje. Algunas de estas visualizaciones sólo incluyen información afectiva, mientras que otras combinan esta información afectiva con otra relacionada con las actividades de los estudiantes con las plataformas de aprendizaje. Algunas de estas visualizaciones fueron evaluadas con estudiantes reales y los resultados mostraron una buena usabilidad, utilidad y efectividad. Finalmente, este trabajo propone un marco genérico para permitir la recomendación de recursos de aprendizaje basados en información afectiva. La solución incluye una interfaz de programación de aplicaciones para la definición de los diferentes eventos posibles. Se ha desarrollado una implementación específica de este recomendador como un plugin de la plataforma ROLE SDK.MadridES

ALAS-KA: a learning analytics extension for better understanding the learning process in the Khan Academy platform

The Khan Academy platform enables powerful on-line courses in which students can watch videos, solve exercises, or earn badges. This platform provides an advanced learning analytics module with useful visualizations. Nevertheless, it can be improved. In this paper, we describe ALAS-MA, which provides an extension of the learning analytics support for the Khan Academy platform. We herein present an overview of the architecture of ALAS-KA. In addition, we report the different types of visualizations and information provided by ALAS-MA, which have not been available previously in the Khan Academy platform. ALAS-MA includes new visualizations for the entire class and also for individual students. Individual visualizations can be used to check on the learning styles of students based on all the indicators available. ALAS-MA visualizations help teachers and students to make decisions in the learning process. The paper presents some guidelines and examples to help teachers make these decisions based on data from undergraduate courses, where ALAS-MA was installed. These courses (physics, chemistry, and mathematics) for freshmen were developed at Universidad Carlos HI de Madrid (UC3M) and were taken by more than 300 student

Designing your first MOOC from scratch: recommendations after teaching "Digital Education of the Future"

Proceedings of: 2nd MOOC European Stakeholders Summit, 10-12 February 2014, Lausanne (Switzerland).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.This work has been funded by the Spanish Ministry of Economy and Competitiveness Project TIN2011-28308-C03-01, the Regional Government of Madrid project S2009/TIC-1650, and the postdoctoral fellowship Alianza 4 Universidades. Prof. Carlos Delgado-Kloos wishes to acknowledge support from Fundación Caja Madrid to visit Harvard University and MIT in the academic year 2012/13. He also thanks numerous people from MIT, Harvard and edX for fruitful conversations carried out during this research stay