Joaquim Moré

A Semi-automatic System to Detect Relevant Learning Content for Each Subject

Today, the crisis has worsened the panorama for Universities, placing new constraints that require being more sustainable economically. In addition, universities will also have to improve their research and teaching in order to obtain more research funds and attract more students. In this panorama, analytics can be a very useful tool since it allows academics (and university managers) to get a more thorough view of their context, to better understand the environment, and to identify potential improvements. Some analytics have been done under the names of Learning analytics, Academic analytics, Educational Data Mining and etcetera. However, these systems, under our humble opinion, only take into account the small part of data related to the problem, but not contextual data. In order to perform analytics efficiently and reproduce their results easily in other contexts, it is necessary to have as much information as possible about the context. For example, when communication forums are analyzed to see the concepts in which students have more doubts, it is important to analyze also what concepts are taught in the subject. Having access to both sources, more information can be obtained and may help to discover not only the problem (a concept is difficult for students) but also its cause (maybe it is not explained in the materials of the course). The work presented in the paper proposes a novel approach to work in that direction: gathering information from different aspects within subjects. In particular, the paper presents an approach that uses natural language processing techniques to analyze the subjects materials in order to discover which concepts are taught and their importance in the subject. The contribution of the paper is a system that allows obtaining a better understanding of subjects. The results can be used for analyzing the suitability of materials to subjects and to enrich and contextualize other analytical processes.

An Answering System for Questions Asked by Students in an e-Learning Context

In this article, we present a system that helps tutors to answer questions asked by their students at an online university: the Open University of Catalonia (UOC). Communication between UOC students and their tutors is fully online; students ask questions and tutors answer them by e-mail. The system, which is currently being developed at the UOC’s Office of Learning Technologies (OLT), aims to find multilingual contexts with useful information to enable tutors to give fast, appropriate answers to students. These contexts are extracted from course learning materials, from previous messages posted on subject discussion boards, and also from articles and other sources of information available on the Internet. Apart from helping tutors to find better answers, the system is also useful for updating their knowledge and contributing to their lifelong learning.ResumenEn este artículo presentamos un sistema que ayuda a los docentes a responder las preguntas de sus alumnos en una universidad virtual, concretamente la Universitat Oberta de Catalunya (UOC). La comunicación entre alumno y docente se realiza de forma totalmente virtual: las preguntas y las respuestas se formulan y contestan mediante correo electrónico. El sistema, que se está desarrollando en el Área de Tecnología Educativa de la UOC, tiene como principal objetivo encontrar contextos multilingües con información útil para responder al estudiante de forma rápida y adecuada. Los contextos se extraen de los materiales del curso, los foros de participación de la asignatura, artículos y otras fuentes de información disponibles en internet. Además de ayudar a los docentes a encontrar mejores respuestas, el sistema también es útil para actualizar sus conocimientos y desarrollar su aprendizaje permanente.

Towards a Web-Based Teaching Tool to Measure and Represent the Emotional Climate of Virtual Classrooms

This paper presents the first results of a teaching innovation project named “Emotional Thermometer for Teaching” (ETT) carried out at the Universitat Oberta de Catalunya. The ETT project intersects the scopes of eLearning and Affective Computing with the aim of collecting and managing emotional information of online students during their learning process. Such information allows lecturers to monitor the overall emotional climate of their virtual classrooms whilst detecting critical moments for timely interventions, such as assisting in certain learning tasks that generate negative emotions (anxiety, fear, etc.). To this end, an innovative teaching tool named ETT was developed as a functional indicator to measure and represent the classroom emotional climate, which is dynamically evolving as the course goes by. In this paper, the technical development of the ETT tool is described that meets the challenging requirement of correctly identifying the overall emotional climate of virtual classrooms from the posts sent by students to in-class forums. First, a machine learning approach combined with Natural Language Processing techniques is described to automatically classify posts in terms of positive, neutral and negative emotions. Then, a web-based graphical tool is presented to visualize the calculated emotional climate of the classroom and its evolution over time. Finally, the post classification approach is technically tested and the initial results are discussed.