A soft computing decision support framework for e-learning

Tesi per compendi de publicacions.Supported by technological development and its impact on everyday activities, e-Learning and b-Learning (Blended Learning) have experienced rapid growth mainly in higher education and training. Its inherent ability to break both physical and cultural distances, to disseminate knowledge and decrease the costs of the teaching-learning process allows it to reach anywhere and anyone. The educational community is divided as to its role in the future. It is believed that by 2019 half of the world's higher education courses will be delivered through e-Learning. While supporters say that this will be the educational mode of the future, its detractors point out that it is a fashion, that there are huge rates of abandonment and that their massification and potential low quality, will cause its fall, assigning it a major role of accompanying traditional education. There are, however, two interrelated features where there seems to be consensus. On the one hand, the enormous amount of information and evidence that Learning Management Systems (LMS) generate during the e-Learning process and which is the basis of the part of the process that can be automated. In contrast, there is the fundamental role of e-tutors and etrainers who are guarantors of educational quality. These are continually overwhelmed by the need to provide timely and effective feedback to students, manage endless particular situations and casuistics that require decision making and process stored information. In this sense, the tools that e-Learning platforms currently provide to obtain reports and a certain level of follow-up are not sufficient or too adequate. It is in this point of convergence Information-Trainer, where the current developments of the LMS are centered and it is here where the proposed thesis tries to innovate. This research proposes and develops a platform focused on decision support in e-Learning environments. Using soft computing and data mining techniques, it extracts knowledge from the data produced and stored by e-Learning systems, allowing the classification, analysis and generalization of the extracted knowledge. It includes tools to identify models of students' learning behavior and, from them, predict their future performance and enable trainers to provide adequate feedback. Likewise, students can self-assess, avoid those ineffective behavior patterns, and obtain real clues about how to improve their performance in the course, through appropriate routes and strategies based on the behavioral model of successful students. The methodological basis of the mentioned functionalities is the Fuzzy Inductive Reasoning (FIR), which is particularly useful in the modeling of dynamic systems. During the development of the research, the FIR methodology has been improved and empowered by the inclusion of several algorithms. First, an algorithm called CR-FIR, which allows determining the Causal Relevance that have the variables involved in the modeling of learning and assessment of students. In the present thesis, CR-FIR has been tested on a comprehensive set of classical test data, as well as real data sets, belonging to different areas of knowledge. Secondly, the detection of atypical behaviors in virtual campuses was approached using the Generative Topographic Mapping (GTM) methodology, which is a probabilistic alternative to the well-known Self-Organizing Maps. GTM was used simultaneously for clustering, visualization and detection of atypical data. The core of the platform has been the development of an algorithm for extracting linguistic rules in a language understandable to educational experts, which helps them to obtain patterns of student learning behavior. In order to achieve this functionality, the LR-FIR algorithm (Extraction of Linguistic Rules in FIR) was designed and developed as an extension of FIR that allows both to characterize general behavior and to identify interesting patterns. In the case of the application of the platform to several real e-Learning courses, the results obtained demonstrate its feasibility and originality. The teachers' perception about the usability of the tool is very good, and they consider that it could be a valuable resource to mitigate the time requirements of the trainer that the e-Learning courses demand. The identification of student behavior models and prediction processes have been validated as to their usefulness by expert trainers. LR-FIR has been applied and evaluated in a wide set of real problems, not all of them in the educational field, obtaining good results. The structure of the platform makes it possible to assume that its use is potentially valuable in those domains where knowledge management plays a preponderant role, or where decision-making processes are a key element, e.g. ebusiness, e-marketing, customer management, to mention just a few. The Soft Computing tools used and developed in this research: FIR, CR-FIR, LR-FIR and GTM, have been applied successfully in other real domains, such as music, medicine, weather behaviors, etc.Soportado por el desarrollo tecnológico y su impacto en las diferentes actividades cotidianas, el e-Learning (o aprendizaje electrónico) y el b-Learning (Blended Learning o aprendizaje mixto), han experimentado un crecimiento vertiginoso principalmente en la educación superior y la capacitación. Su habilidad inherente para romper distancias tanto físicas como culturales, para diseminar conocimiento y disminuir los costes del proceso enseñanza aprendizaje le permite llegar a cualquier sitio y a cualquier persona. La comunidad educativa se encuentra dividida en cuanto a su papel en el futuro. Se cree que para el año 2019 la mitad de los cursos de educación superior del mundo se impartirá a través del e-Learning. Mientras que los partidarios aseguran que ésta será la modalidad educativa del futuro, sus detractores señalan que es una moda, que hay enormes índices de abandono y que su masificación y potencial baja calidad, provocará su caída, reservándole un importante papel de acompañamiento a la educación tradicional. Hay, sin embargo, dos características interrelacionadas donde parece haber consenso. Por un lado, la enorme generación de información y evidencias que los sistemas de gestión del aprendizaje o LMS (Learning Management System) generan durante el proceso educativo electrónico y que son la base de la parte del proceso que se puede automatizar. En contraste, está el papel fundamental de los e-tutores y e-formadores que son los garantes de la calidad educativa. Éstos se ven continuamente desbordados por la necesidad de proporcionar retroalimentación oportuna y eficaz a los alumnos, gestionar un sin fin de situaciones particulares y casuísticas que requieren toma de decisiones y procesar la información almacenada. En este sentido, las herramientas que las plataformas de e-Learning proporcionan actualmente para obtener reportes y cierto nivel de seguimiento no son suficientes ni demasiado adecuadas. Es en este punto de convergencia Información-Formador, donde están centrados los actuales desarrollos de los LMS y es aquí donde la tesis que se propone pretende innovar. La presente investigación propone y desarrolla una plataforma enfocada al apoyo en la toma de decisiones en ambientes e-Learning. Utilizando técnicas de Soft Computing y de minería de datos, extrae conocimiento de los datos producidos y almacenados por los sistemas e-Learning permitiendo clasificar, analizar y generalizar el conocimiento extraído. Incluye herramientas para identificar modelos del comportamiento de aprendizaje de los estudiantes y, a partir de ellos, predecir su desempeño futuro y permitir a los formadores proporcionar una retroalimentación adecuada. Así mismo, los estudiantes pueden autoevaluarse, evitar aquellos patrones de comportamiento poco efectivos y obtener pistas reales acerca de cómo mejorar su desempeño en el curso, mediante rutas y estrategias adecuadas a partir del modelo de comportamiento de los estudiantes exitosos. La base metodológica de las funcionalidades mencionadas es el Razonamiento Inductivo Difuso (FIR, por sus siglas en inglés), que es particularmente útil en el modelado de sistemas dinámicos. Durante el desarrollo de la investigación, la metodología FIR ha sido mejorada y potenciada mediante la inclusión de varios algoritmos. En primer lugar un algoritmo denominado CR-FIR, que permite determinar la Relevancia Causal que tienen las variables involucradas en el modelado del aprendizaje y la evaluación de los estudiantes. En la presente tesis, CR-FIR se ha probado en un conjunto amplio de datos de prueba clásicos, así como conjuntos de datos reales, pertenecientes a diferentes áreas de conocimiento. En segundo lugar, la detección de comportamientos atípicos en campus virtuales se abordó mediante el enfoque de Mapeo Topográfico Generativo (GTM), que es una alternativa probabilística a los bien conocidos Mapas Auto-organizativos. GTM se utilizó simultáneamente para agrupamiento, visualización y detección de datos atípicos. La parte medular de la plataforma ha sido el desarrollo de un algoritmo de extracción de reglas lingüísticas en un lenguaje entendible para los expertos educativos, que les ayude a obtener los patrones del comportamiento de aprendizaje de los estudiantes. Para lograr dicha funcionalidad, se diseñó y desarrolló el algoritmo LR-FIR, (extracción de Reglas Lingüísticas en FIR, por sus siglas en inglés) como una extensión de FIR que permite tanto caracterizar el comportamiento general, como identificar patrones interesantes. En el caso de la aplicación de la plataforma a varios cursos e-Learning reales, los resultados obtenidos demuestran su factibilidad y originalidad. La percepción de los profesores acerca de la usabilidad de la herramienta es muy buena, y consideran que podría ser un valioso recurso para mitigar los requerimientos de tiempo del formador que los cursos e-Learning exigen. La identificación de los modelos de comportamiento de los estudiantes y los procesos de predicción han sido validados en cuanto a su utilidad por los formadores expertos. LR-FIR se ha aplicado y evaluado en un amplio conjunto de problemas reales, no todos ellos del ámbito educativo, obteniendo buenos resultados. La estructura de la plataforma permite suponer que su utilización es potencialmente valiosa en aquellos dominios donde la administración del conocimiento juegue un papel preponderante, o donde los procesos de toma de decisiones sean una pieza clave, por ejemplo, e-business, e-marketing, administración de clientes, por mencionar sólo algunos. Las herramientas de Soft Computing utilizadas y desarrolladas en esta investigación: FIR, CR-FIR, LR-FIR y GTM, ha sido aplicadas con éxito en otros dominios reales, como música, medicina, comportamientos climáticos, etc.Postprint (published version

Share and reuse of context metadata resulting from interactions between users and heterogeneous web-based learning environments

L'intérêt pour l'observation, l'instrumentation et l'évaluation des systèmes éducatifs en ligne est devenu de plus en plus important ces dernières années au sein de la communauté des Environnements Informatique pour l'Apprentissage Humain (EIAH). La conception et le développement d'environnements d'apprentissage en ligne adaptatifs (AdWLE - Adaptive Web-based Learning Environments) représentent une préoccupation majeure aujourd'hui, et visent divers objectifs tels que l'aide au processus de réingénierie, la compréhension du comportement des utilisateurs, ou le soutient à la création de systèmes tutoriels intelligents. Ces systèmes gèrent leur processus d'adaptation sur la base d'informations détaillées reflétant le contexte dans lequel les étudiants évoluent pendant l'apprentissage : les ressour-ces consultées, les clics de souris, les messages postés dans les logiciels de messagerie instantanée ou les forums de discussion, les réponses aux questionnaires, etc. Les travaux présentés dans ce document sont destinés à surmonter certaines lacunes des systèmes actuels en fournissant un cadre dédié à la collecte, au partage et à la réutilisation du contexte représenté selon deux niveaux d'abstraction : le contexte brut (résultant des interactions directes entre utilisateurs et applications) et le contexte inféré (calculé à partir des données du contexte brut). Ce cadre de travail qui respecte la vie privée des usagers est fondé sur un standard ouvert dédié à la gestion des systèmes, réseaux et applications. Le contexte spécifique aux outils hétérogènes constituant les EIAHs est représenté par une structure unifiée et extensible, et stocké dans un référentiel central. Pour faciliter l'accès à ce référentiel, nous avons introduit une couche intermédiaire composée d'un ensemble d'outils. Certains d'entre eux permettent aux utilisateurs et applications de définir, collecter, partager et rechercher les données de contexte qui les intéressent, tandis que d'autres sont dédiés à la conception, au calcul et à la délivrance des données de contexte inférées. Pour valider notre approche, une mise en œuvre du cadre de travail proposé intègre des données contextuelles issues de trois systèmes différents : deux plates-formes d'apprentissage Moodle (celle de l'Université Paul Sabatier de Toulouse, et une autre déployée dans le cadre du projet CONTINT financé par l'Agence Nationale de la Recherche) et une instanciation locale du moteur de recherche de la fondation Ariadne. A partir des contextes collectés, des indicateurs pertinents ont été calculés pour chacun de ces environnements. En outre, deux applications qui exploitent cet ensemble de données ont été développées : un système de recommandation personnalisé d'objets pédagogiques ainsi qu'une application de visualisation fondée sur les technologies tactiles pour faciliter la navigation au sein de ces données de contexte.An interest for the observation, instrumentation, and evaluation of online educational systems has become more and more important within the Technology Enhanced Learning community in the last few years. Conception and development of Adaptive Web-based Learning Environments (AdWLE) in order to facilitate the process of re-engineering, to help understand users' behavior, or to support the creation of Intelligent Tutoring Systems represent a major concern today. These systems handle their adaptation process on the basis of detailed information reflecting the context in which students evolve while learning: consulted resources, mouse clicks, chat messages, forum discussions, visited URLs, quizzes selections, and so on. The works presented in this document are intended to overcome some issues of the actual systems by providing a privacy-enabled framework dedicated to the collect, share and reuse of context represented at two abstraction levels: raw context (resulting from direct interactions between users and applications) and inferred context (calculated on the basis of raw context). The framework is based on an open standard dedicated to system, network and application management, where the context specific to heterogeneous tools is represented as a unified and extensible structure and stored into a central repository. To facilitate access to this context repository, we introduced a middleware layer composed of a set of tools. Some of them allow users and applications to define, collect, share and search for the context data they are interested in, while others are dedicated to the design, calculation and delivery of inferred context. To validate our approach, an implementation of the suggested framework manages context data provided by three systems: two Moodle servers (one running at the Paul Sabatier University of Toulouse, and the other one hosting the CONTINT project funded by the French National Research Agency) and a local instantiation of the Ariadne Finder. Based on the collected context, relevant indicators have been calculated for each one of these environments. Furthermore, two applications which reuse the encapsulated context have been developed on top of the framework: a personalized system for recommending learning objects to students, and a visualization application which uses multi-touch technologies to facilitate the navigation among collected context entities

Contribución a la Aplicación de Técnicas de Inteligencia Artificial para el diseño efectivo de Sistemas Adaptativos de Aprendizaje Competitivo

El objetivo principal de esta tesis consiste en la propuesta y validación de métodos basados en técnicas de Inteligencia Artificial para la estimación del nivel de dificultad de los desafíos propuestos en el entorno On-line de Aprendizaje Competitivo QUESTTOURnament, que permita el posterior establecimiento de concursos o itinerarios de aprendizaje para grupos de alumnos según su nivel de conocimiento. QUESTOURnament es una herramienta telemática que permite el desarrollo de concursos on-line. Mediante estudios de estado de arte de sistemas de aprendizaje competitivo y de sistemas de aprendizaje adaptativos se han identificado las características de estos últimos que permitirían potenciar las ventajas e inconvenientes que presentan los sistemas competitivos y, en concreto, el sistema QUESTOURnament. Así, se propone el sistema QUESTOURnament adaptativo y se diseña y valida una solución basada en algoritmos genéticos y lógica difusa que permite estimar el nivel de dificultad de las preguntas en QUESTOURnament.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería Telemátic

Mellem design, didaktik og diskurs: En analyse af det adaptive læremiddel Rhapsode til matematikundervisning i folkeskolen

I denne artikel præsenteres resultater fra en undersøgelse af det adaptive læremiddel, Rhapsode, til matematik i grundskolen ud fra et tredimensionelt fokus, der omfatter diskurs, didaktik og design. Vores formål med den flerstrengede analysestrategi er dels at sætte (fag)didaktikken i centrum, dvs. hvad vi vil med faget, og hvad den det adaptive design gør ved fagligheden, dels at sætte den adaptive teknologi ind i en større diskursiv kontekst. Dette står i kontrast til det snævre fokus på at udvikle effektive adaptive maskiner, der ellers præger forskning inden for feltet. Resultatet er et skærpet fokus på den dilemmafyldte brug af adaptiv teknologi. Analysen peger på, at der både er behov for a) redesign og udvikling af den didaktiske rammesætning af læremidlets adaptivitet, b) redidaktisering og nyfortolkning af det didaktiske potentiale i praksis samt c) regulering og demokratisk kontrol af de nye forretningsmodeller, der gør data og algoritmer til genstand for forretningsudvikling. This article presents results from a study of the adaptive learning tool, Rhapsode, for mathematics in primary school based on a three-dimensional focus that includes discourse, didactics and design. Our purpose with the multi-stranded analysis strategy is partly to put (subject) didactics in the center, ie. what we want with the subject, and what the adaptive design does to the professionalism, partly to put the adaptive technology into a larger discursive context. This stands in contrast to the narrow focus on developing efficient adaptive machines that otherwise characterizes research in the field. The result is a sharpened focus on the dilemma-filled use of adaptive technology. The analysis indicates that there is a need for both a) redesign and development of the didactic framework of the adaptive material of the teaching aid, b) redidactization and reinterpretation of the didactic potential in practice and c) regulation and democratic control of the new business models that make data and algorithms for business development

Adaptive Instruction for Elementary School Children: The Interplay of Giftedness, Working Memory, and Hypermedia Learning

Several studies have shown that promotion offers for gifted students have positive effects on the students’ educational achievement and development (e.g., Wai, Lubinski, Benbow, & Steiger, 2010). However, it is not entirely clear which promotion offers actually work best for gifted children. According to aptitude-treatment interaction (ATI) research (Cronbach & Snow, 1977), promotion or learning offers that are matched to a learner’s specific prerequisites are assumed to be most beneficial. In line with this, promotion offers that take advantage of the specific aptitudes of gifted children should be most effective for this ability group. Unfortunately, however, studies that focus on the particular aptitudes of gifted children in order to develop appropriate learning offers are rare. Therefore, the present dissertation aimed at closing this research gap by not only exploring the specific learner characteristics of gifted children, but also by investigating whether learning offers that are designed based on the particular strengths of these children might be more beneficial than other, more common learning offers. More precisely, it was first investigated whether the construct of working memory (WM; Baddeley, 2002) represents a crucial cognitive characteristic in gifted children, even beyond intelligence. Second, it was explored whether learning offers that capitalize on the students’ high WM resources, such as hypermedia environments, would be more beneficial for these students than learning offers that require lower WM resources. To this end, the present dissertation focused on the students’ learning performance as well as on their navigational processing during hypermedia exploration. In total, three empirical studies were conducted within the present dissertation.Die Wirksamkeit von Angeboten für hochbegabte Kinder zur Förderung ihrer schulischen Leistung und kognitiven Entwicklung konnte bereits mehrfach gezeigt werden (z.B. Wai, Lubinski, Benbow, & Steiger, 2010). Allerdings ist bisher unklar, welche Förderangebote für diese Zielgruppe am effektivsten sind. Basierend auf dem Aptitude-Treatment Interaction Ansatz (Cronbach & Snow, 1977) sind generell solche Förder- oder Lernangebote am effektivsten, die auf die spezifischen Fähigkeiten einer Person abgestimmt sind; im vorliegenden Fall also auf die spezifischen Fähigkeiten von Hochbegabten. Leider gibt es bislang kaum Studien, die basierend auf den spezifischen Lernvoraussetzungen bzw. Fähigkeiten von hochbegabten Kindern adäquate Lernangebote entwickelt haben. Aus diesem Grund war das Ziel der vorliegenden Dissertation sich genau mit dieser For-schungslücke zu beschäftigen. Im Rahmen von drei Studien sollte neben der Untersuchung und Feststellung der spezifischen Lernvoraussetzungen von hochbegabten Kindern auch die Effektivität von entsprechenden Lernangeboten, die auf diese Voraussetzungen angepasst sind, überprüft werden. So wurde in Studie 1 konkret untersucht, ob das Arbeitsgedächtnis (Baddeley, 2002) neben der Intelligenz eine essentielle kognitive Charakteristik von hochbe-gabten Kindern darstellt. Weiterhin wurde in Studie 2 untersucht, ob Lernangebote, die das Arbeitsgedächtnis besonders beanspruchen, so wie zum Beispiel Hypermedia Lernumgebun-gen, zur kognitiven Förderung von Kindern mit entsprechend hohen Arbeitsgedächtnisres-sourcen geeigneter sind als Lernangebote mit geringerer Beanspruchung des Arbeitsgedächt-nisses. Zur Beurteilung der Effektivität wurden Lern- und Leistungsmaße der Kinder beim Explorieren der Hypermedia Lernumgebung herangezogen. In Studie 3 stand das Navigationsverhalten der Kinder beim Explorieren der Hypermedia Lernumgebung im Fokus. So sollte überprüft werden, ob bestimmte Navigationsstrategien für den höheren Lernerfolg von Kindern mit hohen Arbeitsgedächtnisressourcen verantwortlich sind. Die Ergebnisse und Implikationen der drei Studien werden dargestellt und zusammenfassend diskutiert

An Experimental Evaluation of Logiocando, an Intelligent Tutoring Hypermedia System

Several hypermedia learning environments have been developed in the last years, with the objective of helping students to acquire specific concepts in a given domain, as well as problem-solving abilities. However, there are still many different and sometimes conflicting claims about learning effectiveness using such environments. We present an empirical evaluation of a learning hypermedia with a tutorial component that exploits Artificial Intelligence techniques. This hypermedia, called Logiocando, has been designed for use by a special category of user, namely children of the fourth level of primary school (9-10 years old), to help them to learn basic concepts of logic. This category of user demands special attention to usability of the hypermedia. For this reason, design and development of the hypermedia have been carried out following a learner-centred methodology, in order to build a system that satisfies clear usability objectives. The aim of the study herein reported was to evaluate the learning effectiveness of Logiocando and to estimate the difference between two approaches: computer-based using a hypermedia system, and traditional, namely a typical lesson in the classroom. The results have shown that the hypermedia can be considered a valid support in the process of learning and deepening a topic

5th International Open and Distance Learning Conference Proceedings Book = 5. Uluslararası Açık ve Uzaktan Öğrenme Konferansı Bildiri Kitabı

In celebration of our 40th anniversary in open and distance learning, we are happy and proud to organize the 5th International Open & Distance Learning Conference- IODL 2022, which was held at Anadolu University, Eskişehir, Türkiye on 28-30 September 2022. After the conferences in 2002, 2006, 2010, and 2019, IODL 2022 is the 5th IODL event hosted by Anadolu University Open Education System (OES)