Sistemas de evaluación de los alumnos mediante test informatizados utilizando telefonía móvil

This paper describes a system for executing adaptive tests in mobile devices. Tests are generated in XML files using Test Editor that is a author tool integrated in AHA! system. These tests are interpreted by our system that can show to the user in theirs mobile devices such as mobile phone or PDA. Our test execution engine is a Midlet, that is a small Java application oriented to be executed in a mobile device. This application can execute classic or adaptive test of type activity (it can be executed all the times that students wants) or exam (it can be only executed one time). Studens have to identify with theirs logins the first time that they execute a test. The next times is not necessary due to it is stored in the RMS memory. In this memory, we also store the interface adaptable parameters such as the language, the font type, the number of screens in which the question will be shown, etc. Finally, after executing a test the obtained results are shonw to the students and they are also send it to the AHA! server by GPRS.Este artículo presenta un sistema para la ejecución de test adaptativos en dispositivos móviles. Los test son generados en forma de ficheros XML a través de la herramienta autor Test Editor que se encuentra integrada en el sistema AHA! y son interpretados por nuestro sistema que puede mostrarlos a los usuarios en su dispositivo móvil, ya sea teléfono móvil o PDA. Nuestro motor de ejecución de test es un Midlet que es una pequeña aplicación Java orientada a ser ejecutada en dispositivos móviles. Esta aplicación permite realizar test clásicos y adaptativos de tipo actividad y de tipo examen. Si la prueba es de tipo examen, ésta sólo podrá ser ejecutada una sola vez, en cambio si es de tipo actividad se podrá ejecutar tantas veces como se quiera. Los estudiantes deben de identificarse con su login la primera vez que ejecuta el test, ya que el resto de veces éste queda almacenado en memoria RMS. En esta memora tambien son almacenados los parámetros adaptables del interfaz, ya que los estudiantes podrán personalizar la configuración de el idioma, el tipo de fuente, el número de pantallas en que se muestran las preguntas, etc. Finalmente tras ejecutar un test los resultados obtenidos son mostrados a los estudiantes y son tambien enviados al servidor de AHA! a través de GPRS

Reducing gaps in quantitative association rules: A genetic programming free-parameter algorithm

The extraction of useful information for decision making is a challenge in many different domains. Association rule mining is one of the most important techniques in this field, discovering relationships of interest among patterns. Despite the mining of association rules being an area of great interest for many researchers, the search for well-grouped continuous values is still a challenge, discovering rules that do not comprise patterns which represent unnecessary ranges of values. Existing algorithms for mining association rules in continuous domains are mainly based on a non-deterministic search, requiring a high number of parameters to be optimised. These parameters hinder the mining process, and the algorithms themselves must be known to those data mining experts that want to use them. We therefore present a grammar guided genetic programming algorithm that does not require as many parameters as other existing approaches and enables the discovery of quantitative association rules comprising small-size gaps. The algorithm is verified over a varied set of data, comparing the results to other association rule mining algorithms from several paradigms. Additionally, some resulting rules from different paradigms are analysed, demonstrating the effectiveness of our model for reducing gaps in numerical features

Assignments as Influential Factor to Improve the Prediction of Student Performance in Online Courses

Studies on the prediction of student success in distance learning have explored mainly demographics factors and student interactions with the virtual learning environments. However, it is remarkable that a very limited number of studies use information about the assignments submitted by students as influential factor to predict their academic achievement. This paper aims to explore the real importance of assignment information for solving students’ performance prediction in distance learning and evaluate the beneficial effect of including this information. We investigate and compare this factor and its potential from two information representation approaches: the traditional representation based on single instances and a more flexible representation based on Multiple Instance Learning (MIL), focus on handle weakly labeled data. A comparative study is carried out using the Open University Learning Analytics dataset, one of the most important public datasets in education provided by one of the greatest online universities of United Kingdom. The study includes a wide set of different types of machine learning algorithms addressed from the two data representation commented, showing that algorithms using only information about assignments with a representation based on MIL can outperform more than 20% the accuracy with respect to a representation based on single instance learning. Thus, it is concluded that applying an appropriate representation that eliminates the sparseness of data allows to show the relevance of a factor, such as the assignments submitted, not widely used to date to predict students’ academic performance. Moreover, a comparison with previous works on the same dataset and problem shows that predictive models based on MIL using only assignments information obtain competitive results compared to previous studies that include other factors to predict students performance

Applying data mining to discover common learning routes in Moodle

En este artículo, aplicamos técnicas de minería de datos para descubrir rutas de aprendizaje frecuentes. Hemos utilizado datos de 84 estudiantes universitarios, seguidos en un curso online usando Moodle 2.0. Proponemos agrupar a los estudiantes, en primer lugar, a partir de los datos de una síntesis de uso de Moodle y/o las calificaciones finales de los alumnos en un curso. Luego, usamos los datos de los logs de Moodle sobre cada cluster/grupo de estudiantes separadamente con el fin de poder obtener más específicos y precisos modelos de procesos del comportamiento de los estudiantes.In this paper, we apply techniques data mining to discover common learning routes. We have used data from 84 undergraduate college students who followed an online course using Moodle 2.0. We propose to group students firstly starting from data about Moodle’s usage summary and/or the students’ final marks in the course. Then, we use data from Moodle’s logs about each cluster/group of students separately in order to be able to obtain more specific and accurate process models of students’ behaviour

Towards Portability of Models for Predicting Students’ Final Performance in University Courses Starting from Moodle Logs

Predicting students’ academic performance is one of the older challenges faced by the educational scientific community. However, most of the research carried out in this area has focused on obtaining the best accuracy models for their specific single courses and only a few works have tried to discover under which circumstances a prediction model built on a source course can be used in other different but similar courses. Our motivation in this work is to study the portability of models obtained directly from Moodle logs of 24 university courses. The proposed method intends to check if grouping similar courses by the degree or the similar level of usage of activities provided by the Moodle logs, and if the use of numerical or categorical attributes affect in the portability of the prediction models. We have carried out two experiments by executing the well-known classification algorithm over all the datasets of the courses in order to obtain decision tree models and to test their portability to the other courses by comparing the obtained accuracy and loss of accuracy evaluation measures. The results obtained show that it is only feasible to directly transfer predictive models or apply them to different courses with an acceptable accuracy and without losing portability under some circumstances

A review on data fusion in multimodal learning analytics and educational data mining

The new educational models such as smart learning environments use of digital and context-aware devices to facilitate the learning process. In this new educational scenario, a huge quantity of multimodal students' data from a variety of different sources can be captured, fused, and analyze. It offers to researchers and educators a unique opportunity of being able to discover new knowledge to better understand the learning process and to intervene if necessary. However, it is necessary to apply correctly data fusion approaches and techniques in order to combine various sources of multimodal learning analytics (MLA). These sources or modalities in MLA include audio, video, electrodermal activity data, eye-tracking, user logs, and click-stream data, but also learning artifacts and more natural human signals such as gestures, gaze, speech, or writing. This survey introduces data fusion in learning analytics (LA) and educational data mining (EDM) and how these data fusion techniques have been applied in smart learning. It shows the current state of the art by reviewing the main publications, the main type of fused educational data, and the data fusion approaches and techniques used in EDM/LA, as well as the main open problems, trends, and challenges in this specific research area

Algoritmos Evolutivos para Descubrimiento de Reglas de Predicción en la Mejora de Sistemas Educativos Adaptativos basados en Web

Este artículo muestra la utilización de los algoritmos evolutivos para el descubrimiento de reglas de predicción que se utilizarán en la mejora de Cursos Hipermedia Adaptativos basados en Web. Se ha desarrollado una herramienta de minería de datos específica para descubrir relaciones entre los datos de utilización recogidos durante las ejecuciones de los distintos alumnos. Esta información puede ser de gran utilidad para el profesor o autor del curso, para la toma de decisiones sobre qué modificaciones son las más adecuadas para mejorar el aprendizaje de los alumnos. Para la realización de la búsqueda de reglas de predicción se ha utilizado programación genética basada en gramáticas multi-objetivo y se han comparado con algoritmos clásicos de descubrimiento de reglas.In this paper we show the use of evolutionary algorithms for discovering prediction rules to improve web-based adaptive hypermedia courses. We have developed a specific data mining tool to discover relationship between the usage data pickup during the execution of different students. This information can be very useful to the courseware author in order to make decisions about what are the most appropriated modifications to improve the learning of the students. In order to do prediction rule discovering we have used multi-objective grammar-based genetic programming and we have compared it with other classic algorithm for rule discovering

Sistema de consulta de notas a través de Páginas Web y de Telefonía Móvil

In this paper we are going to present a system which lets any student at Cordoba University knows what the final qualifications he has obtained in the different subjects he is registered are. The sytem lets him consult the qualifications using Internet and mobile telephony. Teachers who are reagistered in the system can upload tha qualifications without addtional software, they only need the certificate file generated by the UcoActas application. Then, all the registered students in this subject and in this service, will receive an e-mail to notify them that they can consult the qualifications. Then, they can use a Web browser or they can use a specific mobile application to consult them. Currently we are doing functionality tests with voluntary students from different courses.En este artículo se presenta un sistema que permite a cualquier alumno de la Universidad de Córdoba informarse sobre las calificaciones finales obtenidas en las asignaturas en las que está matriculado. El sistema permite consultar las notas de las actas utilizando Internet y Telefonía Móvil. Los profesores que estén dados de alta en el sistema podrán subir las notas sin necesidad de utilizar un tercer "software", directamente con el fichero de las actas generado por el programa UcoActas. Una vez subidas las notas, todos los alumnos matriculados que estén suscritos a este servicio, recibirán un e-mail con el aviso de que la consulta en la Web está disponible. Si el alumno lo desea, también podrá consultar sus notas publicadas utilizando un programa para dispositivos móviles que hará una petición segura al sistema. Actualmente se están realizando pruebas de actualización con estudiantes voluntarios de varias asignaturas

Herramienta Autor para la Gestión de Tests Informatizados dentro del Sistema AHA!

En este artículo presentamos Test Editor, una herramienta autor para la construcción de test informatizados, tanto clásicos como adaptativos, a través del Web. Esta herramienta facilita el desarrollo y mantenimiento de diferentes tipos de test de tipo multi-opción o multi-respuesta, con el objetivo de poder utilizarlos dentro de sistemas educativos basados en web. Test editor es una herramienta modular que permite configurar varios parámetros sobre las preguntas o ítems y los tests. También proporciona información estadística sobre la utilización de los tests, que puede ser utilizada para el mantenimiento de los tests. Esta herramienta se ha integrado dentro del sistema AHA!, pero se puede utilizar también dentro de otros sistemas basados en web. Para probar el funcionamiento de la herramienta se ha utilizado en la creación de un test adaptativo para evaluar a dos grupos de alumnos de un mismo curso de extensión universitaria sobre programación en el lenguaje Java.In this paper we describe Test Editor, an authoring tool for building adaptive and classic webbased tests. This tool facilitates the development and maintenance of different types of multiple-choice tests for use in web-based education systems. Test Editor is a modular tool, which lets you configure several parameters about questions or items and tests. It also provides statistical information about tests usage that can be used in tests maintenance. We have integrated the Test Editor with the AHA! system, but it can be used in other web-based systems as well. In order to test the performance of the tool, we have used it to create an adaptive test to evaluate two groups of students of the same university extension course about Java language

Herramienta autor Indesahc para la creación de cursos hipermedia adaptativos

Un elemento fundamental dentro de una plataforma de gestión del aprendizaje (Learning Management Systems, LMS) es la herramienta autor para la producción del material didáctico. En este artículo presentamos un sistema de desarrollo integrado para la creación y evaluación de cursos hipermedia adaptativos accesibles, basados en páginas Web. INDESAHC (Integrated Development System for Adaptive Hypermedia Courses), facilita la introducción del mapa de contenidos del curso, según un modelo del dominio basado en temas, lecciones, conceptos y escenarios tipos, en los cuales se realiza la integración de los archivos de media mediante un entorno visual intuitivo basado en plantillas. Una vez definidas las relaciones entre los temas y los niveles de dificultad de cada lección, se genera el curso hipermedia adaptativo, cuyo diseño final puede ser evaluado a través de una herramienta accesorio llamada EPRules (Educational Prediction Rules). Esta herramienta utiliza algoritmos de minería de datos, para el descubrimiento de información útil para facilitar un proceso de retroalimentación. Se describe además, el modelo didáctico en que se basa INDESAHC y se presenta una metodología eficaz, para evitar los problemas de desorientación y sobrecarga de contenidos en la navegación.In this paper we present an integrated development system for Web Based Adaptive Hypermedia Courses. We have developed an authoring tool called INDESAHC (Integrated Development System for Adaptive Hypermedia Courses) for this purpose. This tool facilitates to the course designer introducing the conceptual map, according to a domain model based on topics, lessons, concepts and learning components. Furthermore, the program allows the integration of media files in the course by means of an intuitive visual environment based on templates. Once defined the relationship among the topics and the difficulty level of each lesson, the user can generate the hypermedia adaptive course. The final design could be evaluated with an accessory tool called EPRules (Educational Prediction Rules). This tool is based on data mining algorithms in order to discover useful information for feedback. We also discuss on how our methodology can avoid the problems of disorientation and cognitive overload