ABSTRACT Experimenting with a Real-Size Man-Hill to Optimize Pedagogical Paths

This paper describes experiments aimed at adapting Ant Colony Optimization (ACO) techniques to an e-learning environment, thanks to the fact that the available on-line material can be organized in a graph by means of hyperlinks between educational topics. The structure of this graph is to be optimized in order to facilitate the learning process for students. ACO is based on an ant-hill metaphor. In this case, however, the agents that move on the graph are students who unconsciously leave pheromones in the environment depending on their success or failure. In the paper, the whole process is therefore referred to as a “man-hill.” Compared to the [13, 14] papers that were providing guidelines for this problem, real-size tests have been performed, showing that man-hills behave differently from ant-hills. The notion of pheromone erosion (rather than evaporation) is introduced. 1

Paving the Way for Lifelong Learning:Facilitating competence development through a learning path specification

Janssen, J. (2010). Paving the Way for Lifelong Learning. Facilitating competence development through a learning path specification. September, 17, 2010, Heerlen, The Netherlands: Open University of the Netherlands, CELSTEC. SIKS Dissertation Series No. 2010-36. ISBN 978-90-79447-43-5Efficient and effective lifelong learning requires that learners can make well informed decisions regarding the selection of a learning path, i.e. a set of learning actions that help attain particular learning goals. In recent decades a strong emphasis on lifelong learning has led educational provision to expand and to become more varied and flexible. Besides, the role of informal learning has become increasingly acknowledged. In light of these developments this thesis addresses the question: How to support learners in finding their way through all available options and selecting a learning path that best fit their needs? The thesis describes two different approaches regarding the provision of way finding support, which can be considered complementary. The first, inductive approach proposes to provide recommendations based on indirect social interaction: analysing the paths followed by other learners and feeding this information back as advice to learners facing navigational decisions. The second, prescriptive approach proposes to use a learning path specification to describe both the contents and the structure of any learning path in a formal and uniform way. This facilitates comparison and selection of learning paths across institutions and systems, but also enables automated provision of way finding support for a chosen learning path. Moreover, it facilitates automated personalisation of a learning path, i.e. adapting the learning path to the needs of a particular learner. Following the first approach a recommender system was developed and tested in an experimental setting. Results showed use of the system significantly enhanced effectiveness of learning. In line with the second approach a learning path specification was developed and validated in three successive evaluations. Firstly, an investigation of lifelong learners’ information needs. Secondly, an evaluation of the specification through a reference (sample) implementation: a tool to describe learning paths according to the specification. Finally, an evaluation of the use and purpose of this tool involving prospective end-users: study advisors and learning designers. Following the various evaluations the Learning Path Specification underwent some changes over time. Results described in this thesis show that the proposed approach of the Learning Path Specification and the reference implementation were well received by end-users.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org

Cálculo de rutas en Sistemas de E-Learning utilizando un algoritmo de optimización por colonias de hormigas

Esta tesis plantea un escenario en el que existen itinerarios de aprendizaje alternativos para adquirir las mismas competencias y habilidades, cuyo nivel de asimilación se evalúa al finalizar el programa completo, compuesto por una secuencia de cursos concreta. El objetivo de este trabajo es estudiar la aplicación de la metaheurística de optimización mediante colonias de hormigas al problema de adaptar el itinerario a seguir por un alumno, en función de las calificaciones obtenidas en cada curso. Para llevar a cabo este estudio se modifica el algoritmo Ant System con el fin de adaptarlo al escenario propuesto (algoritmo ASALI) y se estudian, mediante simulación por ordenador, los resultados obtenidos y la velocidad de adaptación a los cambios, verificando su utilidad en el problema propuesto. Como estudio previo se ofrece el estado del arte actual en algoritmos de optimización por colonias de hormigas, con una breve descripción de los algoritmos más relevantes tanto para un único objetivo como para múltiples objetivos