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Advances in E-learning-Experiences and Methodologies

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of constructive <strong>and</strong> cooperative methods <strong>and</strong> their feasibility <strong>in</strong> an e-learn<strong>in</strong>g platform. Moreover, we<br />

<strong>in</strong>troduce a framework for deal<strong>in</strong>g with language <strong>and</strong> representations to <strong>in</strong>terpret students’ behaviours<br />

<strong>and</strong> show examples of teach<strong>in</strong>g activities. F<strong>in</strong>ally some opportunities for future research are outl<strong>in</strong>ed.<br />

We hope to contribute to overcome the current separation between technology <strong>and</strong> educational research,<br />

as their jo<strong>in</strong>t use can provide matchless opportunities for deal<strong>in</strong>g with most of the learn<strong>in</strong>g problems<br />

related to mathematical concepts as well as to l<strong>in</strong>guistic, metacognitive, <strong>and</strong> noncognitive factors.<br />

Chapter IX<br />

AI Techniques for Monitor<strong>in</strong>g Student Learn<strong>in</strong>g Process .................................................................. 149<br />

David Camacho, Universidad Autonoma de Madrid, Spa<strong>in</strong><br />

Álvaro Ortigosa, Universidad Autonoma de Madrid, Spa<strong>in</strong><br />

Estrella Pulido, Universidad Autonoma de Madrid, Spa<strong>in</strong><br />

María D. R-Moreno, Universidad de Alcalá, Spa<strong>in</strong><br />

The evolution of new <strong>in</strong>formation technologies has orig<strong>in</strong>ated new possibilities to develop pedagogical<br />

methodologies that provide the necessary knowledge <strong>and</strong> skills <strong>in</strong> the higher education environment.<br />

These technologies are built around the use of Internet <strong>and</strong> other new technologies, such as virtual<br />

education, distance learn<strong>in</strong>g, <strong>and</strong> long-life learn<strong>in</strong>g. This chapter focuses on several traditional artificial<br />

<strong>in</strong>telligence (AI) techniques, such as automated plann<strong>in</strong>g <strong>and</strong> schedul<strong>in</strong>g, <strong>and</strong> how they can be applied<br />

to pedagogical <strong>and</strong> educational environments. The chapter describes both the ma<strong>in</strong> issues related to<br />

AI techniques <strong>and</strong> e-learn<strong>in</strong>g technologies, <strong>and</strong> how long-life learn<strong>in</strong>g processes <strong>and</strong> problems can be<br />

represented <strong>and</strong> managed by us<strong>in</strong>g an AI-based approach.<br />

Chapter X<br />

Knowledge Discovery from E-Learn<strong>in</strong>g Activities............................................................................. 173<br />

Addisson Salazar, Universidad Politécnica de Valencia, Spa<strong>in</strong><br />

Luis Vergara, Universidad Politécnica de Valencia, Spa<strong>in</strong><br />

This chapter presents a study applied to the analysis of the utilization of learn<strong>in</strong>g Web-based resources<br />

<strong>in</strong> a virtual campus. A huge amount of historical Web log data from e-learn<strong>in</strong>g activities, such as e-mail<br />

exchange, content consult<strong>in</strong>g, forum participation, <strong>and</strong> chats is processed us<strong>in</strong>g a knowledge discovery<br />

approach. Data m<strong>in</strong><strong>in</strong>g techniques as cluster<strong>in</strong>g, decision rules, <strong>in</strong>dependent component analysis, <strong>and</strong><br />

neural networks, are used to search for structures or patterns <strong>in</strong> the data. The results show the detection of<br />

learn<strong>in</strong>g styles of the students based on a known educational framework, <strong>and</strong> useful knowledge of global<br />

<strong>and</strong> specific content on academic performance success <strong>and</strong> failure. From the discovered knowledge, a<br />

set of prelim<strong>in</strong>ary academic management strategies to improve the e-learn<strong>in</strong>g system is outl<strong>in</strong>ed.<br />

Chapter XI<br />

Swarm-Based Techniques <strong>in</strong> E-Learn<strong>in</strong>g: <strong>Methodologies</strong> <strong>and</strong> <strong>Experiences</strong>....................................... 199<br />

Sergio Gutiérrez, University Carlos III of Madrid, Spa<strong>in</strong><br />

Abelardo Pardo, University Carlos III of Madrid, Spa<strong>in</strong><br />

This chapter provides an overview of the use of swarm-<strong>in</strong>telligence techniques <strong>in</strong> the field of e-learn<strong>in</strong>g.<br />

Swarm <strong>in</strong>telligence is an artificial <strong>in</strong>telligence technique <strong>in</strong>spired by the behavior of social <strong>in</strong>sects. Tak<strong>in</strong>g

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