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Advances in E-learning-Experiences and Methodologies

Swarm-Based Techniques

Swarm-Based Techniques in E-Learning CoFIND. Big populations mean that the system will be helpful for more people, but a smaller group is more likely to make the system evolve faster. This is coherent with results from natural sciences which show that evolution tends to occur more rapidly in isolated populations (Gould, 1978). This leads to a useful mechanism for reducing the bloated excess of results that could be obtained from a typical search engine or directory. If a small group with a common learning interest compiles a set of resources (possibly found using search engines) there is a high probability that they are winning a higher relevance from the rest of the resources (found by their colleagues). The evolutionary model of CoFIND creates a context-dependent taxonomy which captures the usage of the group’s tacitly negotiated and agreed evaluations. Ambiguities and disagreements are not discussed but solved in a sort of indirect democratic process as users vote and rate the resources. concLusIon The pervasive presence of the Internet allows for greater communication between different people than ever before, independently of their physical location. Although this situation brings its own risks, it opens the doors for many ways of collaboration between different members of a community. Taking some inspiration from the biology of social insects, several swarm-intelligence applications have appeared over the last years. Combining some of their know-how with some sociology concepts produces the appearance of social-swarm applications. This chapter has presented those that are focused on e-learning, showing both its strong points as their weaknesses. Future reseArch dIrectIons The Internet is still far from its peak of development, and the effects of “social e-networks” is far from being fully comprehended. In the near future, many more applications built on the foundations of those presented here will develop. Social navigation, very related to social sequencing, is another emerging trend. Social navigation (Marten, Farzan, & Brusilovky, 2006) aims at using navigation information from the users to provide them with clues about where to move next. This clues might give information about the current position of users (e.g., which documents is the user accessing now) or about the path they have followed. These clues can relate to groups of users as well. For example, documents that are more frequently accessed can be highlighted in some form. As the users interact increasingly more between themselves (either consciously or not) during their learning process, their awareness about their environment becomes more important. Learning is known to be a social process that benefits greatly from the interaction with others, yet information technologies have traditionally limited learning to an impersonal paradigm. As technology is providing means for real communication, it becomes important to be aware of the environment with which the student interacts (who is doing what; when, where, etc.). Possible sources of information that can be relevant to the student are virtual location and near peers, peers activities and opinions, timing, and age of activities and actors, and so forth. Enhancing the awareness of the students about all these issues can play an important role in their learning. Besides, being aware of your peers allows you to interact with them directly. Finding the adequate equilibrium between direct and indirect communication (like in swarms) for promoting learning will be one the challenges in the future. 0

Swarm-Based Techniques in E-Learning reFerences Boley, H. (2003). RACOFI: A rule-applying collaborative filtering system. In 2003 IEEE/WIC International Conference on Web Intelligence/ Intelligent Agent Technology. Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm intelligence: From natural to artificial Systems. NY: Oxford University Press Costa, D., Hertz, A., & Dubious, O. (1995). Embedding of a sequential algorithm within an evolutionary algorithm for coloring problems in graphs. Journal of Heuristics, 1, 105-128. Deshpande, M., & Karypis, G. (2004). Selective Markov models for predicting Web page accesses. ACM Transactions on Internet Technology (TOIT), 4(2), 163-184. Dorigo, M., & Stützle, T. (2004). Ant colony optimization. MIT Press. Dron, J. (2002). Achieving self-organisation in network-based learning environments. PhD doctoral dissertation. Dron, J., Mitchell, R., Siviter, P., & Boyne, C (1999). CoFIND: Experiment in n-dimensional collaborative filtering. In World Conference on the WWW and Internet (pp. 301-306). Gambardella, L.M, Taillard E., & Agazzi G. (1999). MACS-VRPTW: A multiple ant colony system for vehicle routing problems with time windows. New Ideas in Optimization, 63-76. Gould, S. J. (1978). Ever since Darwin - reflections in natural history. Burnett. Gutiérrez, S., Pardo, A., & Delgado Kloos, C. (2006a). A modular architecture for intelligent Web resource based tutoring systems. Intelligent Tutoring Systems, 753-755. Gutiérrez, S., Pardo, A., & Delgado Kloos, C. (2006b). Some ideas for the collaborative search of the optimal learning path. In Adaptive Hypermedia 2006 (pp. 430-434). Gutiérrez, S., Valigiani, G., Jamont, Y., Collet, P., & Delgado Kloos, C. (2007). A swarm appoach for automatic auditing of pedagogical planning. In Proceedings of IEEE ICALT 2007 (pp. 136-138). Hartley, J., & Sleeman, D. (1973). Towards more intelligent teaching systems. International Journal of Man-Machine Studies, 2, 215-336. Hofmann, T. (2003). Collaborative filtering via Gaussian probabilistic latent semantic analysis. In 26 th ACM SIGIR Conference on Research in Information Retrieval (pp. 259-266). Kauffman, S. (1996). At home in the universe: The search for the laws of self-organization and complexity. Oxford University Press. Kennedy, R., & Eberhart, R. (2001). Swarm intelligence. CA: Morgan Kaufmann/Academic Press. Koper, R. (2005). Designing learning networks for lifelong learners. In R. Koper & C. Tattersall (Eds.), Learning design: A handbook on modelling and delivering networked education and training (pp. 239-252). Mertens, R., Farzan, R., & Brusilovsky, P. (2006). Social navigation in Web lectures. In U. K. Wiil, P. J. Nürnberg & J. Rubart (Eds.), Proceedings of Hypertext Conference 2006. Miyahara, K., & Pazzani, M. (2000). Collaborative filtering with the simple Bayesian classifier. In Pacific Rim International Conference on Artificial Intelligence (pp. 679-689). Morley, R. (1996). Painting trucks at general motors: The effectiveness of a complexity-based approach. In Ernst and Young Center for Business Innovation, (Ed.), Embracing Complexity: Exploring the Application of Complex Adaptive Systems to Business (pp. 53-58). Cambridge, MA. 0

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    Advances in E-Learning: Experiences

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    Table of Contents Preface .........

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    Chapter XIV Open Source LMS Customi

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    Chapter III Philosophical and Epist

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    contents, learning contexts, proces

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    xvii QuALIty In e-LeArnIng Before t

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    allow that the teachers in training

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    xxi ISO. (1986). Quality-Vocabulary

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    Chapter I RAPAD: A Reflective and P

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    RAPAD in fields such as law, engine

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    RAPAD mystery to the new student. B

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    RAPAD example, whereas Laurillard h

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    RAPAD Ontologically, systems philos

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    RAPAD information related processes

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    RAPAD methods and techniques accord

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    RAPAD 2. An introduction to learnin

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    RAPAD then asked to reflect on and

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    RAPAD Figure 4. A rich picture to h

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    RAPAD Again using techniques from t

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    RAPAD university preparation course

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    RAPAD The third interface is at the

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    RAPAD Knight, P.T., & Trowler, P. (

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    RAPAD AddItIonAL reAdIngs Goodyear,

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    A Heideggerian View on E-Learning (

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    Philisophical and Epistemological B

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    E-Mentoring However, what is unders

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    E-Mentoring baugh, & Williams, 2004

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    E-Mentoring Table 2. Contact. Diffe

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    E-Mentoring Table 10. Ethical impli

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    E-Mentoring Table 21. Coaching. Bes

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    E-Mentoring Table 27. Moment. Best

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    E-Mentoring Moreover, existing rese

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    E-Mentoring Kasprisin, C. A., Singl

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    E-Mentoring Ensher, E. A., Heun, C.

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    Chapter V Training Teachers for E-L

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    Training Teachers for E-Learning FL

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    The Role of Institutional Factors i

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    The Role of Institutional Factors i

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    E-Learning Value and Student Experi

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    E-Learning Value and Student Experi

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    E-Learning Value and Student Experi

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    E-Learning Value and Student Experi

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    E-Learning Value and Student Experi

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    E-Learning Value and Student Experi

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    E-Learning Value and Student Experi

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    E-Learning Value and Student Experi

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    E-Learning Value and Student Experi

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    E-Learning Value and Student Experi

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    Integrating Technology and Research

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    Integrating Technology and Research

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    Integrating Technology and Research

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    Integrating Technology and Research

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    Integrating Technology and Research

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    Integrating Technology and Research

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    Integrating Technology and Research

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    Integrating Technology and Research

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    Chapter IX AI Techniques for Monito

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    AI Techniques for Monitoring Studen

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    AI Techniques for Monitoring Studen

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    AI Techniques for Monitoring Studen

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    Chapter XVI Formative Online Assess

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    Formative Online Assessment in E-Le

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    Formative Online Assessment in E-Le

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    Formative Online Assessment in E-Le

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    Formative Online Assessment in E-Le

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    0 Chapter XVII Designing an Online

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    Designing an Online Assessment in E

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    Designing an Online Assessment in E

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    Designing an Online Assessment in E

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    Designing an Online Assessment in E

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    Designing an Online Assessment in E

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    Chapter XIX E-QUAL: A Proposal to M

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    E-QUAL is proposed to evaluate the

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    E-QUAL provide competent, service-o

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    E-QUAL 2004; Scalan, 2003) and qual

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    E-QUAL benchmarks address technolog

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    E-QUAL E-learning added two differe

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    E-QUAL Table 6. Application of the

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    E-QUAL Future trends The future of

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    E-QUAL (EQO) co-located to the 4 th

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    E-QUAL SMEs: An analysis of e-learn

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    E-QUAL Meyer, K. A. (2002). Quality

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    Compilation of References Argyris,

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    Compilation of References Biggs, J.

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    Compilation of References Cabero, J

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    Compilation of References Comezaña

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    Compilation of References Downes, S

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    Compilation of References Fandos, M

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    Compilation of References national

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    Compilation of References Hudson, B

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    Compilation of References Harbour.

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    Compilation of References Little, J

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    Compilation of References Metros, S

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    Compilation of References ONeill, K

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    Compilation of References Preece, J

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    Compilation of References Sadler, D

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    Compilation of References Shin, N.,

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    Compilation of References tional Co

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    Compilation of References Vermetten

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    Compilation of References Yu, F. Y.

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    About the Contributors Juan Pablo d

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    About the Contributors part: “An

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    About the Contributors María D. R-

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    About the Contributors Applications

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    Index e-learning tools, automated p

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    Socrates 55 Sophists 55 student-foc

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