By Urszula Markowska-Kaczmar, Halina Kwasnicka (auth.), Fatos Xhafa, Santi Caballé, Ajith Abraham, Thanasis Daradoumis, Angel Alejandro Juan Perez (eds.)
E-Learning has develop into some of the most vast unfold methods of distance educating and studying. applied sciences resembling internet, Grid, and cellular and instant networks are pushing educating and studying groups to discover new and clever methods of utilizing those applied sciences to reinforce instructing and studying actions. certainly, those new applied sciences can play a massive position in expanding the help to lecturers and freshmen, to shorten the time to studying and instructing; but, it will be significant to exploit clever strategies to exploit those new applied sciences to accomplish the specified aid to academics and beginners and increase beginners’ functionality in allotted studying environments.
The chapters of this quantity carry advances in utilizing clever recommendations for know-how stronger studying in addition to improvement of e-Learning purposes in accordance with such options and supported via expertise. Such clever innovations comprise clustering and type for personalisation of studying, clever context-aware ideas, adaptive studying, facts mining options and ontologies in e-Learning structures, between others.
Academics, scientists, software program builders, lecturers and tutors and scholars drawn to e-Learning will locate this booklet priceless for his or her educational, examine and perform activity.
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Extra info for Computational Intelligence for Technology Enhanced Learning
In: International Conference Current Developments in Technology-Assisted Education, pp. : Evolutionary algorithms for subgroup discovery in e-learning, A practical application using Moodle data. : Educational data mining: A survey from 1995 to 2005. : Personalized Links Recommendation Based on Data Mining in Adaptive Educational Hypermedia Systems. , Wolpers, M. ) EC-TEL 2007. LNCS, vol. 4753, pp. 292– 306. : Adaptation in current e-learning systems. : Experiencing coral: design and implementation of distant cooperative learning.
Dynamic context (personalization) relates to a use-case in which the user is seen as passive, or at least somewhat less in control. In such a use-case the system monitors, analyses, and reacts dynamically to a users behavior and situated-role. The two types of context are reflected in the two principal ways context is used, these are: (1) as a retrieval indicator (a static context) and (2) to tailor system behavior to match users system usage patterns (a dynamic context). The characteristics of context identified when taken with the diverse range of mobile devices and service infrastructure clearly identify the inherent complexity of context.
Building a Recommender Agent for e-Learning Systems, Computers in Education, 2002. In: Proceedings of International Conference on Computers in Education, vol. 1, pp. : Course ontology-based user’s knowledge requirement acquisition from behaviors within e-learning systems. : Improvement of Matrix-based Clustering Method for Grouping Learners in E-Learning. In: Proceedings of the 11th International Conference on Computer Supported Cooperative Work in Design (2007) Fuzzy ECA Rules for Pervasive Decision-Centric Personalised Mobile Learning Philip Moore, Mike Jackson, and Bin Hu Department of Computing, Birmingham City University, Birmingham, UK Abstract.