The objectives of using new computing techniques in e-learning systems are defined by the necessity of teaching according to the individual needs of the students, to whom education should provide different e-contents, pedagogical paths and interaction manners.
As nowadays e-learning systems are too rigid, there have been numerous attempts to develop really adaptable e-learning systems (Paramythis & Loidl-Reisinger, 2004) (Brusilovsky & Nijhavan, 2002). This implies hard work and teams formed of specialists from diverse fields: instructional, computer science, teaching area experts and important financial and time resources. The main factor that affects the functionality of e-learning systems is the human being, an instable factor, and, therefore the instructional objectives depend on an instable and unpredictable factor.
As a result, an e-learning system has to be prepared to deal with any learning situation.
Building an e-learning system efficient for any learning context is possible only on the condition of using new computing techniques. In the field of online instruction, the part called machine’s intelligence has a primordial role. The machine’s intelligence derived from sophisticated software programme having the following features: adaptability, flexibility, reactivity, autonomy, collaboration and reasoning capacity.
Intelligent Tutoring System and Adaptive Hypermedia System are the main types of e-learning systems that provide instruction according to the parameters of the instructional process. These parameters characterize the actors of the instructional process and the environment and are included in the components of an e-learning system: expert model, learner model, instructional model and interface model (Phobun & Vicheanpanya, 2010) An adaptive system is a system that changes its behaviour according to the environment’s changes in order to reach certain goals. An intelligent e-learning system is an adaptive, complex system. The complexity of the system is due to the different interactions between its component parts and to the nature of these interactions: human-machine, human-human via machine and machine-machine. To confer the system self-learning capacity, the usage of artificial intelligence is imperative.
In (Ruiz et al., 2008), the authors present two types of e-learning systems which adapt taking into account the learning styles of the learners:
“1. Systems that use learning styles to guide the design of the educational contents. These systems are based on offering the users the type of materials that are preferred by individuals classified in their specific learning style.
2. Systems that use learning styles to guide the adaptation of the structure of the contents to the mental processes of each individual (particular styles of thinking, perceiving or remembering) that falls in a certain category.”
The adaptation property of the e-learning systems presented in (Ruiz et al., 2008) is reached using a set of rules or using objects with a certain format adequate for each instructional situation.
Learner’s classification, considering his/her individual learning style is used in the software system for online learning proposed in (Moise, 2007), called iLearning. The system is based on shaping a course by means of conceptual maps. Each node of the conceptual maps contains pedagogical resources in different formats and structures according to the learning styles of each student. The implementation of an iLearning sy stem on a computer is realised using intelligent agents technology.
The system described in (Tzouveli et al., 2008) offers a flexible solution capable to adapt to learners’ preferences using e-questionnaires to establish automatically learners’ profile. The problems of adaptation and flexibility of the e-learning systems are difficult, as the main actors are complex and unpredictable. In order to solve such problems, the researchers ask for new computing techniques.
Bio-computing techniques simulate biological mechanisms and are used in solving the most difficult problems. The purpose of this chapter is to present e-learning systems architectures based on new computing techniques, namely neural networks and swarm intelligence techniques.
Artificial neural networks techniques are inspired by the activity of the human brain and swarm intelligence techniques are inspired by insects’ behaviour. The authors selected these artificial life techniques in order to solve the problems related to collective and individual intelligences, collective and individual behaviours, collective and individual knowledge. The expected results are: a higher level of students participation in the instructional process, a better internalisation of rules and contents, more numerous and diverse types of interaction, a higher level of motivation and self-esteem. The expected results respond to cognitive, psychological and instructional demands.
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