Adaptive E Learning System To Improve Learning Education Essay

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Electronic learning or e-learning is a general term used to refer to computer-enhanced learning. In many respects, it is commonly associated with the field of advanced learning technology (ALT), which deals with both the technologies and associated methodologies in learning using networked and/or multimedia technologies (Floriana & Giovanni, 2004). E-learning environment give users a high degree of freedom in following a preferred educational path, together with a control to explore effective paths. This freedom and control is beneficial for the students, resulting in a deeper understanding of the instructional material. Nevertheless, such facility can also be problematic since some students are not able to explore effectively. One way to address this problem is to argument the environments with personalized support (Floriana & Giovanni, 2004; Valldeperas, 2000). A number of problems in using course management systems (CMS) have been reported such as students may feel isolated due to lack of contact with the lecturer or with other student; students find it difficult to manage without institutional support. Learners perceive and process information in very different ways. These are represented by their learning styles. Therefore, the challenging part of an educator's role is to adopt their teaching or pedagogical style to suit the variety of students in a classroom environment. A designer of an e-learning course also has to make such provisions when developing a course. Today it is widely accepted that during the design and development of educational material attention must be focused on the learner's learning style. The most critical issue is that the learner is more independent in e-learning system, whereas in traditional classroom a teacher can monitor and react accordingly based on student's response. Therefore, e-learning system needs to be adapted to the learner's responses and presents information based on their learning style. However, learners sometimes do not know how to learn and what approach suits them most. There exists a need, therefore, to move away from the one size fit all paradigm and to develop methods whereby personalized courses are presented to learners which cater for these varying learning styles. By recognizing and understanding the individuals to their learning styles, the e-learning techniques can be used better and improve the speed and quality of learning (Dave, 1999; Unal, 2007; Suo, 2007).

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Many researches are now working on improving the learning processes. This is possibly done through an adaptive system. The purpose of this system is to encourage students to enroll into e-learning, gain knowledge through the latest educational techniques, and assist them by providing a convenient learning environment. There are many attempts to improve the adaptive system. They include the use of different methods and artificial intelligence techniques for user modeling to cope with various learning style. However, such system lacks the ability in building student personality by motivation, increasing self-confidence, or reducing shyness. Therefore, most of the researches focus on the student modeling and how the system can automatically deal with different students.

In traditional classroom system, a teacher can monitor and react accordingly based on students' response. However, an e-learning environment requires students to be more independent. As such the system should be able to adapt to the preferred learning style of each student.

Most, if not all, instructional design strategies accommodate different learning styles. These include multimedia, course syllabus, and copies of the lecture slides. However, none of the available learning style models adequately cover all e-learning aspects such as learning approaches and learning preferences. The most critical element for a student's success in school is an understanding of how to learn. The key ingredients for this understanding are confidence, curiosity, intentionality, self-control, relatedness, capacity to communicate, and ability to cooperate. These traits are aspects of emotional intelligence (Marcia , 2005). Furthermore, in the classroom it would not be feasible for the teacher to attempt to teach exclusively to match each student's unique learning style.  However, with an understanding of the different styles of learning, the teacher can plan the environment, lessons/activities and materials to better create a balanced setting to enhance the success of each student. All of these are present in the traditional learning system but not in e-learning (Barroso et, al. 2006). When the learning style of the student is not compatible with the teaching style of the teacher difficulties in learning arise. Therefore, knowing the learning style and studying in favorite study environment supported by emotional intelligence can increase the success of learning and teaching process in e-learning. This research investigates how personalized courses can be delivered to the learner in an adaptive environment. It also develops a model together with neural network to accommodate learners based on individually preferred environment.

1.2 Aims and Objectives

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The primary goal of this thesis is to investigate how an individual learner can be accommodated based on his preferred learning style through adaptive e-learning system to improve learning and teaching process. More specially, it is to develop a learning style model together with neural network technology to suit e-learning requirements. In order to accomplish this primary goal several objectives are outlined:

To investigate the usability of available learning style models for e-learning environment.

To propose a learning style model for e-learning environment.

To identify a method to implement the proposed model for adaptive e-learning system.

1.3 Research questions

This thesis attempt to answer the following research questions:

Are the available learning style models adequate and capable to cover all e-learning aspects such emotional and individual preferred learning environment?

How can we extract a learner's characteristics and preferences for an e-learning environment? Can neural network do that?

How does the e-learning environment can handle various of learning styles and preferences based on developed model?

1.4 Research Scope

This research focuses on how to improve an e-learning environment to suit individual learning styles. C programming language course has been used as a sample course for developing and implement this thesis and SCORM standard is used to develop the learning course content and the learning environment. The research looks at various models as a foundation for the proposed model. Neural network is the technology chosen to adapt individuals based on their learning style preference. Although there methods are possible iterative, they are not produced in this research.

1.5 Thesis Overview

To develop a successful e-learning program it is important to build the courses based on sound pedagogical principles. A starting point in this research is to examine the area of learning and current popular learning styles models. Problems in e-learning and the latest related research are also investigated and documented in chapter two of this thesis.

The aim of this research is to accommodate individual learner's based on their preference to improve e-learning adaptive system. Thus, the capability of the available learning style models to identify the e-learner preference is the first thing examined and presented in chapter three.

Particular interest is paid to answering questions such as: What are the available learning style models that can be used in an e-learning environment? What are the characteristics to be used in analyzing them? Thus and in order to build the e-learning style model three questions need to be answered: What are the learners' preferences in e-learning? How can the model extract the learner's characteristics? What is the method to be used for implementing the e-learning model and How? The above are discussed in chapter four.