Intelligent Learning Model Based On Significant Weight Of Domain Knowledge Concept For Adaptive E-Learning

Abstract

In order to support personalized learning, an adaptive learning system should have a capability to provide each student with a suitable learning material regarding his profile. However, the issue of student varieties in acquiring every Domain Knowledge Concept (DKC), and a range of DKC important variations in a particular learning material produced a complex dependency that causes a difficulty in the learning material selection process. Existing rule-based learning material selection approach requires the definition of a huge set adaptation rules. However, this approach usually results in inaccurate and incorrect selection due to the inconsistent, insufficient and confluence of the defined rules. Consequently, the process of learning material selection is hard to be algorithmized, therefore, intelligent methods are applied to handle the complexity challenges. This research proposes a significance weight approach that represents the complex dependency of learning material selection problem to substitute the rules definition in the selection process. In addition, this research proposes an intelligent learning model that combines unsupervised and supervised machine learning techniques to accurately select the learning material for a particular student adaptively. The unsupervised machine learning technique is vital in obtaining a learning material classification and labelling based on the proposed significance weight. Meanwhile, the supervised machine learning technique, the Multilayer Perceptron Artificial Neural Networks conducts the adaptation process that will assign the student to suitable learning materials regarding his performance upon specific DKC. With 98% achievement of classification accuracies, this model can be considered as highly accurate in selecting a correct and suitable learning material based on student's domain knowledge level.

E-Module Based Problem Solving in Basic Physics Practicum for Science Process Skills

“…Teaching materials developed using information and communication technology products can stimulate students' thoughts, feelings, concerns and interests in a process that is more effective [18]. Learning uses technology through teaching materials so that students can manage learning activities according to their flexible time and activities [19].…”

E-Assessment Motivation in Physics Subjects for Senior High School

“…Four domains of the 21st century are literacy digital era, High Order Thingking Skills, collaboration social skills and productivity in the production [1]. The progress of information and communication technology has transformed the teaching and learning process towards a portable, student-centered and multi-platform environment [2]. Changes and technological advancements in various fields, for example in communication and information technology today [3].…”

Understanding the role of individual learner in adaptive and personalized e-learning system

“…For instance, Ouf et al [20] defined three values of high, fair, and low, as the difficulty parameter. Idris et al [15] proposed a three-step difficulty level, which consists of Beginner, Intermediate, and Advanced. Another static parameter as suggested by [22] defined difficulty level by using the scale from 0 (easy) to 1 (difficult).…”

“…A system can adapt the level of students and decide what the next stage of the learning process might be by accessing the knowledge of the learners. This way of adaptation is promoted by [15], [35], [36]. Knowledge level can also be reflected using performance level indicators, automatic system score, or question items level, such as well learned, learned, poor, and very poor [22].…”