No abstract

A student's ability to grasp new concepts or knowledge from reading material is crucial and dependent on their personalization. Personalization can be identified using learning style. In e-learning, identifying learning style, and matching reading material based on learning style, is critical for students; as it may affect their learning progress and their rate of absorbing information. Therefore, it is crucial for students to be able to locate reading material that best matches their particular learning style. The objective of this paper is to develop a tool that can help retrieve reading material based on personalization. By using a collaborative filtering method, this tool will be able to help students locate reading material that best matches their learning style in e-learning. The architecture and components of the tool are discussed.

Learning can be enhanced when its learning process aligned with learner learning styles. Individual has different learning preferences. The consideration of learner preferences in finding document can enable a learner to obtain knowledge easily and fast where it constantly changes and shift according to learning situation and environment. Thus, the incorporation of Learning Style (LS) with adaptive learning environment is one of a method to assist novice learner in finding a suitable document and enhance the e-learning system; while it can overcome the one-size-fitsfor-all problem. The LS identification is an important component to identify the learner learning style. There are two conventional techniques to identify learners learning style, which are questionnaires and computer-based detection technique. The questionnaire involves the learner to fill up a survey explicitly. However, existing of LS identification techniques facing some major drawbacks can lead the LS tool to become static nature. The questionnaire technique d facing issues such as questionnaire too long and took long time that can make learner lack of motivation to answer the question. Moreover, the result from the questionnaire technique is fixed because it only prompts a learner to do it only one at the time. Therefore, a few researchers employed computer-based detection techniques, which use a numerous computer algorithm to determined LS. However, these techniques suffer on cold start problem, required a large amount of training data and the existing computer-based detection were not focused document as learning material. Hence, there is a need to develop an adaptive learning style identification that uses document as a learning material. This paper presents the architecture for ALSDoc, an adaptive LS identification for document retrieval. In particular, this study explained how LS is adapt to the learner that allows the retrieval the suitable document that match their LS.