AIRCC's International Journal of Computer Science and Information Technology

Abstract

Technology plays an important role in the development of students who can search for the concepts which they learn in the books on the Internet and find out more information on them. This will increase the depth of their knowledge.

According to some researches, students tend to be more active and more participative when technology is being integrated in their lesson resulting to better comprehension and good performance.

Using technology in the learning process can facilities automatic detection of the learner's learning styles which can help the learner to develop his coping strategies to compensate for his/her weaknesses, capitalize on his/her strengths, improve the quality of the learning process and make it more effective.

This research presents an automatic tool for detecting learning styles in a learning environment by analyzing the content of the learner's favorite WebPages using social bookmarking services(www.tagme1.com) and shows that how actual behavior of the learners during the learning process can be used as an effective source for detecting their learning styles based on Felder-Silverman learning style model (FSLSM).

Keywords

Full Text

Abstract

Personalized adaptive systems rely heavily on the learning style and the learner's behavior. Due to traditional teaching methods and high learner/teacher ratios, a teacher faces great obstacles in the classroom. In these methods, teachers deliver the content and learners just receive it. Moreover, teachers can't cope with the individual differences among learners. This weakness may be attributed to various reasons such as the high number of learners accommodated in each classroom and the low teaching skills of the teacher himself/herself, Therefore, identifying learning styles is a critical step in understanding how to improve the learning process.

This paper presented an automatic tool for identifying learning styles based on the Felder-Silverman learning style model in a learning environment using a social book marking website such as www.tagme1.com.

The proposed tool used the learners' behaviour while they are browsing / exploring their favorite web pages in order to gather hints about their learning styles. Then the learning styles were calculated based on the gathered indications from the learners' database.

The results showed that the proposed tool recognition accuracy was 72% when we applied it on 25 learners with low number of links per learner. Recognition accuracy increased to 86.66% when we applied it on 15 learners with high number of links per learner.

Keywords

Full Text