Behavior Analysis in a learning Environment to Identify the Suitable Learning Style

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.