Learning style analysis based on Felder-Silverman Index Model using Rule Base algorithm

Understanding everyone’s unique learning style is key to achieving lasting academic success. The importance of this understanding becomes increasingly important, especially when students are not aware of effective learning strategies, which can inadvertently become an obstacle to their progress. To overcome this concern, the Learning Style Analysis System (SAGP) was developed to identify and provide recommendations based on individual learning styles. The main users of this system are students enrolled and the academic advisors in the faculty of defense science and technology. They were required to fill out a questionnaire on their learning style. This system can determine the student's learning style and provide recommendations tailored to their needs. This process benefits students and strengthens the relationship between educators and students, ensuring a more individual or personal approach to learning. The system is developed using the PHP and CSS programming languages, with XAMPP and MySQL serving as the database that stores the necessary information. The Rule Base algorithm is used to classify students based on their learning style characteristics. The Agile approach is used as a development methodology, emphasizing adaptability and continuous delivery. Felder-Silverman Index learning method will be used as a learning style method in this system. The method will contain 44 questions based on four different domains of learning styles. Overall, the Learning Style Analysis System implementation is expected to positively contribute to the quality of teaching and learning at the university by aligning teaching approaches to individual learning styles. This will hopefully result in improved academic performance and increased student engagement in the learning process. Initially, a survey was conducted to assess the need for such a system to be in place. The survey had the majority saying that there is a need for such a system in UPNM.