Do learning style preferences influence the cumulative gross point average and self directed learning hours in dental students: a preliminary study

Abstract Background: Learning styles influence the outcome of the student performances based on preliminary data available. To evaluate whether the learning styles discriminate the cumulative gross point average (CGPA) scores and selfdirected learning hours (SDL) in an integrated curriculum of dental students. Methods: Participants in this blinded cross-sectional study were dental students enrolled in the Bachelor of Dental & Oral Surgery program at XXXX College of Dentistry. An online survey (Kolb Learning Style Inventory) was used to collect data. It has four sections: Concrete Experience (CE), Abstract Conceptualization (AC), Reflective Observation (RO), and Active Experimentation (AE). Questionnaire was distributed electronically to students of Academic level 1 to 5, selected by using non-probability quota sampling technique. In addition to learning style assessment the questionnaire also included measures to obtain data such as gender, academic level, CGPA score, and SDL hours of participants. The CGPA scores were categorized into average (3 to 3.6), good (3.7 to 4.2), excellent (4.3 to 4.7) and outstanding (> 4.7) as well as SDL in to three classes as, < 1 h, > 1 but < 3 h and > 3 h. Discriminant function test was computed to assess the effectiveness of discrimination by the learning styles in GPA and SDL. Results: The study’s questionnaire was completed by 198 participants (43% females and 57% males). Learning styles were discriminated by excellent category of CGPA scores that presented 72.1% group membership whereas in case of outstanding category presented the least as 17% group membership. Learning styles were discriminated by > 2 but < 3 h category of SDL hours that presented 69.7% group membership. Conclusion: Learning styles can be used to discriminate the student academic performances and self-directed learning hours. Among the different category of CGPA participants with outstanding performance represent a good prediction for learning styles preferences. Participants with varying SDL hours also influenced the learning styles