Assessing Python Programming Through Personalised Learning Styles Model

Learning styles, cognitive traits, personality, and learning preferences can vary greatly. That is why there is a great variety in how people receive and process information. Personalizing learning materials according to learner’s learning styles could enhance learner’s learning motivation and lead to better learning performance. This paper examines the relationship between learner’s learning styles and learning performance by proposing three different sets of documentation to test the relationship between the two learning styles of Felder-Silverman and learning performance. To test the proposed documentations and hypotheses, 182 participants in Multimedia University, Cyberjaya, Malaysia answered the Index of Learning Styles (ILS) questionnaire by Felder-Silverman and participated in a documentation experiment in Python programming. The data gathered was analysed using statistical Chi-square test. The results showed that learning performance was enhanced when the documentation was provided in a learning style that matched the subject’s learning style. The confirmed personalised learning styles model can be beneficial to teachers and e-learning recommendation systems when they provide students with materials that are personalised.