A student's profile defines the best way a student chooses to learn. It comprises information on student's characteristics such as background knowledge, learning style preference, goals, personality etc. The foremost challenge that the students experience in learning system is that they are unable to bring back relevant information based on their needs. One of the methods used to obtain the students' needs is to create a highly efficient student profile, which would represent the true students' needs. Our contribution consists in modeling a student's profile based on many important characteristics. In this context, we developed an "SPOnto" ontology to represent a student profile in an adaptive gamified learning system. This ontology will then help the classification of the different types of learners using the machine learning techniques. The present paper epitomizes the implementation of our contribution in a gamified learning environment "Class Quiz" and the assessment of its effectiveness. We created classification models by applying several algorithms and using a dataset encompassing 500 instances.