Enhancing Object-Oriented Programming Pedagogy with an Adaptive Intelligent Tutoring System

Challenges to teaching programming include a lack of structured teaching methodologies that are tailored for programming subjects while the benefits of providing programming students with individual attention are not easily addressed due to high student-to-teacher ratios. This paper describes how adaptive intelligent tutoring systems may represent a potential solution assisting teachers in delivering individualized attention to their students while also helping them to discover effective ways of teaching a core programming concept such as object-oriented programming. This paper investigates how adaptability in traditional intelligent tutoring systems are achieved, presenting an adaptive pedagogical model that uses machine learning techniques to discover effective teaching strategies suitable for a particular student. The results of a prototype of the proposed model demonstrate the model's ability to classify the student models according to their learning style correctly. The knowledge obtained can be applied by educators to make better-informed choices in the formulation of lesson plans that are more appropriate to their students.