Development and Evaluation of a Game-Based Bayesian Intelligent Tutoring System for Teaching Programming

Abstract

Games with educational purposes usually follow a computer-assisted instruction concept that is predefined and rigid, offering no adaptability to each student. To overcome such problem, some ideas from Intelligent Tutoring Systems have been used in educational games such as teaching introductory programming. The objective of this study was to advance Online Game-based Bayesian Intelligent Tutoring System (OGITS) to enhance programming acquisition and online information searching skills, thus improving students’ ability in web-based problem solving through board games. The study sample comprised 79 college students in introductory level Computer Science classes. Qualitative and quantitative data were then gathered. Results of this study revealed generally favorable opinions about OGITS. As OGITS targets individual knowledge acquisition of computer programming and web-based problem-solving skills, it offers a suitable learning environment for students both as a stand-alone course and as a supplement to traditional classroom settings.