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

Danial Hooshyar, Rodina Binti Ahmad, Minhong Wang, Moslem Yousefi, Moein Fathi, Heuiseok Lim

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)775-801
Number of pages27
JournalJournal of Educational Computing Research
Issue number6
Publication statusPublished - 2018 Oct 1


  • Bayesian network
  • computer programming
  • game-based environment
  • intelligent tutoring system
  • online information searching

ASJC Scopus subject areas

  • Education
  • Computer Science Applications


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