Advertisement

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

  • Methembe Dlamini
  • Wai Sze LeungEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 963)

Abstract

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.

Keywords

Intelligent tutoring systems Pedagogical decision-making Adaptability Artificial intelligence Machine learning 

References

  1. 1.
    Baine, D., Mwamwenda, T.: Education in southern Africa: current conditions and future directions. Int. Rev. Educ. 40(2), 113–134 (1994)CrossRefGoogle Scholar
  2. 2.
    Beck, J., Woolf, B.P., Beal, C.R.: ADVISOR: a machine learning architecture for intelligent tutor construction. In: Joint Proceedings of the 17th National Conference on Artificial Intelligence and 12th Conference on Innovative Applications of Artificial Intelligence, pp. 552–557 (2000)Google Scholar
  3. 3.
    Caputi, V., Garrido, A.: Student-oriented planning of e-learning contents for Moodle. J. Netw. Comput. Appl. 53, 115–127 (2015)CrossRefGoogle Scholar
  4. 4.
    Chrysafiadi, K., Virvou, M.: Student modeling approaches: a literature review for the last decade. Expert Syst. Appl. 40(11), 4715–4729 (2013)CrossRefGoogle Scholar
  5. 5.
    Cuevas, J.: Is learning styles-based instruction effective? a comprehensive analysis of recent research on learning styles. Theor. Res. Educ. 13(3), 308–333 (2015)CrossRefGoogle Scholar
  6. 6.
    Davis, K., Christodoulou, J., Seider, S., Gardner, H.: The theory of multiple intelligences. In: Cambridge Handbook of Intelligence, pp. 485–503 (2011)Google Scholar
  7. 7.
    Dorça, F.A., Lima, L.V., Fernandes, M.A., Lopes, C.R.: comparing strategies for modeling students learning styles through reinforcement learning in adaptive and intelligent educational systems: an experimental analysis. Expert Syst. Appl. 40(6), 2092–2101 (2013)CrossRefGoogle Scholar
  8. 8.
    Evens, M.W., et al.: CIRCSIM-Tutor: an intelligent tutoring system using natural language dialogue. In: Proceedings of 12th Midwest AI and Cognition Science Conference, pp. 16–23 (2001)Google Scholar
  9. 9.
    Felder, R.M., Silverman, L.K.: Learning and teaching styles in engineering education. Eng. Educ. 78(7), 674–681 (1988)Google Scholar
  10. 10.
    Freedman, R.: What is an intelligent tutoring system? Intelligence 11(3), 15–16 (2000)CrossRefGoogle Scholar
  11. 11.
    Ghadirli, H.M., Rastgarpour, M.: A web-based adaptive and intelligent tutor by expert systems. In: Meghanathan, N., Nagamalai, D., Chaki, N. (eds.) Advances in Computing and Information Technology. Advances in Intelligent Systems and Computing, vol. 117, pp. 87–95. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-31552-7_10CrossRefGoogle Scholar
  12. 12.
    Gomes, A., Mendes, A.J.: Learning to program – difficulties and solutions. In: ICEE 2007 Proceedings of the International Conference on Engineering Education, pp. 283–287 (2007)Google Scholar
  13. 13.
    Graesser, A.C.: Conversations with autotutor help students learn. Int. J. Artif. Intell. Educ. 26(1), 124–132 (2016)CrossRefGoogle Scholar
  14. 14.
    Gross, S., Mokbel, B., Hammer, B., Pinkwart, N.: Learning feedback in intelligent tutoring systems. Künstliche Intelligenz 29(4), 413–418 (2015)CrossRefGoogle Scholar
  15. 15.
    Kalelioğlu, F., Gülbahar, Y.: The effects of teaching programming via scratch on problem solving skills: a discussion from learners’ perspective. Inf. Educ. 13(1), 33–50 (2014)Google Scholar
  16. 16.
    Kim, J., Lee, A., Ryu, H.: Personality and its effects on learning performance: design guidelines for an adaptive e-learning system based on a user model. Int. J. Ind. Ergonomics 43(5), 450–461 (2013)CrossRefGoogle Scholar
  17. 17.
    Klement, M.: How do my students study? an analysis of students’ of educational disciplines favorite learning styles according to VARK classification. Procedia Soc. Behav. Sci. 132, 384–390 (2014)CrossRefGoogle Scholar
  18. 18.
    Knight, W.: AI’s language problem (2016). https://tinyurl.com/y7r9haju
  19. 19.
    Koorsse, M., Cilliers, C., Calitz, A.: Programming assistance tools to support the learning of IT programming in South African secondary schools. Comput. Educ. 82, 162–178 (2015)CrossRefGoogle Scholar
  20. 20.
    Kulkarni, P., Ade, R.: Prediction of student’s performance based on incremental learning. Int. J. Comput. Appl. 99(14), 10–16 (2014)Google Scholar
  21. 21.
    Latham, A.M., Crockett, K.A., McLean, D.A., Edmonds, B., O’Shea, K.: Oscar: An intelligent conversational agent tutor to estimate learning styles. In: FUZZ 2010 Proceedings of IEEE International Conference on Fuzzy Systems, pp. 1–8 (2010)Google Scholar
  22. 22.
    Lockspeiser, T.M., Kaul, P.: Using individualized learning plans to facilitate learner-centered teaching. J. Pediatr. Adolesc. Gynecol. 29(3), 214–217 (2016)CrossRefGoogle Scholar
  23. 23.
    Melis, E., Siekmann, J.: ActiveMath: an intelligent tutoring system for mathematics. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 91–101. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-24844-6_12CrossRefGoogle Scholar
  24. 24.
    Milne, I., Rowe, G.: Difficulties in learning and teaching programming – views of students and tutors. Educ. Inf. Technol. 7(1), 55–66 (2002)CrossRefGoogle Scholar
  25. 25.
    Padayachee, I.: Intelligent tutoring systems: architecture and characteristics. In: SACLA 2002 Proceedings of 32nd Annual Conference of the Southern African Computer Lecturers’ Association (2002)Google Scholar
  26. 26.
    Partovi, H.: Should Computer Science Be A Mandatory Class In U.S. High Schools? (2017). https://tinyurl.com/yddfe2n7
  27. 27.
    Pashler, H., McDaniel, M., Rohrer, D., Bjork, R.: Learning styles: concepts and evidence. Psychol. Sci. Public Interest 9(3), 106–119 (2008)CrossRefGoogle Scholar
  28. 28.
    Perone, C.S.: Machine Learning: Cosine Similarity for Vector Space Models (Part III). Technical report (2013). http://blog.christianperone.com/2013/09/
  29. 29.
    Poropat, A.E.: A meta-analysis of the five-factor model of personality and academic performance. Psychol. Bull. 135(2), 322–338 (2009)CrossRefGoogle Scholar
  30. 30.
    Saucier, G., Goldberg, L.R.: The language of personality: lexical perspectives on the five-factor model. In: The Five-Factor Model of Personality: Theoretical Perspectives, pp. 21–50 (1996)Google Scholar
  31. 31.
    Schulze, K.G., Shelby, R.N., Treacy, D.J., Wintersgill, M.C., VanLehn, K.: Andes: an active learning, intelligent tutoring system for newtonian physics. Themes Educ. 1(2), 115–136 (2000)Google Scholar
  32. 32.
    Sterling, L.: An education for the 21st century means teaching coding in schools (2015). https://tinyurl.com/ybuqoh56
  33. 33.
    Susarla, S.C., Adcock, A.B., van Eck, R.N., Moreno, K.N., Graesser, A.: Development and evaluation of a lesson authoring tool for AutoTutor. In: AIED 2003 Supplemental Proceedings, pp. 378–387, Sydney (2003)Google Scholar
  34. 34.
    Wan, S., Niu, Z.: A learner-oriented learning recommendation approach based on mixed concept mapping and immune algorithm. Knowl.-Based Syst. 103(3), 28–40 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Academy of Computer Science and Software EngineeringUniversity of JohannesburgJohannesburgSouth Africa

Personalised recommendations