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

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ICT Education (SACLA 2018)

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.

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Notes

  1. 1.

    The software is available for researchers upon e-mail request: wsleung@uj.ac.za.

References

  1. Baine, D., Mwamwenda, T.: Education in southern Africa: current conditions and future directions. Int. Rev. Educ. 40(2), 113–134 (1994)

    Article  Google Scholar 

  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. Caputi, V., Garrido, A.: Student-oriented planning of e-learning contents for Moodle. J. Netw. Comput. Appl. 53, 115–127 (2015)

    Article  Google Scholar 

  4. Chrysafiadi, K., Virvou, M.: Student modeling approaches: a literature review for the last decade. Expert Syst. Appl. 40(11), 4715–4729 (2013)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. Felder, R.M., Silverman, L.K.: Learning and teaching styles in engineering education. Eng. Educ. 78(7), 674–681 (1988)

    Google Scholar 

  10. Freedman, R.: What is an intelligent tutoring system? Intelligence 11(3), 15–16 (2000)

    Article  Google Scholar 

  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_10

    Chapter  Google Scholar 

  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. Graesser, A.C.: Conversations with autotutor help students learn. Int. J. Artif. Intell. Educ. 26(1), 124–132 (2016)

    Article  Google Scholar 

  14. Gross, S., Mokbel, B., Hammer, B., Pinkwart, N.: Learning feedback in intelligent tutoring systems. Künstliche Intelligenz 29(4), 413–418 (2015)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  18. Knight, W.: AI’s language problem (2016). https://tinyurl.com/y7r9haju

  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)

    Article  Google Scholar 

  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. 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. Lockspeiser, T.M., Kaul, P.: Using individualized learning plans to facilitate learner-centered teaching. J. Pediatr. Adolesc. Gynecol. 29(3), 214–217 (2016)

    Article  Google Scholar 

  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_12

    Chapter  Google Scholar 

  24. Milne, I., Rowe, G.: Difficulties in learning and teaching programming – views of students and tutors. Educ. Inf. Technol. 7(1), 55–66 (2002)

    Article  Google Scholar 

  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. Partovi, H.: Should Computer Science Be A Mandatory Class In U.S. High Schools? (2017). https://tinyurl.com/yddfe2n7

  27. Pashler, H., McDaniel, M., Rohrer, D., Bjork, R.: Learning styles: concepts and evidence. Psychol. Sci. Public Interest 9(3), 106–119 (2008)

    Article  Google Scholar 

  28. Perone, C.S.: Machine Learning: Cosine Similarity for Vector Space Models (Part III). Technical report (2013). http://blog.christianperone.com/2013/09/

  29. Poropat, A.E.: A meta-analysis of the five-factor model of personality and academic performance. Psychol. Bull. 135(2), 322–338 (2009)

    Article  Google Scholar 

  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. 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. Sterling, L.: An education for the 21st century means teaching coding in schools (2015). https://tinyurl.com/ybuqoh56

  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. 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)

    Article  Google Scholar 

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Correspondence to Wai Sze Leung .

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Dlamini, M., Leung, W.S. (2019). Enhancing Object-Oriented Programming Pedagogy with an Adaptive Intelligent Tutoring System. In: Kabanda, S., Suleman, H., Gruner, S. (eds) ICT Education. SACLA 2018. Communications in Computer and Information Science, vol 963. Springer, Cham. https://doi.org/10.1007/978-3-030-05813-5_18

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  • DOI: https://doi.org/10.1007/978-3-030-05813-5_18

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