CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments

by Ali Alkhatlan, Jugal Kalita
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 43
Year of Publication: 2019
Authors: Ali Alkhatlan, Jugal Kalita
10.5120/ijca2019918451

Ali Alkhatlan, Jugal Kalita . Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments. International Journal of Computer Applications. 181, 43 ( Mar 2019), 1-20. DOI=10.5120/ijca2019918451

@article{ 10.5120/ijca2019918451,
author = { Ali Alkhatlan, Jugal Kalita },
title = { Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2019 },
volume = { 181 },
number = { 43 },
month = { Mar },
year = { 2019 },
issn = { 0975-8887 },
pages = { 1-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number43/30402-2019918451/ },
doi = { 10.5120/ijca2019918451 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:08:55.415995+05:30
%A Ali Alkhatlan
%A Jugal Kalita
%T Intelligent Tutoring Systems: A Comprehensive Historical Survey with Recent Developments
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 43
%P 1-20
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper provides interested beginners with an updated and detailed introduction to the field of Intelligent Tutoring Systems (ITS). ITSs are computer programs that use artificial intelligence techniques to enhance and personalize automation in teaching. This paper is a literature review that provides the following: First, a review of the history of ITS along with a discussion on the interface between human learning and computer tutors and how effective ITSs are in contemporary education. Second, the traditional architectural components of an ITS and their functions are discussed along with approaches taken by various ITSs. Finally, recent innovative ideas in ITS systems are presented. This paper concludes with some of the author’s views regarding future work in the field of intelligent tutoring systems.

References
  1. J. Carbonell, “AI in CAI: An artificial-intelligence approach to computer-assisted instruction,” IEEE Transactions on Man-Machine Systems, vol. 11, no. 4, pp. 190–202, 1970.
  2. B. S. Bloom, “The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring,” Educational researcher, vol. 13, no. 6, pp. 4–16, 1984.
  3. R. Nkambou, “Modeling the domain: An introduction to the expert module,” in Advances in Intelligent Tutoring Systems, ser. Studies in Computational Intelligence, R. Nkambou, J. Bourdeau, and R. Mizoguchi, Eds. Springer Berlin Heidelberg, 2010, no. 308, pp. 15–32, DOI: 10.1007/978-3- 642-14363-2 2.
  4. “Handbook of human-computer interaction,” in Handbook of Human-Computer Interaction (Second Edition), M. G. H. K. L. V. Prabhu, Ed. North-Holland, 1997, pp. 1551–1582.
  5. J. Self, “Theoretical foundations for intelligent tutoring systems,” Journal of Artificial Intelligence in Education, vol. 1, no. 4, pp. 3–14, 1990.
  6. K. Vanlehn, “The behavior of tutoring systems,” International journal of artificial intelligence in education, vol. 16, no. 3, pp. 227–265, 2006.
  7. M. Elsom-Cook, Intelligent Computer-aided instruction research at the Open University. Educational Resources Information Center, 1987.
  8. H. S. Nwana, “Intelligent tutoring systems: an overview,” Artificial Intelligence Review, vol. 4, no. 4, pp. 251–277, 1990.
  9. E. Wenger, Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge. Morgan Kaufmann Publishers Inc., 1987.
  10. V. J. Shute and J. Psotka, “Intelligent tutoring systems: Past, present, and future.” DTIC Document, Tech. Rep., 1994.
  11. B. P. Woolf, J. Beck, C. Eliot, and M. Stern, “Growth and maturity of intelligent tutoring systems: A status report,” in Smart machines in education. MIT Press, 2001, pp. 99– 144.
  12. T. Murray, “An overview of intelligent tutoring system authoring tools: Updated analysis of the state of the art,” in Authoring tools for advanced technology learning environments. Springer, 2003, pp. 491–544.
  13. V. Rus, S. D’Mello, X. Hu, and A. Graesser, “Recent advances in conversational intelligent tutoring systems,” AI magazine, vol. 34, no. 3, pp. 42–54, 2013.
  14. A. Mitrovic, “Fifteen years of constraint-based tutors: what we have achieved and where we are going,” User Modeling and User-Adapted Interaction, vol. 22, no. 1-2, pp. 39–72, 2012.
  15. P. Fournier-Viger, R. Nkambou, and E. M. Nguifo, “Building intelligent tutoring systems for ill-defined domains,” in Advances in intelligent tutoring systems. Springer, 2010, pp. 81–101.
  16. B. P. Woolf, Building intelligent interactive tutors: Studentcentered strategies for revolutionizing e-learning. Morgan Kaufmann, 2010.
  17. J. Anania, The effects of quality of instruction on the cognitive and affective learning of students. University of Chicago, 1981.
  18. “The influence of instructional conditions on student learning and achievement,” Evaluation in education, vol. 7, no. 1, pp. 1–92, 1983.
  19. D. C. Merrill, B. J. Reiser, M. Ranney, and J. G. Trafton, “Effective tutoring techniques: A comparison of human tutors and intelligent tutoring systems,” The Journal of the Learning Sciences, vol. 2, no. 3, pp. 277–305, 1992.
  20. K. VanLehn, “The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems,” Educational Psychologist, vol. 46, no. 4, pp. 197–221, 2011.
  21. S. Steenbergen-Hu and H. Cooper, “A meta-analysis of the effectiveness of intelligent tutoring systems on college students’ academic learning.” Journal of Educational Psychology, vol. 106, no. 2, p. 331, 2014.
  22. W. Ma, O. O. Adesope, J. C. Nesbit, and Q. Liu, “Intelligent tutoring systems and learning outcomes: A meta-analysis.” Journal of Educational Psychology, vol. 106, no. 4, p. 901, 2014.
  23. J. A. Kulik and J. Fletcher, “Effectiveness of intelligent tutoring systems a meta-analytic review,” Review of Educational Research, vol. 106, p. 0034654315581420, 2015.
  24. C. Conati, “Intelligent tutoring systems: New challenges and directions.” in IJCAI, vol. 9, 2009, pp. 2–7.
  25. R. Mizoguchi, “Student modeling in ITS,” Emerging Technologies in Education, vol. 8, pp. 35–48, 1995.
  26. J. Ong and S. Ramachandran, “Intelligent tutoring systems: Using AI to improve training performance and ROI,” Stottler Henke Associates, Inc. online http://www. shai. com/papers/ITS using AI to improve training performance and ROI. pdf, 2003.
  27. E. Sierra, R. Garc´ia-Mart´inez, Z. Cataldi, P. Britos, and A. Hossian, “Towards a methodology for the design of intelligent tutoring systems,” Research in Computing Science Journal, vol. 20, pp. 181–189, 2006.
  28. F. S. Gharehchopogh and Z. A. Khalifelu, “Using intelligent tutoring systems in instruction and education,” in 2nd International Conference on Education and Management Technology, vol. 13. IACSIT Press Singapore, 2011, pp. 250– 254.
  29. I. Hatzilygeroudis and J. Prentzas, “Knowledge representation requirements for intelligent tutoring systems,” in Intelligent Tutoring Systems. Springer, 2004, pp. 87–97.
  30. M. C. Desmarais and R. S. Baker, “A review of recent advances in learner and skill modeling in intelligent learning environments,” User Modeling and User-Adapted Interaction, vol. 22, no. 1-2, pp. 9–38, 2012.
  31. K. R. Butcher and V. Aleven, “Using student interactions to foster rule–diagram mapping during problem solving in an intelligent tutoring system.” Journal of Educational Psychology, vol. 105, no. 4, p. 988, 2013.
  32. V. Kodaganallur, R. R. Weitz, and D. Rosenthal, “A comparison of model-tracing and constraint-based intelligent tutoring paradigms,” International Journal of Artificial Intelligence in Education (IJAIED), vol. 15, pp. 117–144, 2005.
  33. V. Aleven, “Rule-based cognitive modeling for intelligent tutoring systems,” in Advances in intelligent tutoring systems. Springer, 2010, pp. 33–62.
  34. A. Mitrovic, K. R. Koedinger, and B. Martin, “A comparative analysis of cognitive tutoring and constraint-based modeling,” in User Modeling 2003, ser. Lecture Notes in Computer Science, P. Brusilovsky, A. Corbett, and F. d. Rosis, Eds. Springer Berlin Heidelberg, 2003, no. 2702, pp. 313– 322, DOI: 10.1007/3-540-44963-9 42.
  35. G.-M. Baschera and M. Gross, “Poisson-based inference for perturbation models in adaptive spelling training,” International Journal of Artificial Intelligence in Education, vol. 20, no. 4, pp. 333–360, 2010.
  36. P. A. Jaques, H. Seffrin, G. Rubi, F. de Morais, C. Ghilardi, I. I. Bittencourt, and S. Isotani, “Rule-based expert systems to support step-by-step guidance in algebraic problem solving: The case of the tutor Pat2Math,” Expert Systems with Applications, vol. 40, no. 14, pp. 5456–5465, 2013.
  37. A. T. Corbett and J. R. Anderson, “Knowledge tracing: Modeling the acquisition of procedural knowledge,” User modeling and user-adapted interaction, vol. 4, no. 4, pp. 253–278, 1994.
  38. H. Cen, K. Koedinger, and B. Junker, “Learning factors analysis–a general method for cognitive model evaluation and improvement,” in International Conference on Intelligent Tutoring Systems. Springer, 2006, pp. 164–175.
  39. P. I. Pavlik Jr, H. Cen, and K. R. Koedinger, “Performance factors analysis–a new alternative to knowledge tracing.” Online Submission, 2009.
  40. S. B. Blessing, S. B. Gilbert, S. Ourada, and S. Ritter, “Authoring model-tracing cognitive tutors,” International Journal of Artificial Intelligence in Education, vol. 19, no. 2, p. 189, 2009.
  41. R. Nkambou, J. Bourdeau, and V. Psych´e, “Building intelligent tutoring systems: An overview,” in Advances in Intelligent Tutoring Systems. Springer, 2010, pp. 361–375.
  42. A. Mitrovic and S. Ohlsson, “Implementing CBM: SQLTutor After Fifteen Years,” International Journal of Artificial Intelligence in Education, pp. 1–10, 2015.
  43. S. Riccucci. (2008) Knowledge management in intelligent tutoring systems - AMS tesi di dottorato - AlmaDL - universit di bologna.
  44. G. Paviotti, P. G. Rossi, and D. Zarka, “Intelligent tutoring systems: an overview,” Pensa Multimedia, 2012.
  45. W. J. Clancey, “Use of Mycin’s rules for tutoring,” Rule- Based Expert Systems. Addison-Wesley, Reading, pp. 19–2, 1984.
  46. B. D. Nye, A. C. Graesser, and X. Hu, “AutoTutor and family: A review of 17 years of natural language tutoring,” International Journal of Artificial Intelligence in Education, vol. 24, no. 4, pp. 427–469, 2014.
  47. S. Moritz and G. Blank, “Generating and evaluating objectoriented designs for instructors and novice students,” Intelligent Tutoring Systems for Ill-Defined Domains: Assessment and Feedback in Ill-Defined Domains., p. 35, 2008.
  48. K. Chrysafiadi and M. Virvou, “Student modeling approaches: A literature review for the last decade,” Expert Systems with Applications, vol. 40, no. 11, pp. 4715–4729, 2013.
  49. E. Mill´an, T. Loboda, and J. L. P´erez-de-la Cruz, “Bayesian networks for student model engineering,” Computers & Education, vol. 55, no. 4, pp. 1663–1683, 2010.
  50. V. Aleven, B. Mclaren, I. Roll, and K. Koedinger, “Toward meta-cognitive tutoring: A model of help seeking with a cognitive tutor,” Int. J. Artif. Intell. Ed., vol. 16, no. 2, pp. 101– 128, 2006.
  51. C. Carmona and R. Conejo, “A learner model in a distributed environment,” in Adaptive Hypermedia and Adaptive Web- Based Systems. Springer, 2004, pp. 353–359.
  52. M. Trella, R. Conejo, D. Bueno, and E. Guzm´an, “An autonomous component architecture to develop WWW-ITS,” in Proceedings of the Workshops on Adaptive Systems for Web-Based Education. Malaga, 2002, pp. 69–80.
  53. C.-H. Lu, C.-W.Wu, S.-H.Wu, G.-F. Chiou, andW.-L. Hsu, “Ontological support in modeling learners’ problem solving process,” Journal of Educational Technology & Society, vol. 8, no. 4, pp. 64–74, 2005.
  54. L. N. Michaud and K. F. McCoy, “Empirical derivation of a sequence of user stereotypes for language learning,” User Modeling and User-Adapted Interaction, vol. 14, no. 4, pp. 317–350, 2004.
  55. A. N. Kumar, “Using enhanced concept map for student modeling in programming tutors.” in FLAIRS Conference, 2006, pp. 527–532.
  56. T. Glushkova, “Adaptive model for user knowledge in the e-learning system,” in Proceedings of the 9th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing. ACM, 2008, p. 78.
  57. C. Limongelli, F. Sciarrone, M. Temperini, and G. Vaste, “Adaptive learning with the LS-plan system: a field evaluation,” Learning Technologies, IEEE Transactions on, vol. 2, no. 3, pp. 203–215, 2009.
  58. E. Gaudioso, M. Montero, and F. Hernandez-Del-Olmo, “Supporting teachers in adaptive educational systems through predictive models: A proof of concept,” Expert Systems with Applications, vol. 39, no. 1, pp. 621–625, 2012.
  59. E. Rich, “Stereotypes and user modeling,” in User Models in Dialog Systems, ser. Symbolic Computation, A. Kobsa and W. Wahlster, Eds. Springer Berlin Heidelberg, 1989, pp. 35–51, DOI: 10.1007/978-3-642-83230-7 2.
  60. J. Kay, “Stereotypes, student models and scrutability,” in Intelligent Tutoring Systems, ser. Lecture Notes in Computer Science, G. Gauthier, C. Frasson, and K. VanLehn, Eds. Springer Berlin Heidelberg, 2000, no. 1839, pp. 19–30, DOI: 10.1007/3-540-45108-0 5.
  61. A. Grubisic, S. Stankov, and B. Zitko, “Stereotype student model for an adaptive e-learning system,” in Proceedings of World Academy of Science, Engineering and Technology, vol. 76. World Academy of Science, Engineering and Technology (WASET), 2013, p. 20.
  62. M. Grigoriadou, H. Kornilakis, K. A. Papanikolaou, and G. D. Magoulas, “Fuzzy inference for student diagnosis in adaptive educational hypermedia,” in Methods and Applications of Artificial Intelligence, ser. Lecture Notes in Computer Science, I. P. Vlahavas and C. D. Spyropoulos, Eds. Springer Berlin Heidelberg, 2002, no. 2308, pp. 191–202, DOI: 10.1007/3-540-46014-4 18.
  63. V. Tsiriga and M. Virvou, “Evaluation of an intelligent webbased language tutor,” in Knowledge-Based Intelligent Information and Engineering Systems, ser. Lecture Notes in Computer Science, V. Palade, R. J. Howlett, and L. Jain, Eds. Springer Berlin Heidelberg, 2003, no. 2774, pp. 275–281, DOI: 10.1007/978-3-540-45226-3 38.
  64. K. Tourtoglou and M. Virvou, “User stereotypes concerning cognitive, personality and performance issues in a collaborative learning environment for UML,” in New Directions in Intelligent Interactive Multimedia, ser. Studies in Computational Intelligence, G. A. Tsihrintzis, M. Virvou, R. J. Howlett, and L. C. Jain, Eds. Springer Berlin Heidelberg, 2008, no. 142, pp. 385–394, DOI: 10.1007/978-3- 540-68127-4 40.
  65. S. Durrani and D. Durrani, “Intelligent tutoring systems and cognitive abilities,” in Proceedings of graduate colloquium on computer sciences (GCCS), 2010.
  66. A. C. Martins, L. Faria, C. V. De Carvalho, and E. Carrapatoso, “User modeling in adaptive hypermedia educational systems,” Journal of Educational Technology & Society, vol. 11, no. 1, pp. 194–207, 2008.
  67. L. Nguyen and P. Do, “Learner model in adaptive learning,” World Academy of Science, Engineering and Technology, vol. 45, pp. 395–400, 2008.
  68. R. A. Faraco, M. C. Rosatelli, and F. A. Gauthier, “An approach of student modelling in a learning companion system,” in Advances in Artificial Intelligence–IBERAMIA 2004. Springer, 2004, pp. 891–900.
  69. H. D. Surjono and J. R. Maltby, “Adaptive educational hypermedia based on multiple student characteristics,” in Advances in Web-Based Learning-ICWL 2003. Springer, 2003, pp. 442–449.
  70. A. Mitrovic, M. Mayo, P. Suraweera, and B. Martin, “Constraint-based tutors: A success story,” in Engineering of Intelligent Systems, ser. Lecture Notes in Computer Science, L. Monostori, J. Vncza, and M. Ali, Eds. Springer Berlin Heidelberg, 2001, no. 2070, pp. 931–940, DOI: 10.1007/3- 540-45517-5 103.
  71. A. Mitrovic, “An intelligent SQL tutor on the web,” International Journal of Artificial Intelligence in Education (IJAIED), vol. 13, pp. 173–197, 2003.
  72. R. Zatarain-Cabada, M. L. Barr´on-Estrada, G. Alor- Hern´andez, and C. A. Reyes-Garc´ia, “Emotion recognition in intelligent tutoring systems for android-based mobile devices,” in Human-Inspired Computing and Its Applications. Springer, 2014, pp. 494–504.
  73. J. H. A. Mitrovic, “J-LATTE: A constraint-based tutor for java,” in 17th Intl. on Conf. on Computers in Education, 2009, pp. 142–146.
  74. N.-T. Le and W. Menzel, “UsingWeighted constraints to diagnose errors in logic programming - the case of an illdefined domain,” Int. J. Artif. Intell. Ed., vol. 19, no. 4, pp. 381–400, 2009.
  75. A. Collins, M. Burstein, and M. Baker, “Human plausible reasoning,” DTIC Document, Tech. Rep., 1988.
  76. T. Gwo-Hshiung, “Multiple attribute decision making: methods and applications,” Multiple Attribute Decision Making: Methods and Applications, 2010.
  77. M. Virvou and B. Du Boulay, “Human plausible reasoning for intelligent help,” User Modeling and User-Adapted Interaction, vol. 9, no. 4, pp. 321–375, 1999.
  78. M. Virvou and K. Kabassi, “F-smile: An intelligent multiagent learning environment,” in Proceedings of 2002 IEEE International Conference on Advanced Learning Technologies-ICALT. Citeseer, 2002, pp. 144–149.
  79. K. Kabassi and M. Virvou, “Personalised adult e-training on computer use based on multiple attribute decision making,” Interacting with Computers, vol. 16, no. 1, pp. 115–132, 2004.
  80. E. Alepis, M. Virvou, and K. Kabassi, “Mobile education: Towards affective bi-modal interaction for adaptivity,” in Third International Conference on Digital Information Management, 2008. ICDIM 2008, 2008, pp. 51–56.
  81. N. Jongsawat, A. Tungkasthan, andW. Premchaiswadi, “Dynamic data feed to Bayesian network model and SMILE web application,” Bayesian Network edited by Dr. Ahmed Rabai, pp. 155–166, 2010.
  82. M. J. SMILE, “Structural modeling, inference, and learning engine and genie: A development environment for graphical decision-theoretic models,” in Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI- 99), July, 1999, pp. 18–22.
  83. A. S. Gertner, C. Conati, and K. VanLehn, “Procedural help in Andes: Generating hints using a Bayesian network student model,” in Proceedings of the Fifteenth National/Tenth Conference on Artificial Intelligence/Innovative Applications of Artificial Intelligence, ser. AAAI ’98/IAAI ’98. American Association for Artificial Intelligence, 1998, pp. 106–111.
  84. E. Mill´an and J. L. P´erez-De-La-Cruz, “A Bayesian diagnostic algorithm for student modeling and its evaluation,” User Modeling and User-Adapted Interaction, vol. 12, no. 2-3, pp. 281–330, 2002.
  85. A. Bunt and C. Conati, “Probabilistic student modelling to improve exploratory behaviour,” User Modeling and User- Adapted Interaction, vol. 13, no. 3, pp. 269–309, 2003.
  86. J.-D. Zapata-Rivera, “Indirectly Visible Bayesian Student Models.” in BMA, 2007.
  87. S. Schiaffino, P. Garcia, and A. Amandi, “Eteacher: Providing personalized assistance to e-learning students,” Computers & Education, vol. 51, no. 4, pp. 1744–1754, 2008.
  88. C. Conati and H. Maclaren, “Empirically building and evaluating a probabilistic model of user affect,” User Modeling and User-Adapted Interaction, vol. 19, no. 3, pp. 267–303, 2009.
  89. K. Muoz, P. M. Kevitt, T. Lunney, J. Noguez, and L. Neri, “PlayPhysics: An emotional games learning environment for teaching physics,” in Knowledge Science, Engineering and Management, ser. Lecture Notes in Computer Science, Y. Bi and M.-A. Williams, Eds. Springer Berlin Heidelberg, 2010, no. 6291, pp. 400–411, DOI: 10.1007/978-3- 642-15280-1 37.
  90. V. M. Chieu, V. Luengo, L. Vadcard, and J. Tonetti, “Student modeling in orthopedic surgery training: Exploiting symbiosis between temporal Bayesian networks and fine-grained didactic analysis,” International Journal of Artificial Intelligence in Education, vol. 20, no. 3, pp. 269–301, 2010.
  91. J. Sabourin, B. Mott, and J. C. Lester, “Modeling Learner Affect with Theoretically Grounded Dynamic Bayesian Networks,” in Affective Computing and Intelligent Interaction, ser. Lecture Notes in Computer Science, S. D’Mello, A. Graesser, B. Schuller, and J.-C. Martin, Eds. Springer Berlin Heidelberg, 2011, no. 6974, pp. 286–295, DOI: 10.1007/978-3-642-24600-5_32.
  92. M. Danaparamita and F. L. Gaol, “Comparing Student Model Accuracy with Bayesian Network and Fuzzy Logic in Predicting Student Knowledge Level,” International Journal of Multimedia and Ubiquitous Engineering, vol. 9, no. 4, pp. 109–120, 2014.
  93. A. S. Drigas, K. Argyri, and J. Vrettaros, “Decade review (1999-2009): Artificial intelligence techniques in student modeling,” in Best Practices for the Knowledge Society. Knowledge, Learning, Development and Technology for All, ser. Communications in Computer and Information Science, M. D. Lytras, P. O. d. Pablos, E. Damiani, D. Avison, A. Naeve, and D. G. Horner, Eds. Springer Berlin Heidelberg, 2009, no. 49, pp. 552–564, DOI: 10.1007/978-3-642- 04757-2 59.
  94. K. Chrysafiadi and M. Virvou, “Evaluating the integration of fuzzy logic into the student model of a web-based learning environment,” Expert Systems with Applications, vol. 39, no. 18, pp. 13 127–13 134, 2012.
  95. P. Suraweera and A. Mitrovic, “KERMIT: A constraintbased tutor for database modeling,” in Intelligent Tutoring Systems, ser. Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2002, no. 2363, pp. 377–387, DOI: 10.1007/3-540-47987-2 41.
  96. J. Bourdeau and M. Grandbastien, “Modeling tutoring knowledge,” in Advances in Intelligent Tutoring Systems, ser. Studies in Computational Intelligence, R. Nkambou, J. Bourdeau, and R. Mizoguchi, Eds. Springer Berlin Heidelberg, 2010, no. 308, pp. 123–143, DOI: 10.1007/978-3- 642-14363-2 7.
  97. Z. Jeremi´c, J. Jovanovi´c, and D. Ga?sevi´c, “Student modeling and assessment in intelligent tutoring of software patterns,” Expert Systems with Applications, vol. 39, no. 1, pp. 210– 222, 2012.
  98. L. Lesta and K. Yacef, “An intelligent teaching assistant system for logic,” in Intelligent Tutoring Systems, ser. Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2002, no. 2363, pp. 421–431, DOI: 10.1007/3-540-47987- 2 45.
  99. N. T. Heffernan, K. R. Koedinger, and L. Razzaq, “Expanding the model-tracing architecture: A 3rd generation intelligent tutor for algebra symbolization,” International Journal of Artificial Intelligence in Education, vol. 18, no. 2, p. 153, 2008.
  100. A. Mitrovic and B. Martin, “Evaluating the effectiveness of feedback in SQL-tutor,” in International Workshop on Advanced Learning Technologies, 2000. IWALT 2000. Proceedings, 2000, pp. 143–144.
  101. C. Kenny and C. Pahl, “Personalised correction, feedback and guidance in an automated tutoring system for skills training,” International Journal of Knowledge and Learning, vol. 4, no. 1, pp. 75–92, 2008.
  102. M. Mendicino, L. Razzaq, and N. T. Heffernan, “A comparison of traditional homework to computer-supported homework,” Journal of Research on Technology in Education, vol. 41, no. 3, pp. 331–359, 2009.
  103. K. Brawner and A. Graesser, “Natural language, discourse, and conversational dialogues within intelligent tutoring systems: A review,” Design Recommendations for Intelligent Tutoring Systems, p. 189, 2014.
  104. D. J. Hacker, J. Dunlosky, and A. C. Graesser, Handbook of metacognition in education. Routledge, 2009.
  105. A. C. Graesser, K. Wiemer-Hastings, P. Wiemer-Hastings, R. Kreuz, T. R. Group et al., “AutoTutor: A simulation of a human tutor,” Cognitive Systems Research, vol. 1, no. 1, pp. 35–51, 1999.
  106. C. P. Ros´e, P. Jordan, M. Ringenberg, S. Siler, K. VanLehn, and A. Weinstein, “Interactive conceptual tutoring in atlasandes,” in Proceedings of AI in Education 2001 Conference, 2001, pp. 151–153.
  107. R. Freedman, “Plan-based dialogue management in a physics tutor,” in Proceedings of the sixth conference on Applied natural language processing. Association for Computational Linguistics, 2000, pp. 52–59.
  108. C. P. Ros´e, “A framework for robust semantic interpretation,” in Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference. Association for Computational Linguistics, 2000, pp. 311– 318.
  109. V. Rus, N. Niraula, and R. Banjade, “DeepTutor: An effective, online intelligent tutoring system that promotes deep learning,” in Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015.
  110. T. C. Koopmans and M. Beckmann, “Assignment problems and the location of economic activities,” Econometrica: journal of the Econometric Society, pp. 53–76, 1957.
  111. M. W. Evens, R.-C. Chang, Y. H. Lee, L. S. Shim, C. W. Woo, Y. Zhang, J. A. Michael, and A. A. Rovick, “Circsimtutor: An intelligent tutoring system using natural language dialogue,” in Proceedings of the fifth conference on Applied natural language processing: Descriptions of system demonstrations and videos. Association for Computational Linguistics, 1997, pp. 13–14.
  112. M. Al Emran and K. Shaalan, “A survey of intelligent language tutoring systems,” in Advances in Computing, Communications and Informatics (ICACCI, 2014 International Conference on. IEEE, 2014, pp. 393–399.
  113. S. D’Mello and A. Graesser, “Design of dialog-based intelligent tutoring systems to simulate human-to-human tutoring,” in Where Humans Meet Machines. Springer, 2013, pp. 233–269.
  114. D. J. Litman, C. P. Ros´e, K. Forbes-Riley, K. VanLehn, D. Bhembe, and S. Silliman, “Spoken versus typed human and computer dialogue tutoring.” IJ Artificial Intelligence in Education, vol. 16, no. 2, pp. 145–170, 2006.
  115. H. Pon-Barry, B. Clark, K. Schultz, E. O. Bratt, and S. Peters, “Advantages of spoken language interaction in dialogue-based intelligent tutoring systems,” in Intelligent Tutoring Systems. Springer, 2004, pp. 390–400.
  116. A. G. Hauptmann and A. I. Rudnicky, “Talking to computers: an empirical investigation,” International Journal of Man-Machine Studies, vol. 28, no. 6, pp. 583–604, 1988.
  117. D. J. Litman and S. Silliman, “Itspoke: An intelligent tutoring spoken dialogue system,” in Demonstration papers at HLT-NAACL 2004. Association for Computational Linguistics, 2004, pp. 5–8.
  118. X. Huang, F. Alleva, H.-W. Hon, M.-Y. Hwang, K.-F. Lee, and R. Rosenfeld, “The sphinx-ii speech recognition system: an overview,” Computer Speech & Language, vol. 7, no. 2, pp. 137–148, 1993.
  119. M. Ben Ammar, M. Neji, A. M. Alimi, and G. Gouard, “The Affective Tutoring System,” Expert Systems with Applications, vol. 37, no. 4, pp. 3013–3023, 2010.
  120. M. Spering, D. Wagener, and J. Funke, “The role of emotions in complex problem-solving,” Cognition and Emotion, vol. 19, pp. 1252–1261, 2005.
  121. C. Frasson and P. Chalfoun, “Managing learner’s affective states in intelligent tutoring systems,” in Advances in Intelligent Tutoring Systems. Springer, 2010, pp. 339–358.
  122. R. S. Baker, S. K. D’Mello, M. M. T. Rodrigo, and A. C. Graesser, “Better to be frustrated than bored: The incidence, persistence, and impact of learners? cognitive– affective states during interactions with three different computer-based learning environments,” International Journal of Human-Computer Studies, vol. 68, no. 4, pp. 223–241, 2010.
  123. S. D’Mello and A. Graesser, “Automatic detection of learner’s affect from gross body language,” Applied Artificial Intelligence, vol. 23, no. 2, pp. 123–150, 2009.
  124. I. Jraidi, P. Chalfoun, and C. Frasson, “Implicit strategies for intelligent tutoring systems,” in Intelligent Tutoring Systems, ser. Lecture Notes in Computer Science, S. A. Cerri, W. J. Clancey, G. Papadourakis, and K. Panourgia, Eds. Springer Berlin Heidelberg, 2012, no. 7315, pp. 1–10, DOI: 10.1007/978-3-642-30950-2 1.
  125. B. P. Woolf, I. Arroyo, D. Cooper, W. Burleson, and K. Muldner, “Affective tutors: Automatic detection of and response to student emotion,” in Advances in Intelligent Tutoring Systems, ser. Studies in Computational Intelligence, R. Nkambou, J. Bourdeau, and R. Mizoguchi, Eds. Springer Berlin Heidelberg, 2010, no. 308, pp. 207–227, DOI: 10.1007/978-3-642-14363-2 10.
  126. I. Arroyo, D. G. Cooper,W. Burleson, B. P.Woolf, K. Muldner, and R. Christopherson, “Emotion sensors go to school,” in Proceedings of the 2009 Conference on Artificial Intelligence in Education: Building Learning Systems That Care: From Knowledge Representation to Affective Modelling. IOS Press, 2009, pp. 17–24.
  127. H. Prendinger and M. Ishizuka, “The empathic companion: A character-based interface that addresses users’affective states,” Applied Artificial Intelligence, vol. 19, no. 3-4, pp. 267–285, 2005.
  128. A. Ogan andW. L. Johnson, “Preface for the special issue on culturally aware educational technologies,” Int J Artif Intell Educ, vol. 25, pp. 173–176, 2015.
  129. B. D. Nye, “Intelligent tutoring systems by and for the developing world: a review of trends and approaches for educational technology in a global context,” International Journal of Artificial Intelligence in Education, vol. 25, no. 2, pp. 177–203, 2015.
  130. A. Ogan, E. Walker, R. Baker, M. M. T. Rodrigo, J. C. Soriano, and M. J. Castro, “Towards understanding how to assess help-seeking behavior across cultures,” International Journal of Artificial Intelligence in Education, vol. 25, no. 2, pp. 229–248, 2015.
  131. P. Mohammed and P. Mohan, “Dynamic cultural contextualisation of educational content in intelligent learning environments using icon,” International Journal of Artificial Intelligence in Education, vol. 25, no. 2, pp. 249–270, 2015.
  132. B. Kyun, Young, Gaming for Classroom-Based Learning: Digital Role Playing as a Motivator of Study: Digital Role Playing as a Motivator of Study. IGI Global, 2010.
  133. A. B. Raut, S. D. A. Uroojussama, U. Farheen, and A. Anwari, “Game based intelligent tutoring system,” International Journal of Engineering Research and General Science, vol. 3, no. 2, 2015.
  134. M. W. Easterday, V. Aleven, R. Scheines, and S. M. Carver, “Using tutors to improve educational games,” in Artificial Intelligence in Education, ser. Lecture Notes in Computer Science, G. Biswas, S. Bull, J. Kay, and A. Mitrovic, Eds. Springer Berlin Heidelberg, 2011, no. 6738, pp. 63–71, DOI: 10.1007/978-3-642-21869-9 11.
  135. G. T. Jackson and D. McNamara, “Motivational impacts of a game-based intelligent tutoring system,” in Twenty-Fourth International FLAIRS Conference, 2011.
  136. R. Moreno and R. E. Mayer, “Role of guidance, reflection, and interactivity in an agent-based multimedia game.” Journal of Educational Psychology, vol. 97, no. 1, p. 117, 2005.
  137. D. Rai and J. E. Beck, “Math learning environment with game-like elements: An incremental approach for enhancing student engagement and learning effectiveness,” in Intelligent Tutoring Systems, ser. Lecture Notes in Computer Science, S. A. Cerri, W. J. Clancey, G. Papadourakis, and K. Panourgia, Eds. Springer Berlin Heidelberg, 2012, no. 7315, pp. 90–100, DOI: 10.1007/978-3-642-30950-2 13.
  138. P. E, Kristine, Exploring Technology for Writing and Writing Instruction. IGI Global, 2013.
  139. J. C. Lester, E. Y. Ha, S. Y. Lee, B. W. Mott, J. P. Rowe, and J. L. Sabourin, “Serious games get smart: Intelligent gamebased learning environments,” AI Magazine, vol. 34, no. 4, pp. 31–45, 2013.
  140. R. Peredo, A. Canales, A. Menchaca, and I. Peredo, “Intelligent web-based education system for adaptive learning,” Expert Systems with Applications, vol. 38, no. 12, pp. 14 690– 14 702, 2011.
  141. P. Brusilovsky, “Adaptive hypermedia: From intelligent tutoring systems to web-based education,” in Intelligent Tutoring Systems, ser. Lecture Notes in Computer Science, G. Gauthier, C. Frasson, and K. VanLehn, Eds. Springer Berlin Heidelberg, 2000, no. 1839, pp. 1–7, DOI: 10.1007/3- 540-45108-0 1.
  142. P. De Bra, “Adaptive educational hypermedia on the web,” Communications of the ACM, vol. 45, no. 5, pp. 60–61, 2002.
  143. J. Eklund and P. Brusilovsky, “The value of adaptivity in hypermedia learning environments: A short review of empirical evidence,” in Proceedings of Second Adaptive Hypertext and Hypermedia Workshop at the Ninth ACM International Hypertext Conference Hypertext, vol. 98, 1998, pp. 11–17.
  144. P. D. Bra and L. Calvi, “Aha! an open adaptive hypermedia architecture,” New Review of Hypermedia and Multimedia, vol. 4, no. 1, pp. 115–139, 1998.
  145. J. Eklund and P. Brusilovsky, “Interbook: an adaptive tutoring system,” UniServe Science News, vol. 12, no. 3, pp. 8– 13, 1999.
  146. P. Brusilovsky, “Adaptive hypermedia for education and training,” in Adaptive Technologies for Training and Education, P. Durlach and A. Lesgold, Eds. Cambridge University Press, 2012, pp. 46–68.
  147. N. M. Dowell, W. L. Cade, Y. Tausczik, J. Pennebaker, and A. C. Graesser, “What works: Creating adaptive and intelligent systems for collaborative learning support,” in Intelligent Tutoring Systems. Springer, 2014, pp. 124–133.
  148. Y. Lou, P. C. Abrami, and S. Apollonia, “Small group and individual learning with technology: A meta-analysis,” Review of educational research, vol. 71, no. 3, pp. 449–521, 2001.
  149. A. Soller, “Supporting social interaction in an intelligent collaborative learning system,” International Journal of Artificial Intelligence in Education (IJAIED), vol. 12, pp. 40–62, 2001.
  150. Y. Hayashi, “Togetherness: Multiple pedagogical conversational agents as companions in collaborative learning,” in Intelligent Tutoring Systems, ser. Lecture Notes in Computer Science, S. Trausan-Matu, K. E. Boyer, M. Crosby, and K. Panourgia, Eds. Springer International Publishing, 2014, no. 8474, pp. 114–123, DOI: 10.1007/978-3-319- 07221-0 14.
  151. R. Kumar and C. P. Rose, “Architecture for building conversational agents that support collaborative learning,” Learning Technologies, IEEE Transactions on, vol. 4, no. 1, pp. 21–34, 2011.
  152. R. Kumar, C. P. Ros´e, Y.-C. Wang, M. Joshi, and A. Robinson, “Tutorial dialogue as adaptive collaborative learning support,” Frontiers in artificial intelligence and applications, vol. 158, p. 383, 2007.
  153. J. K. Olsen, D. M. Belenky, V. Aleven, and N. Rummel, “Using an intelligent tutoring system to support collaborative as well as individual learning,” in Intelligent Tutoring Systems, ser. Lecture Notes in Computer Science, S. Trausan-Matu, K. E. Boyer, M. Crosby, and K. Panourgia, Eds. Springer International Publishing, 2014, no. 8474, pp. 134–143, DOI: 10.1007/978-3-319-07221-0 16.
  154. I. H. Witten and E. Frank, Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2005.
  155. R. S. d Baker, A. T. Corbett, K. R. Koedinger, S. Evenson, I. Roll, A. Z. Wagner, M. Naim, J. Raspat, D. J. Baker, and J. E. Beck, “Adapting to when students game an intelligent tutoring system,” in Intelligent tutoring systems. Springer, 2006, pp. 392–401.
  156. R. S. d Baker, A. Mitrovi´c, and M. Mathews, “Detecting gaming the system in constraint-based tutors,” in User Modeling, Adaptation, and Personalization. Springer, 2010, pp. 267–278.
  157. S. E. Fancsali, “Data-driven causal modeling of “gaming the system” and off-task behavior in cognitive tutor algebra,” in NIPS Workshop on Data Driven Education, 2013.
  158. R. S. d Baker, S. M. Gowda, M.Wixon, J. Kalka, A. Z.Wagner, A. Salvi, V. Aleven, G. W. Kusbit, J. Ocumpaugh, and L. Rossi, “Towards sensor-free affect detection in cognitive tutor algebra.” International Educational Data Mining Society, 2012.
  159. C. Conati and H. Maclaren, “Empirically building and evaluating a probabilistic model of user affect,” User Modeling and User-Adapted Interaction, vol. 19, no. 3, pp. 267–303, 2009.
  160. R. Agrawal and R. Srikant, “Mining sequential patterns,” in Data Engineering, 1995. Proceedings of the Eleventh International Conference on. IEEE, 1995, pp. 3–14.
  161. R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, A. I. Verkamo et al., “Fast discovery of association rules.” Advances in knowledge discovery and data mining, vol. 12, no. 1, pp. 307–328, 1996.
  162. F. Kabanza, R. Nkambou, and K. Belghith, “Path-planning for autonomous training on robot manipulators in space.” in IJCAI, 2005, pp. 1729–1731.
  163. P. Fournier-Viger, R. Nkambou, and E. M. Nguifo, “A knowledge discovery framework for learning task models from user interactions in intelligent tutoring systems,” in MICAI 2008: Advances in Artificial Intelligence. Springer, 2008, pp. 765–778.
  164. R. S. d Baker, “Mining data for student models,” in Advances in intelligent tutoring systems. Springer, 2010, pp. 323– 337.
  165. R. Nkambou, J. Bourdeau, and V. Psych´e, “Building intelligent tutoring systems: An overview,” in Advances in Intelligent Tutoring Systems. Springer, 2010, pp. 361–375.
  166. J.-D. Zapata-Rivera and J. Greer, “Inspectable Bayesian student modelling servers in multi-agent tutoring systems,” International Journal of Human-Computer Studies, vol. 61, no. 4, pp. 535–563, 2004.
  167. S. Stankov, M. Rosi´c, B. ? Zitko, and A. Grubi?si´c, “Tex-sys model for building intelligent tutoring systems,” Computers & Education, vol. 51, no. 3, pp. 1017–1036, 2008.
  168. S. Ainsworth, N. Major, S. Grimshaw, M. Hayes, J. Underwood, B. Williams, and D. Wood, “Redeem: Simple intelligent tutoring systems from usable tools,” in Authoring Tools for Advanced Technology Learning Environments. Springer, 2003, pp. 205–232.
  169. R. Nkambou, C. Frasson, and G. Gauthier, “Cream-tools: An authoring environment for knowledge engineering in intelligent tutoring systems,” in Authoring Tools for Advanced Technology Learning Environments. Springer, 2003, pp. 269–308.
  170. S. B. Blessing, “A programming by demonstration authoring tool for model-tracing tutors,” International Journal of Artificial Intelligence in Education (IJAIED), vol. 8, pp. 233– 261, 1997.
  171. S. Hsieh and P. Hsieh, “Intelligent tutoring system authoring tool for manufacturing engineering education,” International Journal of Engineering Education, vol. 17, no. 6, pp. 569–579, 2001.
  172. P. Jordan, C. P. Ros´e, and K. VanLehn, “Tools for authoring tutorial dialogue knowledge,” in Artificial Intelligence in Education: AI-ED in the Wired and Wireless Future, Proceedings of AI-ED, 2001, pp. 222–233.
  173. V. Aleven, J. Sewall, O. Popescu, M. van Velsen, S. Demi, and B. Leber, “Reflecting on twelve years of ITS authoring tools research with CTAT,” Design Recommendations for Intelligent Tutoring Systems: Authoring Tools and Expert Modeling Techniques, p. 263, 2015.
  174. A. Mitrovic, B. Martin, P. Suraweera, K. Zakharov, N. Milik, J. Holland, and N. McGuigan, “ASPIRE: an authoring system and deployment environment for constraint-based tutors,” 2009.
  175. A. Graesser and X. Hu, “AutoTutor script authoring tool,” Design Recommendations for Intelligent Tutoring Systems: Authoring Tools and Expert Modeling Techniques, p. 199, 2015.
  176. M. Hoffman and C. Ragusa, “Unwrapping GIFT: A primer on authoring tools for the generalized intelligent framework for tutoring,” in Generalized Intelligent Framework for Tutoring (GIFT) Users Symposium (GIFTSym2), 2015, p. 11.
Index Terms

Computer Science
Information Sciences

Keywords

Tutoring systems intelligent tutoring systems artificial intelligence

Powered by PhDFocusTM