Abstract
Artificial Intelligence applications in educational field are getting more and more popular during the last decade (1999-2009) and that is why much relevant research has been conducted. In this paper, we present the most interesting attempts to apply artificial intelligence methods such as fuzzy logic, neural networks, genetic programming and hybrid approaches such as neuro – fuzzy systems and genetic programming neural networks (GPNN) in student modeling. This latest research trend is a part of every Intelligent Tutoring System and aims at generating and updating a student model in order to modify learning content to fit individual needs or to provide reliable assessment and feedback to student’s answers. In this paper, we make a brief presentation of methods used to point out their qualities and then we attempt a navigation to the most representative studies sought in the decade of our interest after classifying them according to the principal aim they attempted to serve.
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Vrettaros, J., Pavlopoulos, J., Vouros, G.A., Drigas, A.: The Development of a Self-assessment System for the Learners Answers with the Use of GPNN. In: Lytras, M.D., Carroll, J.M., Damiani, E., Tennyson, R.D. (eds.) WSKS 2008. LNCS (LNAI), vol. 5288, pp. 332–340. Springer, Heidelberg (2008)
Stathakopoulou, R., Magoulas, G., Grigoriadou, M., Samarakou, M.: Neuro –Fuzzy knowledge processing in intelligent learning environments for improved student diagnosis. Information Sciences 170, 273–307 (2005)
Kinshuk, A., Nikov, A., Patel, A.: Adaptive Tutoring in Business Education Using Fuzzy Backpropagation Approach. In: Proceedings of the Ninth International Conference on Human-Computer Interaction, pp. 465–468 (2001)
Grigoriadou, M., Kornilakis, H., Papanikolaou, K., Magoulas, G.: Fuzzy Inference for Student Diagnosis in Adaptive Educational Systems. In: Vlahavas, I.P., Spyropoulos, C.D. (eds.) SETN 2002. LNCS (LNAI), vol. 2308, pp. 191–202. Springer, Heidelberg (2002)
de Arriaga, F., Arriaga, A., El Alami, M., Laureano-Cruces, A., Ramírez-Rodríguez, J.: Fuzzy Logic Applications To Students’ Evaluation in Intelligent Learning Systems. In: En Memorias XVI Congreso Nacional y II Congreso Internacional de Informática y Computación de la ANIEI, Zacatecas, 22-24 de octubre del, vol. I, pp. 161–166 (2003)
Hui-Yu, W., Shyi-Ming, C.: Evaluating Students’ Answerscripts Using Fuzzy Numbers Associated With Degrees of Confidence. IEEE Transactions on Fuzzy Systems 16(2) (April 2008)
Hwang, G.J.: Development of an intelligent testing and diagnostic system on computer networks. In: Proceedings of the National Science Council of ROC, vol. 9(1), pp. 1–9 (1999)
Olds, B., Miller, R., Pavelich, M.: Measuring the Intellectual Development of Engineering Students Using Intelligent Assessment Software. In: Proceedings of the International Conference on Engineering Education, Taipei, Taiwan, August 14-18 (2000)
Vrettaros, J., Vouros, G., Drigas, A.: Development of a Diagnostic System of Taxonomies Using Fuzzy Logic - Case SOLO (useful for e-learning system). In: Proceedings of 5th WSEAS International Conference on Automation & Information (ICAI 2004), Venice, Italy, November 15-17 (2004); Selected and is included also in WSEAS Transactions on Information Science and Applications 1(6) (December 2004)
Zadeh, L.A.: Fuzzy Sets. Information and Control, 338–353 (1965)
Drigas, A.S., Vrettaros, J.: An Intelligent Tool for Building E-Learning Contend-Material Using Natural Language in Digital Libraries. In: Proceedings of WSEAS Int. Conf. on E-AVTIVITIES (E-AVTIVITIES 2004), Rethymno, Crete, Greece, October 24-26 (2004); Selected and is included also in WSEAS Transactions on Information Science and Applications 5(1) (November 2004)
Weon, S., Kim, J.: Learning achievement evaluation strategy using fuzzy membership function. In: Proceedings of the 31st ASEE/IEEE frontiers in education conference, Reno, NV, vol. 1, pp. 19–24 (2001)
McAlister, M., Wermter, S.: Rule Generation from Neural Networks for Student Assessment. In: Proceedings of the International Joint Conference on Neural Networks, Washington, USA (July 1999)
Al-Hammadi, A.S., Milne, R.H.: A Neuro-Fuzzy Classification Approach To the Assessment of Student Performance. In: IEEE International Conference on Fuzzy Systems, July 2004, vol. 2, pp. 837–841 (2004)
Homsi, M., Lutfi, R., Rosa, M.C., Barakat, G.: Student modeling using NN – HMM for EFL course. In: 3rd International Conference on Information and Communication Technologies: From Theory to Applications, ICTTA 2008, April 7-11, pp. 1–6 (2008)
Shen, R., Tang, Y., Zhang, T.: The Intelligent Assessment System in Web_based Distance Learning Education. In: 31st ASEE/IEEE Frontiers in Education Conference (2001)
Othman, M., Ku-Mahamud, K.R., Abu Bakar, A.: Fuzzy evaluation method using fuzzy rule approach in multicriteria analysis. Yugoslav Journal of Operations Research 18(1), 95–107 (2008)
de Arriaga, F., El Alami, M., Arriaga, A.: Evaluation of Fuzzy Intelligent Learning Systems. In: Méndez-Vilas, A., et al. (eds.) Recent Research Develoments in Learning Technologies, Formatex (2005)
Kasabov, N.K., Kim, J.S., Gray, A.R., Watts, M.J.: FuNN – A Fuzzy Neural Network Architecture for Adaptive Learning and Knowledge Acquisition, Technical Report, Department of Information Science, University of Otago, Dunedin, New Zealand (1997)
Nykänen, O.: Inducing Fuzzy Models for Student Classification. Educational Technology & Society 9(2), 223–234 (2006)
Frias-Martinez, E., Magoulas, G.D., Chen, S.Y., Macredie, R.D.: Modeling Human Behavior in User-Adaptive Systems: Recent Advances Using Soft Computing Technique. Expert Systems with Applications 29(2) (2005)
Lane, H.C.: Intelligent Tutoring Systems: Prospects for Guided Practice and Efficient Learning. Whitepaper for the Army’s Science of Learning Workshop, Hampton, VA, August 1-3 (2006)
Brusilovsky, P., Peylo, C.: Adaptive and Intelligent Web – Based Educational Systems. International Journal of Artificial Intelligence in Education 13, 156–169 (2003)
Al Hamadi, A.S., Milne, R.H.: A neuro – fuzzy approach for student performance modeling. In: Proceedings of the 2003 10th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2003, vol. 3, pp. 1078–1081 (2003)
Baylor, A.L.: Intelligent agents as cognitive tools for education. Educational Technology 39(2), 36–40 (1999)
Alimisis, D., Moro, M., Arlegui, J., Pina, A., Frangou, S., Papanikolaou, K.: Robotics & Constructivism in Education: the TERECoP project (2007)
Brusilovsky, P., Eklund, J.: A Study of User Model Based Link Annotation in Educational Hypermedia. Journal of Universal Computer Science 4(4), 429–448 (1998)
Ma, J., Zhou, D.: Fuzzy set approach to the assessment of student-centered learning. IEEE Trans. Educ. 43, 237–241 (2000)
Ali, M.S., Ghatol, A.A.: An Adaptive Neuro Fuzzy Inference System For Student Modeling. In: Web-Based Intelligent Tutoring Systems (2004)
Sevarac, Z.: Neuro Fuzzy Reasoner for Student Modeling. In: Proceedings of the Sixth International Conference on Advanced Learning Technologies, pp. 740–744 (2006)
Salleh, N.S.M., Jais, J., Mazalan, L., Ismail, R., Yussof, S., Ahmad, A., Anuar, A., Mohamad, D.: Sign Language to Voice Recognition: Hand Detection Techniques for Vision-Based Approach. In: Uniten Student Conference on Research and Development (SCOReD), May 14-15 (2007)
Hawkes, L.W., Derry, S.J., Rundensteiner, E.A.: Individualized tutoring using an intelligent fuzzy temporal relational database. International Journal of Man–Machines Studies 33, 409–429 (1990)
Siddique, M.N.H., Tokhi, M.: Training Neural Networks: Backpropagation vs. Genetic Algorithms. In: Proceedings of the IEEE International Joint Conference on Neural Networks, vol. 4, pp. 2673–2678 (2001)
Ma, J.: Group decision support system for assessment of problem-based learning. IEEE Trans. Educ. 39, 388–393 (1996)
Zhou, D., Ma, J., Kwok, R.C.W., Tian, Q.: Group decision support system for project assessment based on fuzzy set theory. Presented at the Proc. 32nd Hawaii Int. Conf. System Sciences (HICSS-32), Honolulu, HI (January 1999)
Kwok, R., Ma, J.: Use of group support system for collaborative assessment. Comput. Educ. Int. J. 32, 109–125 (1999)
VanLhen, K.: Student Modeling. In: Polson, M.C., Richardson, J.J., Lea (eds.) Foundations of Intelligent Tutoring Systems, Hove & London (1988)
Biswas, R.: An application of fuzzy sets in students’ evaluation. Fuzzy Sets Syst. 74(2), 187–194 (1995)
Chen, S.M., Lee, C.H.: New methods for students’ evaluating using fuzzy sets. Fuzzy Sets Syst. 104(2), 209–218 (1999)
Liao, S.-H.: Expert system methodologies and applications - a decade review from 1995 to 2004. Expert Syst. Appl. 28(1), 93–103 (2005)
Drigas, A., Vrettaros, J., Kouremenos, D.: Tele education and e-learning services for teaching English as a second language to Deaf People, whose first language is the Sign Language. WSEAS Transactions on Information Science and Applications 1(3) (September 2004)
Drigas, A.S., Vrettaros, J., Stavrou, L., Kouremenos, D.: Elearning Environment for Deaf people in the E-Commerce and New Technologies Sector. In: 6th WSEAS International Conference on EACTIVITIES, Rethymno (October 20, 2004)
Brusilovsky, P.: Methods and Techniques of Adaptive Hypermedia. User Modeling and User-Adapted Interaction, vol. 6, pp. 87–129. Kluwer Academic Publ., Netherlands (1996)
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Drigas, A.S., Argyri, K., Vrettaros, J. (2009). Decade Review (1999-2009): Artificial Intelligence Techniques in Student Modeling. In: Lytras, M.D., Ordonez de Pablos, P., Damiani, E., Avison, D., Naeve, A., Horner, D.G. (eds) Best Practices for the Knowledge Society. Knowledge, Learning, Development and Technology for All. WSKS 2009. Communications in Computer and Information Science, vol 49. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04757-2_59
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DOI: https://doi.org/10.1007/978-3-642-04757-2_59
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