Student modeling approaches: A literature review for the last decade

Expert Systems with Applications

Volumn 40, Issue 11, 2013, Pages 4715-4729

  Chrysafiadi, Konstantina ^a   Virvou, Maria ^a  

^a UNIVERSITY OF PIRAEUS   (Greece)

Author keywords

Adaptive learning; Intelligent tutoring systems; Personalised e learning; Student modeling

Indexed keywords

ADAPTIVE LEARNING; EDUCATIONAL APPLICATIONS; EDUCATIONAL SOFTWARE; INTELLIGENT TUTORING SYSTEM; LITERATURE REVIEWS; MOBILE-LEARNING SYSTEM; SOFTWARE DEVELOPER; STUDENT MODELING;

BAYESIAN NETWORKS; COMPUTER AIDED INSTRUCTION; E-LEARNING; LEARNING SYSTEMS; UNCERTAINTY ANALYSIS;

STUDENTS;

EID: 84876033227     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2013.02.007     Document Type: Review

References (159)

1. Albano, G. (2011). Knowledge, skills, competencies: A model for mathematics e-learning. In Proceedings of the 18th international conference on telecommunications (pp. 214-225). Ayia Napa, Cyprus.
2. E. Alepis, and M. Virvou Automatic generation of emotions in tutoring agents for affective e-learning in medical education Expert Systems with Applications 38 2011 9840 9847
3. Alepis, E., Virvou, M., & Kabassi., K. (2008). Mobile education: Towards affective bi-modal interaction for adaptivity. In Proceedings of the 3rd international conference on digital information management, ICDIM 2008 (pp. 51-56). London.
4. Al-Hmouz, A., Shen, J., Yan, J., & Al-Hmouz, R. (2010). Enhanced learner model for adaptive mobile learning. In Proceedings of the 12th international conference on information integration and web-based applications & services (pp. 783-786). Paris, France.
5. Al-Hmouz, A., Shen, J., Yan, J., & Al-Hmouz, R. (2011). Modeling mobile learning system using ANFIS. In Proceedings of the 11th IEEE international conference on advanced learning technologies (ICALT 2011) (pp. 32-36). Athens, USA.
6. Arroyo, I., Meheranian, H., & Woolf, B. P. (2010). Effort-based tutoring: An empirical approach to intelligent tutoring. In Proceedings of the 3rd international conference on educational data mining (pp. 1-10). Pittsburgh, PA, USA.
7. Baghaei, N., Mitrovic, A., & Irwin, W. (2005). A constraint-based tutor for learning object-oriented analysis and design using UML. In Proceedings of international conference on computers in education 2005 (pp. 9-16). Singapore.
8. Baker, R. S. (2007). Modeling and understanding students' off-task behavior in intelligent tutoring systems. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 1059-1068). San Jose, California, USA.
9. Baker, R. S., Corbett, A. T., Koedinger, K. R., & Wagner, A. Z. (2004). Off-task behavior in the cognitive tutor classroom: When students 'game the system. In Proceedings of the SIGCHI conference on human factors in computing systems (CHI '04) (pp. 383-390). Vienna, Austria.
10. Baker, R. S., Goldstein, A. B., & Heffernan, N. T. (2010). Detecting the moment of learning. In Proceedings of the ACM international conference on interactive tabletops and surfaces (pp. 25-34). Saarbrücken, Germany.
11. Balakrishnan, A. (2011). On modeling the affective effect on learning. In Proceedings of the 5th international conference on multi-disciplinary trends in artificial intelligence (pp. 225-235). Hyderabald, India.
12. M. Barak Motivating self-regulated learning in technology education International Journal of Technology and Design Education 20 4 2010 381 401
13. G.M. Baschera, and M. Gross Poisson-based inference for perturbation models in adaptive spelling training International Journal of Artificial Intelligence in Education 20 2010 1 3
14. C.C. Bishop, and D. Wheeler The Myers-Briggs personality type and its relationship to computer programming Journal of Research on Computing in Education 26 1994 358 371
15. K. Bontcheva, and Y. Wilks Tailoring automatically generated hypertext User Modeling and User-Adapted Interaction 15 1-2 2005 135 168
16. Boticario, J. G., Santos, O. C., & van Rosmalen, P. (2005). Issues in developing standard-based adaptive learning management systems. In Proceedings of the EADTU 2005 working conference: Towards Lisbon 2010: Collaboration for innovative content in lifelong open and flexible learning.
17. J.S. Brown, and R.R. Burton Multiple representation of knowledge for tutorial reasoning D. Bobrow, A. Collins, Representation and understanding: Studies in cognitive science 1975 Academic Press New York
18. P. Brusilovsky, and E. Millán User models for adaptive hypermedia and adaptive educational systems P. Brusilovsky, A. Kobsa, W. Neidl, The adaptive web: Methods and strategies of web personalization 2007 Springer-Verlag Berlin, Heidelberg 3 53
19. A. Bunt, and C. Conati Probabilistic student modelling to improve exploratory behaviour User Modeling and User-Adapted Interaction 13 3 2003 269 309
20. M.H. Burstein, and A.M. Collins Modeling a theory of human plausible reasoning T. O'Shea, V. Sgurev, Artificial intelligence III: Methodology, systems, applications 1988 Elsevier Science Publishers B.V. North Holland 21-28
21. M.H. Burstein, A. Collins, and M. Baker Plausible generalisation: Extending a model of human plausible reasoning Journal of the Learning Sciences 3 1991 319 359
22. Carmona, C., & Conejo, R. (2004). A learner model in a distributed environment. In Proceedings of the 3rd international conference on adaptive hypermedia and adaptive web-based systems (AH'2004) (pp. 353-359). Eindhoven, the Netherlands.
23. Carmona, C., Castillo, G., & Millán, E. (2008). Designing a dynamic Bayesian network for modeling students' learning styles. In Proceedings of the 8th IEEE international conference on advanced learning technologies (ICALT 2008) (pp. 346-350). Santander, Cantabria, Spain.
24. G. Castillo, J. Gama, and A.M. Breda An adaptive predictive model for student modeling Advances in web-based education: Personalized learning environments 2009 Information Science Publishing USA 70 92
25. S. Cetintas, L. Si, Y.P. Xin, and C. Hord Automatic detection of off-task behaviors in intelligent tutoring systems with machine learning techniques IEEE Transactions on Learning Technologies 3 3 2010 228 236
26. Cheung, R., Wan, C., & Cheng, C. (2010). An ontology-based framework for personalized adaptive learning. In Proceedings of the 9th international conference on web-based learning (ICWL 2010) (pp. 52-61). Shanghai, China.
27. 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 Journal of Artificial Intelligence in Education 20 3 2010 269 301
28. Chrysafiadi, K., & Virvou, M. (2008). Personalized teaching of a programming language over the web: Stereotypes and rule-based mechanisms. In Proceedings of the 8th joint conference on knowledge-based software engineering (pp. 484-492). Piraeus, Greece.
29. Chrysafiadi K., & Virvou, M. (in press). Evaluating the integration of fuzzy logic into the student model of a web-based learning environment, Available online 15 June 2012, Accepted Manuscript, http://dx.doi.org/10.1016/j. eswa.2012.05.089.
30. W. Clancey The role of qualitative models in instruction J. Self, Artificial intelligence and human learning 1988 Chapman and Hall Computing
31. J. Clemente, J. Ramírez, and A. de Antonio A proposal for student modeling based on ontologies and diagnosis rules Expert Systems with Applications 38 7 2011 8066 8078
32. A. Collins, and R. Michalski The logic of plausible reasoning: A core theory Cognitive Science 13 1989 1 49
33. C. Conati, and H. Maclaren Empirically building and evaluating a probabilistic model of user affect User Modeling and User-Adapted Interaction 19 3 2009 267 303
34. Conati, C., & Zhou, X. (2002). Modeling students' emotions from cognitive appraisal in educational games. In Proceedings of the 6th international conference on intelligent tutoring systems (pp. 944-954). Biarritz, France and San Sebastian, Spain.
35. C. Conati, A. Gertner, and K. Vanlehn Using Bayesian networks to manage uncertainty in student modeling User Modeling and User-Adapted Interaction 12 4 2002 371 417
36. 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 22 1-2 2012 9 38
37. V. Devedzic Semantic web and education (monograph) 2006 Springer, Berlin Heidelberg New York
38. Dolog, P., Henze, N., Nejdl, W., & Sintek, M. (2004). The personal reader: Personalizing and enriching learning resources using semantic web technologies. In Proceedings of the 3rd international conference on adaptive hypermedia and adaptive web-based systems (pp. 85-94). Eindhoven, Netherlands.
39. Drigas, A., Argyri, K., & Vrettaros, J. (2009). Decade review (1999-2009): Artificial intelligence techniques in student modeling. In Proceedings of the 2nd World summit on the knowledge society (WSKS 2009) (pp. 552-564). Chania, Crete, Greece.
40. Durrani, S., & Durrani, D. S. (2010). Intelligent tutoring systems and cognitive abilities. In Proceedings of graduate colloquium on computer sciences (GCCS), Department of Computer Science, FAST-NU Lahore, 1.
41. Faraco, R. A., Rosatelli, M. C., & Gauthier, F. A. O. (2004). An approach of student modeling in a learning companion system. In Proceedings of IX IBERAMIA (pp. 891-900). Puebla, Mexico.
42. R.M. Felder, and L.K. Silverman Learning and teaching styles Engineering Education 78 7 1988 674 681
43. Felder, R. M., & Soloman, B. A. (2003). Learning styles and strategies. URL last accessed on 2012-06-28. < http://www.ncsu.edu/felder- public/ILSdir/styles.htm >.
44. J.H. Flavell Metacognitive aspects of problem solving L.B. Resnick, The nature of intelligence 1976 Erlbaum Hillsdale 231 236
45. E. Gaudioso, M. Montero, and F. Hernandez-del-Olmo Supporting teachers in adaptive educational system through predictive models: A proof of concept Expert Systems with Applications 39 1 2012 621 625
46. R. Glaser, A. Lesgold, and S. Lajoie Toward a cognitive theory for the measurement of achievement R. Ronning, J. Glover, J.C. Conoley, J. Witt, The influence of cognitive psychology on testing and measurement: The Buros-Nebraska symposium on measurement and testing Vol. 3 1987 Hillsdale NJ Erlbaum 41 85
47. Glushkova, T. (2008). Adaptive model for user knowledge in the e-learning system. In Proceedings of the international conference on computer systems and technologies (CompSysTech'08).
48. Goel, G., Lallé, S., & Luengo, V. (2012). fuzzy logic representation for student modelling case study on geometry. In Proceedings of the 11th international conference on intelligent tutoring systems (pp. 428-433). Chania, Greece.
49. Goguadze, G., Sosnovsky, S. A., Isotani, S., & McLaren, B. M. (2011). Evaluating a Bayesian student model of decimal misconceptions. In The proceedings of the 4th International conference on educational data mining (pp. 301-306). Eindhoven, the Netherlands.
50. Gonzalez, C., Burguillo, J. C., & Llamas, M. (2006). A qualitative comparison of techniques for student modeling in intelligent tutoring systems. In Proceedings of the 36th frontiers in education conference (pp. 13-18).
51. Grigoriadou, M., Kornilakis, H., Papanikolaou, K. A., & Magoulas, G. D. (2002). Fuzzy inference for student diagnosis in adaptive educational hypermedia. In Proceedings of the 2nd Hellenic conference on AI: Methods and applications of artificial intelligence (pp. 191-202). Thessaloniki, Greece.
52. Hernández, Y., Sucar, L., & Arroyo-Figueroa, G. (2010). Evaluating an affective student model for intelligent learning environments. In Proceedings of the 12th Ibero-American conference on advances in artificial intelligence (IBERAMIA'10) (pp. 473-482). Bahía Blanca, Argentina.
53. Holland, J., Mitrovic, A., & Martin B. (2009). J-Latte: A constraint-based tutor for java. In Proceedings of the 17th international conference on computers in education (pp. 142-146). Hong Kong.
54. C.L. Hwang, and K. Yoon Multiple attribute decision making: Methods and applications ecture notes in economics and mathematical systems 186 1981 Springer Berlin
55. Inventado, P. S., Legaspi, R., The Duy Bui, & Suarez., M. (2010) Predicting student's appraisal of feedback in an ITS using previous affective states and continuous affect labels from EEG data. In Proceedings of the 18th international conference on computers in education (pp. 71-75). Putrajaya, Malaysia.
56. A. Jameson Numerical uncertainty management in user and student modeling: An overview of systems and issues User Modeling and User-Adapted Interaction 5 3-4 1996 193 251
57. Z. Jeremić, J. Jovanović, and D. Gasvić Student modeling and assessment in intelligent tutoring of software patterns Expert Systems with Applications 39 1 2012 210 222
58. B. Jia, S. Zhong, T. Zheng, and Z. Liu The study and design of adaptive learning system based on fuzzy set theory Z.A.D. Cheok, W. Müller, X. Zhang, K. Wong, Transactions on edutainment IV 2010 Springer-Verlag Berlin, Heidelberg pp. 1-11
59. W.L. Johnson, J.W. Rickel, and J.C. Lester Animated pedagogical agents: Face-to-face interaction in interactive learning environment International Journal of Artificial Intelligence in Education 11 2000 47 78
60. D.H. Jonassen, and B.L. Grabowski Handbook of individual differences, learning and instruction 1993 Lawrence Erlbaum Associates Hillsdale, NJ
61. Jurado, F., Santos, O. C., Redondo, M. A., Boticario, J. G., & Ortega, M. (2008). Providing dynamic instructional adaptation in programming learning. In Proceedings of the 3rd international workshop on hybrid artificial intelligence systems (pp. 329-336). Burgos, Spain.
62. K. Kabassi, and M. Virvou Personalised adult e-training on computer use based on multiple attribute decision making Interacting with Computers 16 1 2004 115 132
63. R. Kass Building a user model implicitly from a cooperative advisory dialog User Modeling and User-Adapted Interaction 1 1991 203 258
64. A.A. Kassim, S.A. Kazi, and S. Ranganath A web-based intelligent learning environment for digital systems International Journal of Engineering Education 20 1 2004 13 23
65. Katsionis, G., & Virvou, M. (2004). A cognitive theory for affective user modelling in a virtual reality educational game. In Proceedings of 2004 IEEE international conference on systems, man, and cybernetics (pp. 1209-1213).
66. Kavcic, A. (2004). Fuzzy student model in InterMediActor platform. In Proceedings of the 26th international conference on information technology interfaces (pp. 297-302). Croatia.
67. J. Kay Stereotypes, student models and scrutability Intelligent Tutoring Systems 2000 19 30
68. A. Kofod-Petersen, S.A. Petersen, G.G. Bye, L. Kolås, and A. Staupe Learning in an ambient intelligent environment-towards modeling learners through stereotypes Revue d'Intelligence Artificielle 22 5 2008 569 588
69. Kosba, E., Dimitrova, V., & Boyle R. (2003). Using fuzzy techniques to model students in web-based learning environment. In Proceedings of the 7th international conference on knowledge-based intelligent information & engineering systems (pp. 222-229). United Kingdom.
70. Kosba, E., Dimitrova, V., & Boyle R. (2005). Using student and group models to support teachers in web-based distance education. In Proceedings of the international conference on user modeling (pp. 124-133). Edinburgh.
71. Kumar, A. (2006a). Using enhanced concept map for student modeling in programming tutors. In Proceedings of the 19th international Florida artificial intelligence research society conference (pp. 527-532). Melbourne Beach.
72. Kumar, A. (2006b). A scalable solution for adaptive problem sequencing and its evaluation. In Proceedings of the 4th international conference on adaptive hypermedia and adaptive web-based systems (AH'2006) (pp. 161-171). Dublin, Ireland.
73. Kyriacou, D. (2008). A scrutable user modelling infrastructure for enabling life-long user modelling. In Proceedings of the 5th international conference on adaptive hypermedia and adaptive web-based systems (pp. 421-425). Hannover, Germany.
74. N.T. Le, and W. Menzel Using weighted constraints to diagnose errors in logic programming-The case of an Ill-defined domain Journal on Artificial Intelligence in Education 19 2 2009 382 400
75. Lehman, B., Matthews. M., D'Mello, S., & Person, N. (2008). What are you feeling? Investigating student affective states during expert human tutoring sessions. In Proceedings of the 9th international conference on intelligent tutoring systems (ITS 2008) (pp 50-59). Montreal.
76. Li, N., Cohen, W. W., Koedinger, K. R., & Matsuda, N. (2011). A machine learning approach for automatic student model discovery. In Proceedings of conf. on educational data mining (EDM 2011) (pp. 31-40). Eindhoven, the Netherlands.
77. C. Limongelli, F. Sciarrone, M. Temperini, and G. Vaste Adaptive learning with the LS-Plan system: A field evaluation IEEE Transactions on Learning Technologies 2 3 2009 203 215
78. C.-l. Liu Using bayesian networks for student modeling R.M. Viccari, P. Augustin-Jaques, R. Verdin, Agent-based tutoring systems by cognitive and affective modeling 2008 Igi Global pp. 97-113
79. Liu, Z., & Wang, H. (2007). A modeling method based on bayesian networks in intelligent tutoring system. In Proceedings of the 11th international conference on computer supported cooperative work in design (pp. 967-972). Melbourne, Australia.
80. J.J. Lo, Y.C. Chan, and S.W. Yeh Designing an adaptive web-based learning system based on students' cognitive styles identified online Computers & Education 58 2012 209 222
81. C.-H. Lu, C.-S. Ong, and W.-L. Hsu Using an ITS as an arithmetic assistant for teachers 3-year review Journal of Internet Technology 8 4 2007 389 398
82. C.-H. Lu, C.-W. Wu, S.-H. Wu, G.-F. Chiou, and W.-L. Hsu Ontological support in modeling learners' problem solving process Educational Technology & Society 8 4 2005 64 74
83. L. Mahnane, M.T. Laskri, and P. Trigano An adaptive hypermedia system integrating thinking style (AHS-TS): Model and experiment International Journal of Hybrid Information Technology 5 1 2012 11 28
84. Martin, B. (1999). Constraint-based student modeling: Representing student knowledge. In Proceedings of the 3rd New Zealand computer science research students' conference (pp. 22-29). Hamilton NZ.
85. A.C. Martins, L. Faria, C. Vaz de Carvalho, and E. Carrapatoso User modeling in adaptive hypermedia educational systems Educational Technology & Society 11 1 2008 194 207
86. Mayo, M. J. (2001). Bayesian student modelling and decision-Theoretic selection of tutorial actions in intelligent tutoring systems. PhD Thesis. Retrieved 29 June 2012, from < http://www.cosc.canterbury.ac.nz/research/ reports/PhdTheses/2001/phd-0102.pdf >.
87. M. Mayo, and A. Mitrovic Optimising ITS behavior with Bayesian networks and decision theory International Journal of Artificial Intelligence in Education 12 2001 124 153
88. L.N. Michaud, and K.F. McCoy Empirical derivation of a sequence of user stereotypes for language learning User Modeling and User-Adapted Interaction 14 2004 317 350
89. E. Millán, and J.-L. Perez de la Cruz A Bayesian diagnostic Algorithm for student modeling User Modeling and User-Adapted Interaction 12 2/3 2002 281 330
90. E. Millán, T. Loboda, and J.L. Pérez-de-la-Cruz Bayesian networks for student model engineering Computers & Education 55 4 2010 1663 1683
91. A. Mitrovic An intelligent SQL tutor on the web International Journal of Artificial Intelligence in Education 13 2-4 2003 173 197
92. Mitrovic, A., & Martin, B. (2006). Evaluating the effects of open student models on learning. In Proceedings of the 2nd international conference on adaptive hypermedia and adaptive web-based systems (pp. 296-305).
93. Mitrovic, A., Mayo, M., Suraweera, P., & Martin, B. (2001). Constraint-based tutors: A success story. In Proceedings of 14th international conference on industrial and engineering applications of artificial intelligence and expert systems (IEA/AIE-2001) (pp. 931-940). Budapest.
94. Muñoz, K., Mc Kevitt, P., Lunney, T., Noguez, J., & Neri, L. (2010). PlayPhysics: An emotional game learning environment for teaching physics. In Proceedings of the 4th international conference on knowledge science, engineering and management (KSEM' 10) (pp. 400-411). Belfast, Northern Ireland, UK.
95. K. Muñoz, P. Mc Kevitt, T. Lunney, J. Noguez, and L. Neri An emotional student model for game-play adaptation Entertainment Computing 2 2 2011 133 141
96. Nguyen, L., & Do, P. (2008). Learner model in adaptive learning. In Proceedings of World academy of science, engineering and technology (pp. 396-401).
97. Nguyen, L., & Do, P. (2009). Combination of Bayesian network and overlay model in user modeling. In Proceedings of the 9th international conference on computational science (pp. 5-14). Baton Rouge, Louisiana, USA.
98. Nguyen, C. D., Vo, K. D., Bui, D. B., & Nguyen, D. T. (2011). An ontology-based IT student model in an educational social network. In Proceedings of the 13th international conference on information integration and web-based applications and services (iiWAS '11) (pp. 379-382). Bali, Indonesia.
99. S. Ohlsson Constraint-based student modeling J.E. Greer, G.I. McCalla, Student modeling: The key to individualized knowledge-based instruction 1994 Springer-Verlag Berlin 167 189
100. S. Ohlsson Learning from performance errors Psychological Review 103 2 1996 241 262
101. Ohlsson, S., & Mitrovic, A. (2006). Constraint-based modeling: An introduction. In Proceedings of the the 28th annual conference of the cognitive science society. Vancouver, Canada.
102. A. Ortony, G.L. Clore, and A. Collins The cognitive structure of emotions 1988 Cambridge University Press Cambridge
103. K.A. Papanikolaou, M. Grigoriadou, H. Kornilakis, and G.D. Magoulas Personalizing the interaction in a web-based educational hypermedia system: The case of INSPIRE User Modeling and User-Adapted Interaction 13 3 2003 213 267
104. Parvez, S. M., & Blank, G. D. (2008). Individualizing tutoring with learning style based feedback. In Proceedings of the 9th international conference on intelligent tutoring systems (pp. 291-301). Montreal.
105. R. Pekrun, A.C. Frenzel, T. Goetz, and R.P. Perry The control value theory of achievement emotions: An integrative approach to emotions in education P.A. Shutz, R. Pekrun, Emotion in education 2007 Elsevier London 13 36
106. Peña, A., & Kayashima, M. (2011). Improving students' meta-cognitive skills within intelligent educational systems: A Review. In Proceedings of the 6th international conference on Foundations of augmented cognition: Directing the future of adaptive systems (pp. 442-451).
107. Peña, A., & Sossa, H. (2010). Semantic representation and management of student models: An approach to adapt lecture sequencing to enhance learning. In Proceedings of the 9th Mexican international conference on Advances in artificial intelligence: Part I, Pachuca (pp. 175-186). Mexico.
108. J. Pearl Probabilistic Reasoning in expert systems: Networks of plausible inference 1988 Morgan Kaufmann Publishers, Inc. San Francisco
109. J. Pearl Decision making under uncertainty ACM Computing Surveys 28 1 1996 89 92
110. E. Popescu Diagnosing students' learning style in an educational hypermedia system Cognitive and emotional processes in web-based education: Integrating human factors and personalization Advances in web-based learning book series 2009 IGI Global 187 208
111. Popescu, E., Badica, C., & Moraret, L. (2009). WELSA: An intelligent and adaptive web-based educational system. In Proceedings of the 3rd symposium on intelligent distributed computing (pp. 175-185). Ayia Napa, Cyprus.
112. E. Popescu, C. Badica, and L. Moraret Accommodating learning styles in an adaptive educational system Informatica 34 2010 451 462
113. Pramitasari, L., Hidayanto, A. N., Aminah, S., Krisnadhi, A. A., & Ramadhanie, M. A. (2009). Development of student model ontology for personalization in an e-learning system based on semantic web. In Proceedings of the international conference on advanced computer science and information systems (ICACSIS 2009) (pp. 434-439). Universitas Indonesia, Depok, Indonesia.
114. E. Rich User modelling via stereotypes Cognitive Science 3 4 1979 329 354
115. R. Rivers Embedded user models-where next? Interacting with Computers 1 1989 14 30
116. Rodrigo, M., Baker, R., Maria, L., Sheryl, L., Alexis, M., & Sheila, P. (2007). Affect and usage choices in simulation problem solving environments. In Proceedings of the 13th international conference on artificial intelligence in education (pp. 145-152). Marina Del Ray, CA, USA.
117. Sabourin, J., Mott, B., & Lester, J. C. (2011). Modeling learner affect with theoretically grounded dynamic Bayesian networks. In Proceedings of the 4th international conference on affective computing and intelligent interaction (pp. 286-295). Memphis, Tennessee.
118. Salim, N., & Haron, N. (2006). The construction of fuzzy set and fuzzy rule for mixed approach in adaptive hypermedia learning system. In Proceedings of the 1st international conference on technologies for E-learning and digital entertainment (Edutainment 2006) (pp. 183-187). Hangzhou, China.
119. G. Salomon Studying the flute and the orchestra: Controlled vs. classroom research on computers International Journal of Educational Research 14 1990 521 532
120. S. Schiaffino, P. Garcia, and A. Amandi ETeacher: Providing personalized assistance to e-learning students Computers & Education 51 4 2008 1744 1754
121. J.A. Self Bypassing the intractable problem of student modelling C. Frasson, G. Gauthier, Intelligent tutoring systems: At the crossroads of AI and education 1990 Ablex Norwood, NJ 107 123
122. H.G. Shakouri, and M. Menhaj A systematic fuzzy decision-making process to choose the best model among a set of competing models IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 38 5 2008 1118 1128
123. H.G. Shakouri, and N. Tavassoli Implementation of a hybrid fuzzy system as a decision support process: A FAHP-FMCDM-FIS composition Expert Systems with Applications 39 3 1998 3682 3691
124. J.A. Shapiro An algebra subsystem for diagnosing students' input in a physics tutorin system International Journal of Artificial Intelligence in Education 15 3 2005 205 228
125. R. Sison, and M. Shimura Student modeling and machine learning International Journal of Artificial Intelligence in Education 9 1998 128 158
126. Smith, S. (1998). Tutorial on. Retrieved 15 Mar, 2002, from < http://www.cs.mdx.ac.uk/staffpages/serengul/table.of.contents.htm >.
127. S. Sosnovsky, and D. Dicheva Ontological technologies for user modeling International Journal of Metadata, Semantics and Ontologies 5 2 2010 32 71
128. Spada, H. (1993). How the role of cognitive modeling for computerized instruction is changing. In Proceedings of AI-ED'93, World conference on artificial intelligence in education (pp. 21-25). Edinburgh, Scotland.
129. Staff, C. (2001) HyperContext: A framework for adaptive and adaptable hypertext. Ph.D. Thesis. University of Sussex.
130. Stansfield, J. C., Carr, B., & Goldstein, I. P. (1976). Wumpus advisor I: A first implementation of a program that tutors logical and probabilistic reasoning skills. At Lab Memo 381, Massachusetts Institute of Technology, Cambridge, Massachusetts.
131. R. Stathacopoulou, G.D. Magoulas, M. Grigoriadou, and M. Samarakou Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis Information Sciences 170 2-4 2005 273 307
132. L.E. Sucar, and J. Noguez Student modeling O. Pourret, P. Naom, B. Marcot, Bayesian networks: A practical guide to applications 2008 J. Wiley & Sons West Sussex 173 185
133. P. Suraweera, and A. Mitrovic An intelligent tutoring system for entity-relationship modelling Artificial Intelligence in Education 14 3-4 2004 375 417
134. Surjono, H., & Maltby, J. (2003). Adaptive educational hypermedia based on multiple student characteristics. In Proceedings of the 2nd international conference on web-based learning (pp. 442-449). Melbourne, Australia.
135. Thomson, D., & Mitrovic, A. (2009). Towards a negotiable student model for constraint-based ITSs. In Proceedings 17th international conference on computers in education (pp. 83-90), Hong Kong.
136. C.-Y. Ting, and S. Phon-Amnuaisuk Properties of Bayesian student model for INQPRO Applied Intelligence 36 2 2012 391 406
137. Tourtoglou, K., & Virvou, M. (2008). User stereotypes concerning cognitive, personality and performance issues in a collaborative learning environment for UML. In Proceedings of the 1st international symposium on intelligent interactive multimedia systems and services (KES-IIMSS 2008) (pp. 385-394). Piraeus, Greece.
138. K. Tourtoglou, and M. Virvou An intelligent recommender system for trainers and trainees in a collaborative learning environment for UML Journal of Intelligent Decision Technologies 6 2 2012 79 95
139. G. Tsaganoua, M. Grigoriadou, T. Cavoura, and D. Koutra Evaluating an intelligent diagnosis system of historical text comprehension Expert Systems with Applications 25 2003 493 502
140. Tsiriga, V., & Virvou, M. (2002). Initializing the student model using stereotypes and machine learning. In Proceedings of the 2002 IEEE international conference on system, man and cybernetics (pp. 404-409).
141. V. Tsiriga, and M. Virvou Modelling the student to individualise tutoring in a web-based ICALL International Journal of Continuing Engineering Education and Lifelong Learning 13 3-4 2003 350 365
142. Tsiriga, V., & Virvou, M. (2003b). Evaluation of an intelligent web-based language tutor. In Proceedings of the 7th international conference on knowledge-based intelligent information and engineering systems (KES 2003) (pp. 275-281). Oxford, UK.
143. Tsiriga, V., & Virvou, M. (2003c). Initializing student models in web-based ITSs: A generic approach. In Proceedings of the 3rd international conference on advanced learning technologies (ICALT 2003) (pp. 42-46). Athens, Greece.
144. Vélez, J., Fabregat, R., Nassiff, S., Petro, J., & Fernandez, A. (2008). User integral model in adaptive virtual learning environment. In Proceedings of World conference on e-learning in corporate, government, healthcare, and higher education (pp. 3275-3284). Las Vegas, Nevada, United States.
145. R.M. Viccari, C.D. Flores, L. Seixas, J.C. Gluz, and H. Coelho AMPLIA: A probabilistic learning environment International Journal of Artificial Intelligence in Education 18 4 2008 347 373
146. Virvou, M., & Kabassi, K. (2002). F-SMILE: An intelligent multi-Agent learning environment. In Proceedings of IEEE international conference on advanced learning technologies 2002 (ICALT'02) (pp. 144-149).
147. M. Virvou, G. Katsionis, and K. Manos Combining software games with education: Evaluation of its educational effectiveness Educational Technology and Society 8 2 2005 54 65
148. Wang, X., Yang, Y., & Wen, X. (2009). Study on blended learning approach for English teaching. In Proceedings of the 2009 IEEE international conference on systems, man, and cybernetics (pp. 4641-4644).
149. G. Webb Preface to UMUAI special issue on machine learning for user modeling User Modeling and User-Adapted Interaction 8 1998 1 3
150. G. Webb, M. Pazzani, and D. Billsus Machine learning for user modeling User Modeling and User-Adapted Interaction 11 2001 19 29
151. M. Welch, and K. Brownell The development and evaluation of a multimedia course on educational collaboration Journal of Educational Multimedia and Hypermedia 9 3 2000 169 194
152. Weerasinghe, A., & Mitrovic, A. (2011). Facilitating adaptive tutorial dialogues in EER-tutor. In Proceedings of the 15th international conference on artificial intelligence in education (pp. 630-631). Auckland, New Zealand.
153. Winter, M., Brooks, C., & Greer, J. (2005). Towards best practices for semantic web student modeling. In Proceedings of the 12th international conference on artificial intelligence in education (pp. 694-701). Amsterdam, the Netherlands.
154. Xu, D., Wang, H., & Su, K. (2002). Intelligent student profiling with fuzzy models. In Proceedings of the 35th Hawaii international conference on system sciences.
155. Yang, G., Kinshuk, K., & Graf, S. (2010). A practical student model for a location-Aware and context-sensitive personalized adaptive learning system. In Proceedings of the IEEE technology for education conference (pp. 130-133). Bombay, India.
156. L.A. Zadeh Fuzzy sets Information and Control 8 3 1965 338 353
157. L.A. Zadeh Fuzzy logic=computing with words IEEE Transactions on Fuzzy Systems 4 2 1996 103 111
158. Zapata-Rivera, D. (2007). Indirectly visible Bayesian student models. In Proceedings of the 5th UAI Bayesian modelling applications workshop. Vancouver, BC, Canada.
159. X.M. Zhang, and H. Han An empirical testing of user stereotypes of informational retrieval systems Information Processing & Management 41 3 2005 651 664