• Amit Kumar, M.tech School of Computer Science and Engineering, University of Petroleum and Energy Studies, Dehradun, Uttarakhand
  • Ninni Singh, M.tech School of Computer Science and Engineering, University of Petroleum and Energy Studies, Dehradun, Uttarakhand
  • Neelu Jyothi Ahuja, Dr. School of Computer Science and Engineering, University of Petroleum and Energy Studies, Dehradun, Uttarakhand


Actualizing instructional intercessions to suit learner contrasts has gotten extensive consideration. Among these individual contrast factors, the observational confirmation in regards to the academic benefit of learning styles has been addressed, yet the examination on the issue proceeds. Late improvements in web-based executions have driven researchers to re-examine the learning styles in adaptive tutoring frameworks. Adaptivity in intelligent tutoring systems is strongly influenced by the learning style of a learner. This study involved extensive document analysis of adaptive tutoring systems based on learning styles. Seventy-eight studies in literature from 2001 to 2016 were collected and classified under select parameters such as main focus, purpose, research types, methods, types and levels of participants, field/area of application, learner modelling, data gathering tools used and research findings. The current studies reveal that majority of the studies defined a framework or architecture of adaptive intelligent tutoring system (AITS) while others focused on impact of AITS on learner satisfaction and academic outcomes. Currents trends, gaps in literature and ications were discussed.


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Adetunji, A., & Ademola, A. (2014). A Proposed Architectural Model for an Automatic Adaptive E-Learning System Based on Users Learning Style. (IJACSA) International Journal of Advanced Computer Science and Applications, 5(4). DOI: 10.14569/IJACSA.2014.050401

Aleven, V., Mclaren, B., Roll, I., & Koedinger, K. (2006). Toward meta-cognitive tutoring: A model of help seeking with a Cognitive Tutor. International Journal of Artificial Intelligence in Education, 16(2), 101-128. https://dl.acm.org/citation.cfm?id=1435346

Alfonseca, E., Carro, R. M., Martín, E., Ortigosa, A., & Paredes, P. (2006). The impact of learning styles on student grouping for collaborative learning: a case study. User Modeling and User-Adapted Interaction, 16(3), 377-401. https://doi.org/10.1007/s11257-006-9012-7

Alkhuraiji, S., Cheetham, B., & Bamasak, O. (2011, July). Dynamic adaptive mechanism in learning management system based on learning styles. In Advanced Learning Technologies (ICALT), 2011 11th IEEE International Conference on (pp. 215-217). IEEE. https://doi.org/10.1109/ICALT.2011.69

Anthony, P., Joseph, N. E., & Ligadu, C. (2013). Learning how to program in c using adaptive hypermedia system. International Journal of Information and Education Technology, 3(2), 151. DOI: 10.7763/IJIET.2013.V3.254

Akkoyunlu, B., & Yilmaz-Soylu, M. (2008). A study of student’s perceptions in a blended learning environment based on different learning styles. Educational Technology & Society, 11(1), 183-193. https://www.learntechlib.org/p/75024/

Alepis, E., Virvou, M., & Kabassi, K. (2008, November). Mobile education: Towards affective bi-modal interaction for adaptivity. In Digital Information Management, 2008. ICDIM 2008. Third International Conference on (pp. 51-56). IEEE. DOI: 10.1109/ICDIM.2008.4746737

Ary, D., Jacobs, L. C., Irvine, C. K. S., & Walker, D. (2013). Introduction to research in education. Cengage Learning. https://www.goodreads.com/book/show/3528730-introduction-to-research-in-education

Aslan, B. G., Öztürk, Ö., & Inceoglu, M. M. (2014). Effect of Bayesian Student Modeling on Academic Achievement in Foreign Language Teaching (University Level English Preparatory School Example). Educational Sciences: Theory and Practice, 14(3), 1160-1168. https://eric.ed.gov/?id=EJ1034095

Baldiris, S., Santos, O. C., Barrera, C., Boticario, J., Velez, J., & Fabregat, R. (2008). Integration of educational specifications and standards to support adaptive learning scenarios in ADAPTAPlan. IJCSA, 5(1), 88-107. http://www.tmrfindia.org/ijcsa/V5I16.pdf

Balasubramanian, V., & Anouncia, S. M. (2016). Learning style detection based on cognitive skills to support adaptive learning environment–A reinforcement approach. Ain Shams Engineering Journal. https://doi.org/10.1016/j.asej.2016.04.012

Beal, C., & Lee, H. (2005, July). Creating a pedagogical model that uses student self-reports of motivation and mood to adapt ITS instruction. In Workshop on Motivation and Affect in Educational Software, in conjunction with the 12th International Conference on Artificial Intelligence in Education (Vol. 574). https://www.researchgate.net/publication/26621956_Integration_of_Educational_Specifications_and_Standards_to_Support_Adaptive_Learning_Scenarios_in_ADAPTAPlan

Bozkurt, O., & Aydoğdu, M. (2009). A comparative analysis of the effect of dunn and dunn learning styles model and traditional teaching method on 6th grade students’ achievement levels and attitudes in science education lesson. Elementary Education Online, 8(3), 741-754. https://www.academia.edu/4805395/A_Comparative_Analysis_of_the_Effect_of_Dunn_and_Dunn_Learning_Styles_Model_and_Traditional_Teaching_Method_on_6th_Grade_Students_Achievement_Levels_and_Attitudes_in_Science_Education_Lesson

Botsios, S., Georgiou, D., & Safouris, N. (2008). Contributions to adaptive educational hypermedia systems via on-line learning style estimation. Journal of Educational Technology & Society, 11(2). http://www.ifets.info/journals/11_2/23.pdf

Cabada, R. Z., Estrada, M. L. B., & García, C. A. R. (2011). EDUCA: A web 2.0 authoring tool for developing adaptive and intelligent tutoring systems using a Kohonen network. Expert Systems with Applications, 38(8), 9522-9529. https://doi.org/10.1016/j.eswa.2011.01.145

Cabada, R., Estrada, M., Sanchez, L., Sandoval, G., Velazquez, J., & Barrientos, J. (2009). Modeling student’s learning styles in web 2.0 learning systems. World Journal on Educational Technology, 1(2), 75-88. https://doi.org/10.1007/978-3-642-05258-3_45

Carmona, C., Castillo, G., & Millán, E. (2008, July). Designing a dynamic bayesian network for modeling students’ learning styles. In Advanced Learning Technologies, 2008. ICALT’08. Eighth IEEE International Conference on (pp. 346-350). IEEE. http://doi.ieeecomputersociety.org/10.1109/ICALT.2008.116

Conati, C., Gertner, A., & Vanlehn, K. (2002). Using Bayesian networks to manage uncertainty in student modeling. User modeling and user-adapted interaction, 12(4), 371-417. https://doi.org/10.1023/A:1021258506583

Cha, H. J., Kim, Y. S., Park, S. H., Yoon, T. B., Jung, Y. M., & Lee, J. H. (2006, June). Learning styles diagnosis based on user interface behaviors for the customization of learning interfaces in an intelligent tutoring system. In International Conference on Intelligent Tutoring Systems (pp. 513-524). Springer, Berlin, Heidelberg. https://doi.org/10.1007/11774303_51

Chang, Y. C., Kao, W. Y., Chu, C. P., & Chiu, C. H. (2009). A learning style classification mechanism for e-learning. Computers & Education, 53(2), 273-285. https://doi.org/10.1016/j.compedu.2009.02.008

Chrysafiadi, K., & Virvou, M. (2012). Evaluating the integration of fuzzy logic into the student model of a web-based learning environment. Expert Systems with Applications, 39(18), 13127-13134. https://doi.org/10.1016/j.eswa.2012.05.089

Dagger, D., Wade, V., & Conlan, O. (2002). Towards a standards-based approach to e-learning personalization using reusable learning objects. In E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 210-217). Association for the Advancement of Computing in Education (AACE). https://www.scss.tcd.ie/Owen.Conlan/publications/eLearn2002_v1.24_Conlan.pdf

Demirbaş, O. O., & Demirkan, H. (2003). Focus on architectural design process through learning styles. Design Studies, 24(5), 437-456. https://doi.org/10.1016/S0142-694X(03)00013-9

Del Corso, D., Ovcin, E., & Morrone, G. (2005). A teacher friendly environment to foster learner-centered customization in the development of interactive educational packages. IEEE Transactions on Education, 48(4), 574-579. DOI: 10.1109/TE.2005.850709

De Moura, F. F., Franco, L. M., De Melo, S. L., & Fernandes, M. A. (2013, October). Development of learning styles and multiple intelligences through particle swarm optimization. In Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on (pp. 835-840). IEEE. DOI:10.1109/SMC.2013.148

Dorça, F. A., Lima, L. V., Fernandes, M. A., & Lopes, C. R. (2013). Automatic student modeling in adaptive educational systems through probabilistic learning style combinations: a qualitative comparison between two innovative stochastic approaches. Journal of the Brazilian Computer Society, 19(1), 43-58. DOI 10.1007/s13173-012-0078-2

Dwivedi, P., & Bharadwaj, K. K. (2013). Effective trust-aware e-learning recommender system based on learning styles and knowledge levels. Journal of Educational Technology & Society, 16(4), 201. http://www.ifets.info/journals/16_4/16.pdf

Essaid El Bachari, E. H. A., & El Adnani, M. (2011). E-LEARNING PERSONALIZATION BASED ON DYNAMIC LEARNERS’PREFERENCE. International Journal of Computer Science & Information Technology (IJCSIT), 3(3), 200–216. https://www.scribd.com/document/58614290/E-Learning-personalization-based-on-Dynamic-learners-preference

Essalmi, F., Ayed, L. J. B., Jemni, M., & Graf, S. (2010). A fully personalization strategy of E-learning scenarios. Computers in Human Behavior, 26(4), 581-591. DOI: 10.1016/j.chb.2009.12.010

Fazlollahtabar, H., & Mahdavi, I. (2009). User/tutor optimal learning path in e-learning using comprehensive neuro-fuzzy approach. Educational Research Review, 4(2), 142-155. https://doi.org/10.1016/j.edurev.2009.02.001

Felder, R. M., & Silverman, L. K. (1988). Learning and teaching styles in engineering education. Engineering education, 78(7), 674-681. http://www4.ncsu.edu/unity/lockers/users/f/felder/public/Papers/LS-1988.pdf

Felder, R. M., & Spurlin, J. (2005). Applications, reliability and validity of the index of learning styles. International journal of engineering education, 21(1), 103-112. https://www.ijee.ie/articles/Vol21-1/IJEE1553.pdf

Feldman, J., Monteserin, A., & Amandi, A. (2014). Detecting students’ perception style by using games. Computers & Education, 71, 14-22. https://doi.org/10.1016/j.compedu.2013.09.007

Filippidis, S. K., & Tsoukalas, I. A. (2009). On the use of adaptive instructional images based on the sequential–global dimension of the Felder–Silverman learning style theory. Interactive Learning Environments, 17(2), 135-150. http://dx.doi.org/10.1080/10494820701869524

Franzoni, A. L., Assar, S., Defude, B., & Rojas, J. (2008, July). Student learning styles adaptation method based on teaching strategies and electronic media. In Advanced Learning Technologies, 2008. ICALT’08. Eighth IEEE International Conference on (pp. 778-782). IEEE. DOI: 10.1109/ICALT.2008.149

García, P., Amandi, A., Schiaffino, S., & Campo, M. (2007). Evaluating Bayesian networks’ precision for detecting students’ learning styles. Computers & Education, 49(3), 794-808. https://doi.org/10.1016/j.compedu.2005.11.017

García, P., Schiaffino, S., & Amandi, A. (2008). An enhanced Bayesian model to detect students’ learning styles in Web‐based courses. Journal of Computer Assisted Learning, 24(4), 305-315. DOI: 10.1111/j.1365-2729.2007.00262.x

Germanakos, P., Tsianos, N., Lekkas, Z., Mourlas, C., & Samaras, G. (2008). Capturing essential intrinsic user behaviour values for the design of comprehensive web-based personalized environments. Computers in Human Behavior, 24(4), 1434-1451. DOI: 10.1016/j.chb.2007.07.010

Graf, S., & Liu, T. C. (2008, July). Identifying learning styles in learning management systems by using indications from students’ behaviour. In Advanced Learning Technologies, 2008. ICALT’08. Eighth IEEE International Conference on (pp. 482-486). IEEE. DOI: 10.1109/ICALT.2008.84

Graf, S., Liu, T. C., Chen, N. S., & Yang, S. J. (2009). Learning styles and cognitive traits–Their relationship and its benefits in web-based educational systems. Computers in Human Behavior, 25(6), 1280-1289. https://doi.org/10.1016/j.chb.2009.06.005

Graf, S., & Liu, T. C. (2010). Analysis of learners’ navigational behaviour and their learning styles in an online course. Journal of Computer Assisted Learning, 26(2), 116-131. doi: 10.1111/j.1365-2729.2009.00336.x

Hong, H. (2004). Adaptation to student learning styles in web based educational systems. In EdMedia: World Conference on Educational Media and Technology (pp. 491-496). Association for the Advancement of Computing in Education (AACE). https://www.learntechlib.org/p/12978/

Huang, E. Y., Lin, S. W., & Huang, T. K. (2012). What type of learning style leads to online participation in the mixed-mode e-learning environment? A study of software usage instruction. Computers & Education, 58(1), 338-349. https://doi.org/10.1016/j.compedu.2011.08.003

Hwang, G. J., & Tsai, C. C. (2011). Research trends in mobile and ubiquitous learning: A review of publications in selected journals from 2001 to 2010. British Journal of Educational Technology, 42(4). DOI: 10.1111/j.1467-8535.2011.01183.x

Hwang, G. J., Sung, H. Y., Hung, C. M., & Huang, I. (2013). A Learning Style Perspective to Investigate the Necessity of Developing Adaptive Learning Systems. Educational Technology & Society, 16(2), 188-197. https://eric.ed.gov/?id=EJ1016557

James, W. B., & Blank, W. E. (1993). Review and critique of available learning‐style instruments for adults. New Directions for Adult and Continuing Education, 1993(59), 47-57. https://eric.ed.gov/?id=EJ472130

Jonassen, D. H., & Grabowski, B. L. (2012). Handbook of individual differences, learning, and instruction. Routledge. https://doi.org/10.1016/0022-4405(95)00013-C

Jovanović, J., Gašević, D., & Devedžić, V. (2009). TANGRAM for personalized learning using the semantic web technologies. Journal of emerging technologies in web intelligence, 1(1), 6-21. DOI: 10.4304/jetwi.1.1.6-21

Kelly, D., & Tangney, B. (2005, July). ‘First Aid for You’: getting to know your learning style using machine learning. In Advanced Learning Technologies, 2005. ICALT 2005. Fifth IEEE International Conference on (pp. 1-3). IEEE. DOI: 10.1109/ICALT.2005.1

Kelly, D. (2008). Adaptive versus learner control in a multiple intelligence learning environment. Journal of Educational Multimedia and Hypermedia, 17(3), 307. https://www.learntechlib.org/p/24252/

Ketamo, H. (2003). An adaptive geometry game for handheld devices. Educational Technology & Society, 6(1), 83-95. http://www.ifets.info/journals/6_1/ketamo.html

Kim, J., Lee, A., & Ryu, H. (2013). Personality and its effects on learning performance: Design guidelines for an adaptive e-learning system based on a user model. International Journal of Industrial Ergonomics, 43(5), 450-461. https://doi.org/10.1016/j.ergon.2013.03.001

Klašnja-Milićević, A., Vesin, B., Ivanović, M., & Budimac, Z. (2011). E-Learning personalization based on hybrid recommendation strategy and learning style identification. Computers & Education, 56(3), 885-899. https://doi.org/10.1016/j.compedu.2010.11.001

Koutsojannis, C., Prentzas, J., & Hatzilygeroudis, I. (2001). A web-based intelligent tutoring system teaching nursing students fundamental aspects of biomedical technology. In Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE (Vol. 4, pp. 4024-4027). IEEE. DOI: 10.1109/IEMBS.2001.1019728

Kurilovas, E., Kubilinskiene, S., & Dagiene, V. (2014). Web 3.0–Based personalisation of learning objects in virtual learning environments. Computers in Human Behavior, 30, 654-662. https://doi.org/10.1016/j.chb.2013.07.039

Kuljis, J., & Liu, F. (2005). A Comparison of Learning Style Theories on the Suitability for e-learning. Web Technologies, Applications, and Services, 2005, 191-197. http://www.actapress.com/PaperInfo.aspx?PaperID=21202&reason=500

Latham, A., Crockett, K., McLean, D., & Edmonds, B. (2012). A conversational intelligent tutoring system to automatically predict learning styles. Computers & Education, 59(1), 95-109. https://doi.org/10.1016/j.compedu.2011.11.001

Latham, A., Crockett, K., & McLean, D. (2014). An adaptation algorithm for an intelligent natural language tutoring system. Computers & Education, 71, 97-110. https://doi.org/10.1016/j.compedu.2013.09.014

Liegle, J. O., & Janicki, T. N. (2006). The effect of learning styles on the navigation needs of Web-based learners. Computers in human behavior, 22(5), 885-898. https://www.researchgate.net/profile/Jens_Liegle/publication/201381995_Effect_of_Learning_Styles_on_the_Navigational_Needs_of_Computer-Based_Training_Module_Learners/links/5411bfa70cf264cee28b5412.pdf

Limongelli, C., Sciarrone, F., Temperini, M., & Vaste, G. (2011). The lecomps5 framework for personalized web-based learning: a teacher’s satisfaction perspective. Computers in Human Behavior, 27(4), 1310-1320. https://doi.org/10.1016/j.chb.2010.07.026

Lin, C. F., Yeh, Y. C., Hung, Y. H., & Chang, R. I. (2013). Data mining for providing a personalized learning path in creativity: An application of decision trees. Computers & Education, 68, 199-210. https://doi.org/10.1016/j.compedu.2013.05.009

Litzinger, T. A., Wise, J. C., & Lee, S. H. (2005). Self‐directed Learning Readiness Among Engineering Undergraduate Students. Journal of Engineering Education, 94(2), 215-221. https://doi.org/10.1002/j.2168-9830.2005.tb00842.x

Lu, H., Jia, L., Gong, S. H., & Clark, B. (2007). The relationship of Kolb learning styles, online learning behaviors and learning outcomes. Journal of Educational Technology & Society, 10(4). http://www.ifets.info/journals/10_4/17.pdf

Mahnane, L., Laskri, M. T., & Trigano, P. (2013). A model of adaptive e-learning hypermedia system based on thinking and learning styles. International Journal of Multimedia and Ubiquitous Engineering, 8(3), 339-350. https://doi.org/10.4018/ijicte.2013100102

Manochehr, N. N. (2006). The influence of learning styles on learners in e-learning environments: An empirical study. Computers in Higher Education Economics Review, 18(1), 10-14. http://www.economicsnetwork.ac.uk/cheer/ch18/manochehr.pdf

Mcquiggan, S. W., Mott, B. W., & Lester, J. C. (2008). Modeling self-efficacy in intelligent tutoring systems: An inductive approach. User modeling and user-adapted interaction, 18(1), 81-123. https://doi.org/10.1007/s11257-007-9040-y

Melis, E., & Siekmann, J. (2004, June). Activemath: An intelligent tutoring system for mathematics. In ICAISC (pp. 91-101). https://doi.org/10.1007/b98109

Mitrovic, A., Martin, B., & Mayo, M. (2002). Using evaluation to shape ITS design: Results and experiences with SQL-Tutor. User Modeling and User-Adapted Interaction, 12(2), 243-279. https://doi.org/10.1023/A:1015022619307

Mitrovic, A., Koedinger, K., & Martin, B. (2003). A comparative analysis of cognitive tutoring and constraint-based modeling. User Modeling 2003, 147-147. https://doi.org/10.1007/3-540-44963-9_42

Mödritscher, F. (2008). Adaptive e-learning environments: theory, practice, and experience. VDM, Müller.

Mustafa, Y. E. A., & Sharif, S. M. (2011). An approach to adaptive e-learning hypermedia system based on learning styles (AEHS-LS): Implementation and evaluation. International Journal of Library and Information Science, 3(1), 15-28. http://www.academicjournals.org/journal/IJLIS/article-abstract/75161B52666

Özyurt, Ö., & Özyurt, H. (2015). Learning style based individualized adaptive e-learning environments: Content analysis of the articles published from 2005 to 2014. Computers in Human Behavior, 52, 349-358. https://doi.org/10.1016/j.chb.2015.06.020

Özpolat, E., & Akar, G. B. (2009). Automatic detection of learning styles for an e-learning system. Computers & Education, 53(2), 355-367. https://doi.org/10.1016/j.compedu.2009.02.018

Özyurt, Ö., Özyurt, H., Baki, A., & Güven, B. (2013). Integration into mathematics classrooms of an adaptive and intelligent individualized e-learning environment: Implementation and evaluation of UZWEBMAT. Computers in Human Behavior, 29(3), 726-738. https://doi.org/10.1016/j.chb.2012.11.013

Papanikolaou, K. A., Grigoriadou, M., Kornilakis, H., & Magoulas, G. D. (2003). Personalizing the Interaction in a Web-based Educational Hypermedia System: the case of INSPIRE. User modeling and user-adapted interaction, 13(3), 213-267. https://doi.org/10.1023/A:1024746731130

Park, O. C., & Lee, J. (2003). Adaptive instructional systems. Educational Technology Research and Development, 25, 651-684. http://www.aect.org/edtech/ed1/22/index.html

Ray, R. D., & Belden, N. (2007). Teaching college level content and reading comprehension skills simultaneously via an artificially intelligent adaptive computerized instructional system. The Psychological Record, 57(2), 201. https://doi.org/10.1007/BF03395572

Read, T., Barros, B., Bárcena, E., & Pancorbo, J. (2006). Coalescing individual and collaborative learning to model user linguistic competences. User Modeling and User-Adapted Interaction, 16(3), 349-376. https://doi.org/10.1007/s11257-006-9014-5

Reategui, E., Boff, E., & Campbell, J. A. (2008). Personalization in an interactive learning environment through a virtual character. Computers & Education, 51(2), 530-544. https://doi.org/10.1016/j.compedu.2007.05.018

Romero, C., Ventura, S., Gibaja, E. L., Hervás, C., & Romero, F. (2006). Web-based adaptive training simulator system for cardiac life support. Artificial Intelligence in Medicine, 38(1), 67-78. https://doi.org/10.1016/j.artmed.2006.01.002

Sancho, P., Martínez, I., & Fernández-Manjón, B. (2005). Semantic Web Technologies Applied to e-learning Personalization in< e-aula>. Journal of Universal Computer Science, 11(9), 1470-1481. http://dx.doi.org/10.3217/jucs-011-09-1470

Sanders, D. A., & Bergasa-Suso, J. (2010). Inferring learning style from the way students interact with a computer user interface and the WWW. IEEE Transactions on Education, 53(4), 613-620. https://doi.org/10.1109/TE.2009.2038611

Sangineto, E., Capuano, N., Gaeta, M., & Micarelli, A. (2008). Adaptive course generation through learning styles representation. Universal Access in the Information Society, 7(1-2), 1-23. https://doi.org/10.1007/s10209-007-0101-0

Schiaffino, S., Garcia, P., & Amandi, A. (2008). eTeacher: Providing personalized assistance to e-learning students. Computers & Education, 51(4), 1744-1754. https://doi.org/10.1016/j.compedu.2008.05.008

Scott, E., Rodríguez, G., Soria, Á., & Campo, M. (2014). Are learning styles useful indicators to discover how students use Scrum for the first time? Computers in Human Behavior, 36, 56-64. https://doi.org/10.1016/j.chb.2014.03.027

Shute, V., & Towle, B. (2003). Adaptive e-learning. Educational psychologist, 38(2), 105-114. http://dx.doi.org/10.1207/S15326985EP3802_5

Shih, M., Feng, J., & Tsai, C. C. (2008). Research and trends in the field of e-learning from 2001 to 2005: A content analysis of cognitive studies in selected journals. Computers & Education, 51(2), 955-967. https://doi.org/10.1016/j.compedu.2007.10.004

Stash, N. (2007). Incorporating cognitive/learning styles in a general-purpose adaptive hypermedia system. Dissertation Abstracts International, 68(04). https://doi.org/10.1145/1324960.1324963

Sun, S., Joy, M., & Griffiths, N. (2007). The use of learning objects and learning styles in a multi-agent education system. Journal of Interactive Learning Research, 18(3), 381. http://www.dcs.warwick.ac.uk/~nathan/resources/Publications/edmedia-2005.pdf

Tseng, J. C., Chu, H. C., Hwang, G. J., & Tsai, C. C. (2008). Development of an adaptive learning system with two sources of personalization information. Computers & Education, 51(2), 776-786. https://doi.org/10.1016/j.compedu.2007.08.002

Van Zwanenberg, N., Wilkinson, L. J., & Anderson, A. (2000). Felder and Silverman’s Index of Learning Styles and Honey and Mumford’s Learning Styles Questionnaire: how do they compare and do they predict academic performance? Educational Psychology, 20(3), 365-380. https://doi.org/10.1080/713663743

Vassileva, D., & Bontchev, B. (2006). Self-adaptive hypermedia navigation based on learner model characters. http://www.iadat.org/iadat-e2006/abstracts_web/IADAT-e2006_13.pdf

Vermunt, J. D. (1998). The regulation of constructive learning processes. British journal of educational psychology, 68(2), 149-171. DOI: 10.1111/j.2044-8279.1998.tb01281.x

Villaverde, J. E., Godoy, D., & Amandi, A. (2006). Learning styles’ recognition in e‐learning environments with feed‐forward neural networks. Journal of Computer Assisted Learning, 22(3), 197-206. DOI: 10.1111/j.1365-2729.2006.00169.x

Wang, T. I., Wang, K. T., & Huang, Y. M. (2008). Using a style-based ant colony system for adaptive learning. Expert Systems with Applications, 34(4), 2449-2464. https://doi.org/10.1016/j.eswa.2007.04.014

Wen, D., Graf, S., Lan, C. H., Anderson, T., & Dickson, K. (2007). Supporting web-based learning through adaptive assessment. FormaMente Journal, 2(1-2), 45-79. http://sgraf.athabascau.ca/publications/wen_graf_lan_anderson_kinshuk_dickson_FormaMenteJournal.pdf

Xu, D., Wang, H., & Su, K. (2002, January). Intelligent student profiling with fuzzy models. In System Sciences, 2002. HICSS. Proceedings of the 35th Annual Hawaii International Conference on (pp. 8-pp). IEEE. https://doi.org/10.1109/HICSS.2002.994005

Xu, D., & Wang, H. (2006). Intelligent agent supported personalization for virtual learning environments. Decision Support Systems, 42(2), 825-843. https://doi.org/10.1016/j.dss.2005.05.033

Yang, T. C., Hwang, G. J., & Yang, S. J. H. (2013). Development of an adaptive learning system with multiple perspectives based on students’ learning styles and cognitive styles. Journal of Educational Technology & Society, 16(4), 185. http://www.ifets.info/journals/16_4/15.pdf

Zakrzewska, D. (2010, June). Building group recommendations in e-learning systems. In KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications (pp. 391-400). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32066-8_7
How to Cite
KUMAR, Amit; SINGH, Ninni; AHUJA, Neelu Jyothi. LEARNING STYLES BASED ADAPTIVE INTELLIGENT TUTORING SYSTEMS: DOCUMENT ANALYSIS OF ARTICLES PUBLISHED BETWEEN 2001. AND 2016.. International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE), [S.l.], v. 5, n. 2, p. 83-98, dec. 2017. ISSN 2334-8496. Available at: <http://ijcrsee.com/index.php/IJCRSEE/article/view/168>. Date accessed: 19 jan. 2018. doi: https://doi.org/10.5937/IJCRSEE1702083K.