Learning style recognition based on an adjustable three-layer fuzzy cognitive map

• Autorzy: Georgiou D. A. , Botsios S. , Mitropoulou V. , Papaioannou M. , Schizas C. , Tsoulouhas G.
• Języki publikacji: EN

Abstrakty

EN

Identification of learning styles supports Adaptive Educational Hypermedia Systems compiling and presenting tutorials custom in cognitive characteristics of each individual learner. This work addresses the issue: identifying the learning style of students, following the Kolb’s learning cycle. To this purpose, we propose a three-layers Fuzzy Cognitive Map (FCM) in conjunction with a dynamic Hebbian rule for learning styles recognition. The form of FCMs is designed by humans who determine its weighted interconnections among concepts. But the human factor may not be as reliable as it should be. Thus, a FCM model of the system allowing the adjustment of its weights using additional learners’ characteristics such as the Learning Ability Factors. In this article, two consecutively interconnected FCM (in the form of a three layer FCM) are presented. The schema’s efficiency has been tested and compared to known results after a fine-tuning of the weights of the causal interconnections among concepts. The simulations results of training the process system verify the effectiveness, validity and advantageous characteristics of those learning techniques for FCMs. The online recognition of learning styles by using threelayer Fuzzy Cognitive Map improves the accuracy of recognition obtained using Bayesian Networks that uses quantitative measurements of learning style taken from statistical samples. This improvement is due to the fuzzy nature of qualitative characterizations (such as learning styles), and the presence of intermediate level nodes representing Learning Ability Factors. Such factors are easily recognizable characteristics of a learner to improve adjustment of weights in edges with one end in the middle-level nodes. This leads to the establishment of a more reliable model, as shown by the results given by the application to a test group of students.

Słowa kluczowe

EN

learning style; adaptive educational hypermedia systems; Kolb’s learning cycle; Fuzzy Cognitive Map; FCM; Learning Ability Factors; Bayesian networks; cognitive map; three-layer fuzzy

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2011
• Tom: Vol. 1, No. 4
• Strony: 333--347
• Opis fizyczny: Bibliogr. 52 poz., rys.

Twórcy

autor

Georgiou D. A.

• School of Engineering, Department of Electrical and Computer Engineering, Democritus University of Thrace, Greece

autor

Botsios S.

• School of Engineering, Department of Electrical and Computer Engineering, Democritus University of Thrace, Greece

autor

Mitropoulou V.

• Laboratory of Education, School of Theology, Aristostle University of Thessaloniki, Greece

autor

Papaioannou M.

• Department of Computer Science, University of Cyprus

autor

Schizas C.

• Department of Computer Science, University of Cyprus

autor

Tsoulouhas G.

• School of Engineering, Department of Electrical and Computer Engineering, Democritus University of Thrace, Greece

Bibliografia

• [1] ALIBracknell LEA November 2002. Inspection report. Coventry: Adult learning Inspectorate. Available via www.ali.gov.uk/ (2002a)
• [2] ALIBirmingham Institute of Education, Training and Technology. February 2003. Inspection report. Coventry: Adult Learning Inspectorate. Available via: www.ali.gov.uk/ (2003b).
• [3] Bajraktarevic N., W. Hall, & P. Fullick (2003) ILASH: Incorporating Learning Strategies in Hypermedia. In Fourteenth Conference on Hypertext and Hypermedia.
• [4] Berg S. A., & S. Youn. Factors that influence informal learning in the workplace. Journal ofWorkplace Learning 20, 2008
• [5] Botsios S., D. Georgiou, & N. Safouris, Contributions to AEHS via on-line Learning Style Estimation. Journal of Educational Technology and Society 11, 2008
• [6] Brown E. J., & T. Brailsford, Integration of learning style theory in an adaptive educational hypermedia (AEH) system. In ALT-C Conference, 2004
• [7] Brusilovsky P., Adaptive hypermedia. User Modelling and User-Adapted Interaction 11:(1-2).,2004
• [8] Carver C.A., Howard, R.A., Lavelle, E., Enhancing student learning by incorporating student learning styles into adaptive hypermedia Proceedings of ED-MEDIA’96 - World Conference on Educational Multimedia and Hypermedia, Boston, MA, pages 118–123, 1996
• [9] Carver Jr C. A., R. A. Howard, & W. D. Lane. (1999). Enhancing student learning through hypermedia courseware and incorporation of student learning styles. IEEE Transactions on Education 42 :33–38, 1999
• [10] Coffield F., Moseley D., Hall E., Ecclestone K., Should we be using learning styles? What recent research has to say to practice. London: Learning and Skills Research Centre, 2004
• [11] Danielson R.,Learning styles, media preferences, and adaptive education, Proceedings of Workshop ’Adaptive Systems and User Modeling on the World Wide Web’ at 6th International Conference on User Modeling, UM97, pages 31–35., 1997
• [12] Dickerson J. A., & Bart Kosko Virtual worlds as fuzzy cognitive maps. Paper read at 1993 IEEE Annual Virtual Reality International Symposium.
• [13] Dunn R.,Rita Dunn answers questions on learning styles. Educational Leadership, 48: 15–19,1990
• [14] Farnham-Diggory S., The Learning- Disabled Child, Developing Child. Cambridge, MA: Harvard University Press, 1992
• [15] Felder R. M., & L. K. Silverman,Learning and teaching styles in engineering education. Engineering Education 78:674–681, 1988
• [16] Flouris G. (1986). Architecture of Teaching and the procedure of learning. Athens: Gregory.
• [17] Georgiou D. A., & S. D. Botsios,Learning style recognition: A three layers fuzzy cognitive map schema. Proceedings of Fuzzy Systems,FUZZIEEE 2008. (IEEE World Congress on Computational Intelligence). IEEE International Conference on Fuzzy Systems. art. no. 4630675, pages 2202–2207
• [18] Georgopoulos V. C., G. A. Malandraki, & C. D. Stylios.A fuzzy cognitive map approach to differential diagnosis of specific language impairment. Artificial Intelligence in Medicine 29:261–278, 2003
• [19] Gilbert J.E., Han C.Y., Arthur:Adapting instruction to accommodate learning style Proceedings ofWebNet’99,World Conference of theWWWand Internet, pages 433–438. Honolulu, HI 1999
• [20] Graf S., & Kinshuk Considering Cognitive Traits and Learning Styles to Open Web-Based Learningto a Larger Student Community. In International Conference on Information and Communication Technology and Accessibility. Hammamet, Tunisia, 2007
• [21] Green M.Improving initial assessment in workbased perspective. Theory into Practice. 23, 51–55, 2002
• [22] Honey P., & A. Mumford The Manual of Learning Styles 3rd Ed. Maidenhead, Peter Honey, 1992
• [23] Honey P., & A. Mumford, The Learning Styles Helper’s Guide. Maidenhead: Peter Honey Publications, 2000
• [24] Huff A. S.Mapping strategic thought, New York: Wiley, 1990
• [25] Hussein B., & A. Ismael,Fuzzy neural network controller for a water desalination system. Proceedings of Artificial Neural Networks in Engineering (ANNIE’95):599–604, 1995
• [26] Joy S., Kolb D.A., Are There Cultural Differences in Learning Style? International Journal of Intercultural Relations, 33 (2009) 69-85
• [27] Jonassen D., Grabowski B.,Handbook of individual differences learning and instruction Lawrence Erlbaum Associates. 1992
• [28] Ogbu J. U., Understanding Cultural Diversity and Learning, Educational Researcher, 21, No 8., 5–14+24; 1992
• [29] Kerawalla L., S. Minocha, G. Kirkup, & G. Conole, An empirically grounded framework to guide blogging in higher education. Journal of Computer Assisted Learning 25:31–42, 2009
• [30] Kolb D. A., The Kolb Learning Style Inventory. Version 3. Boston: Hay Group, 1999
• [31] Kolb D. A.,Experimental learning: Experience as the source of learning and development. Jersey: Prentice Hall, 1984
• [32] Lee, Kun Chang, Jin Sung Kim, Nam Ho Chung,& Soon Jae Kwon, Fuzzy cognitive map approach to web-mining inference amplification. Expert Systems with Applications 22:197–211, 2002
• [33] Lee S., & I. Han. Fuzzy cognitive map for the design of EDI controls. Information and Management 37:37–50, 2000
• [34] Levy Y.The top 10 most valuable online learning activities for graduate MIS students. International Journal of Information and Communication Technology Education 2:27–44, 2006
• [35] Li S. J., & R. M. Shen Fuzzy cognitive map learning based on improved nonlinear hebbian rule. Paper read at Proceedings of 2004 International Conference on Machine Learning and Cybernetics, 2004,
• [36] Liu Z. Q., Causation, Bayesian networks, and cognitive maps Zidnghua Xubao / Acta Automatica Sinica, Volume 27, Issue 4, Pages 552-566, 2001
• [37] Loo R., Kolb’s Learning Styles and Learning Preferences: Is there a linkage? Educational Psychology 24:99–108, 2004
• [38] Loo R., & K. Thorpe, Confirmatory factor analyses of the full and short versions of the Marlowe-Crowne social desirability scale. Journal of Social Psychology 140:628–635, 2000
• [39] Manos K. Educational Psychology – Psychopedagogy. Athens: Gregory, 1993
• [40] Mitropoulou, V. Educational Software for the Teaching of Religion in Schools. Thessaloniki: Vanias, 2008
• [41] Moore A., T. J. Brailsford, & C. D. Stewart. Personally tailored teaching in WHURLE using conditional transclusion. In Proceedings of the ACM Conference on Hypertext, 2001
• [42] Osei-Bryson, K. M.Generating consistent subjective estimates of the magnitudes of causal relationships in fuzzy cognitive maps. Computers and Operations Research 31 (8):1165–1175, 2004.
• [43] Papanikolaou K. A., A. Mabbott, S. Bull, & M. Grigoriadou,Designing learner-controlled educational interactions based on learning/cognitive style and learner behaviour. Interacting with Computers 18:356–384,2006
• [44] Pipatsarun P., Jiracha V., Adaptive Intelligent Tutoring Systems for e-learning systems Procedia – Social and Behavioral Sciences, 2 (2010) p.p. 4064–4069, 2010
• [45] Reynolds M. Learning Styles: a critique. Management Learning. 28:115–133
• [46] Specht M., & R. Oppermann. ACE – adaptive courseware environment. New Review of Hypermedia and Multimedia 4:141–161.,1997–1998
• [47] Stake, R. Learning parameters, aptitudes and achievements. Princeton Univ., Psychol. Dept., 1958 (multilith)
• [48] Styblinski M. A., & B. D. Meyer. Fuzzy cognitive maps, signal flow graphs, and qualitative circuit analysis. International Conference on Neural Networks IEEE Issue 24-27 Vol. 2 page(s): 549–556,1988
• [49] Verpoorten D., M. Poumay, & D. Leclercq, The eight learning events model: A pedagogic conceptual tool supporting diversification of learning methods. Interactive Learning Environments 15:151–160, 2007.
• [50] Willcoxson L., & M. Prosser. 1996Kolb’s Learning Style Inventory 1985: Review and further study of validity and reliability. British Journal of Educational Psychology 66:247–257.
• [51] Wolf C., iWeaver: Towards ’Learning Style’-based e-Learning in Computer Science Education. In Proceedings of the Fifth Australasian Computing Education Conference on Computing Education 2003.
• [52] Yahya I., Willcoxson & Prosser’s factor analyses on Kolb’s (1985) LSI data: Reflections and reanalyses. British Journal of Educational Psychology 68:281–286, 1998