Fuzzy-logic based learning style prediction in e-learning using web interface information

Sadhana - Academy Proceedings in Engineering Sciences

Volumn 40, Issue 2, 2015, Pages 379-394

  DEBORAH, L J E G A T H A ^a   SATHIYASEELAN, R ^b   AUDITHAN, S ^c   VIJAYAKUMAR, P ^a  

^a University College of Engineering   (India)

^b ANNA UNIVERSITY   (India)

^c PRIST UNIVERSITY   (India)

Author keywords

e contents; E learning; Felder Silverman learning style model; fuzzy rules; learning styles

Indexed keywords

C (PROGRAMMING LANGUAGE); E-LEARNING; FORECASTING; FUZZY LOGIC; FUZZY RULES; MEMBERSHIP FUNCTIONS; SOCIAL NETWORKING (ONLINE);

E-CONTENT; E-LEARNERS; GAUSSIAN MEMBERSHIP FUNCTION; LEARNING PATTERNS; LEARNING STYLE; WEB ENVIRONMENT; WEB INTERFACE;

LEARNING SYSTEMS;

EID: 84929837176     PISSN: 02562499     EISSN: 09737677     Source Type: Journal    
DOI: 10.1007/s12046-015-0334-1     Document Type: Article

References (52)

1. Acampora G, Cadenas J M, Loia V, and Ballester E M 2011 A multi-agent memetic system for human-based knowledge selection. IEEE Trans Syst. Man and Cybernetics, Part A: Systems and Humans, 41(5)
2. Almohammadi K and Hagras H 2013 An interval Type-2 fuzzy logic based system for customised knowledge delivery within pervasive e-learning platforms. In: Proceedings of IEEE international conference on systems, man, and cybernetics (SMC), 2872–2879
3. Anozie N and Junker B W 2006 Predicting end-of-year accountability assessment scores from monthly student records in an online tutoring system. In: Proceedings of AAAI workshop on educational data mining, Menlo Park, 1–6
4. Antunes C 2008 Acquiring background knowledge for intelligent tutoring systems. In: Proceedings of international conference on educational data mining, Montreal, 18–27
5. Avouris N, Komis V, Fiotakis G, Margaritis M, and Voyiatzaki E 2005 Why logging of fingertip actions is not enough for analysis of learning activities. In: Proceedings of AIED conference on workshop usage analysis learning system, Amsterdam, 1–8
6. Ayers E, Nugent R, and Dean N 2009 A comparison of student skill knowledge estimates. In: Proceedings of international conference on educational data mining, Cordoba, 1–10
7. Ba-Omar H, Petrounias I, and Anwar F 2007 A framework for using web usage mining for personalize E-learning. In: Proceedings of international conference on advanced learning technologies, Niigata, 937–938
8. Baker R, Corbett A, and Koedinger K 2004 Detecting student misuse of intelligent tutoring systems. In: Proceedings of international conference on intelligence tutoring system, Alagoas, 531–540
9. Barnes T 2005 The q-matrix method: Mining student response data for knowledge. In: Proceedings of AAAI workshop educational data mining, Pittsburgh, 1–8
10. Beck J E, Baker R, Corbett A T, Kay J, Litman D J, Mitrovic T, and Ritter S 2004 Analyzing student tutor interaction logs improve educational outcomes. Presented at the 7th international conference on workshop, Alagoas
11. Bergasa-Suso J, Sanders D A, and Tewkesbury G E 2005 Intelligent browser-based systems to assist Internet users. IEEE Trans. Educ., 48(4): 580–585
12. Butler K 1986 A learning and teaching style in theory and practice. Columbia: Learner’s Dimension
13. Carver C A, Howard R A, and Lane W D 1999 Enhancing student learning through hypermedia courseware and incorporation of student learning styles. IEEE Trans. Educ., 42(1): 33–38
14. Cornwell J M and Manfredo P A 1994 Kolb learning style theory revisited. Educ. Psychol. Meas., 54(2): 317–327
15. Chen Y and Weng C 2009 Mining fuzzy association rules from questionnaire data. Knowl. Based Syst. J., 22(1): 46–56
16. Chen S -M and Chang Y -C 2011 Weighted fuzzy rule interpolation based on GA-based weight-learning techniques. IEEE Trans. Fuzzy Syst., 19(4): 729–744
17. Chen C, Duh L, and Liu C 2004 A personalized courseware recommendation system based on fuzzy item response theory. In: Proceedings of IEEE international conference on E-Technology, E-Commerce, E-Service, Washington, 305–308
18. Chen G, Liu C, Ou K, and Liu B 2000 Discovering decision knowledge from web log portfolio for managing classroom processes by applying decision tree and data cube technology. J. Educ. Comput. Res., 23(3): 305–332
19. Dekker G W, Pechenizkiy M, and Vleeshouwers J M 2009 Predicting students drop out: A case study. In: Proceedings of international conference on educational data mining, Cordoba, 41–50
20. Dias S B and Diniz J A 2013 FuzzyQoI Model: A fuzzy logic-based modelling of users’ quality of interaction with a learning management system under blended learning. Elsevier COMPUT EDUC, 69: 38–59
21. Dunn R 1990 Understanding the Dunn and Dunn learning style model and the need for individual diagnosis and prescription. Read Write Learn Disabil., 6: 223–247
22. Dunn R and Dunn K 1989 Learning style inventory. Lawrence: Price Systems
23. Dupre M. J and Tipler F. T 2009 New axioms for bayesian probability. International Society for Bayesian Analysis, 3: 599–606
24. Elhag M F and Ahmed A H A 2013 Intelligent system for selecting Optimum Instructional style(s) based on fuzzy logic to develop a courseware (ISSIDC). In: Proceedings of international conference on computing, electrical and electronics engineering (ICCEEE), 518–524
25. Felder R M and Silverman L K 1988 Learning styles and teaching styles in engineering education. Eng. Educ., 78(7): 674–681
26. Fleming N D 2001 Teaching and learning styles: VARK strategies. N.D. Fleming Christchurch
27. Garcia-Valverde T, Garcia-Sola A, Gomez-Skarmeta A, and Botia J A 2012 An adaptive learning fuzzy logic system for indoor localisation using Wi-Fi in Ambient Intelligent Environments. In: Proceedings of IEEE international conference on fuzzy systems (FUZZ-IEEE), 1–8
28. Gardner H 1999 Intelligence reframed: Multiple intelligences for the 21st century. New York: Basic Books
29. Gilbert J E and Han C Y 1999 Adapting instruction in search of a significant difference. J. Netw. Comput. Appl., 22(3): 149–160
30. Gregorc A F 1985 Inside styles: Beyond the basics. Maynard: Gabriel sys
31. Gregorc A F and Ward H 1977 BA new definition for individual: Implications for learning and teaching. NASSP Bull., 401(6): 20–23
32. Guennoun K and Drira K 2006 Using graph grammars for interaction style description: Applications for service-oriented architectures. IJCSSE, 21(2)
33. Honey P and Mumford 2000 The learning styles helper’s guide. Maidenhead: Peter Honey Publications Ltd
34. Jackson C 2002 Manual of the learning styles profiler. Available at http://www.cymeon.com
35. Jegatha Deborah L, Baskaran R and Kannan A 2014 Learning Styles assessment and theoretical origin in an E-learning scenario: a survey. Artif. Intell. Rev 42(4): 801–819
36. Jegatha Deborah L, Baskaran R, and Kannan A 2011a Construction of ontology using computational linguistics for E-learning. In: Proceedings of the 2nd international conference on visual informatics, Kualalumpur. Springer—Lecture Notes in Computer Science, pp 50–63
37. Jegatha Deborah L, Karthika V, Baskaran R, and Kannan A 2011b Deontic based ontology matching for conflict resolution between text documents. In: Proceedings of the international conference on information processing, Bangalore. Springer—Communications in Computer and Information Science, 526–535
38. Kakoty S, Lal M, and Sarma S K 2012 Fuzzy expert logic to evaluate learner’s expertise level in e-learning environment. In: Proceedings of IEEE international conference on advanced communication control and computing technologies (ICACCCT), 148–152
39. Kolb A Y and Kolb D A 2005 Learning styles and learning spaces: enhancing Experiential learning in higher education. Acad. Manag. Learn Educ., 4(2): 193–212
40. Lu F, Li X, Liu Q, Yang Z, Tan G, and He T 2007 Research on personalized E-learning system using fuzzy set based clustering algorithm. In: Proceedings of international conference on computer science, Beijing, 587–590
41. McCaulley M H 2000 Myers–Briggs type indicator: A bridge between counseling and consulting. Consult. Psychol. J. Practice Res., 52: 117–132
42. Nebot A, Castro F, Vellido A, and Mugica F 2006 Identification of fuzzy models to predict students performance in an E-learning environment. In: Proceedings of international conference on web-based education, Puerto Vallarta, 74–79
43. Papanikolaou K A, Grigoriadou M, Magoulas G D, and Kornilakis H 2002 Towards newforms of knowledge communication: The adaptive dimension of a Web-based learning environment. Comput. Educ., 39 (4): 333–360
44. Paredes P and Rodriguez P 2002 Considering sensing-intuitive dimension to exposition-exemplification in adaptive sequencing. In: Proceedings AH2002 Conf. 556–559
45. Pittenger D J 2005 Cautionary comments regarding the Myers–Briggs type indicator. Consult. Psychol. J. Practice Res., 57(3): 210–221
46. Sanders D A 1993 System specification 2. Microprocess. Microprogram., 38(1–5): 833
47. Sanders D A and Bergasa-Suso J 2010 Inferring learning style from the way students interact with a computer user interface and the WWW. IEEE Trans. Educ., 53(4): 613–620
48. Swati Chaudhari and Manoj Patil 2014 Study and review of fuzzy inference systems for decision making and control. AIJRSTEM 5(1): 88–92
49. Tang Z., Er M. J., and Qi F. 2011 Dynamic fuzzy neural network for the intelligent control of a humanoid robot. IJCSSE, 26(3)
50. Triantafillou E, Pomportsis A, and Georgiadou E 2002 AES-CS: Adaptive educational system base on cognitive styles. In: Proceedings AH2002 Workshop, 10–20
51. Wilges B, Mateus G P, Nassar S M and Bastos R C 2012 Integration of BDI agent with fuzzy logic in a virtual learning environment. IEEE Trans. Latin Am. (Revista IEEE America Latina), 10(1)
52. Yang T-C, Hwang G-J and Yang S J-H 2013 Development of an adaptive learning system with multiple perspectives based on students’ learning styles and cognitive styles. Educational Technology & Society 16(4): 185–200