We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Fuzzy Logic based Assessment Model Proposal for Online Problem-based Learning

by Abdulkadir Karaci
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 117 - Number 9
Year of Publication: 2015
Authors: Abdulkadir Karaci
10.5120/20580-2998

Abdulkadir Karaci . Fuzzy Logic based Assessment Model Proposal for Online Problem-based Learning. International Journal of Computer Applications. 117, 9 ( May 2015), 5-8. DOI=10.5120/20580-2998

@article{ 10.5120/20580-2998,
author = { Abdulkadir Karaci },
title = { Fuzzy Logic based Assessment Model Proposal for Online Problem-based Learning },
journal = { International Journal of Computer Applications },
issue_date = { May 2015 },
volume = { 117 },
number = { 9 },
month = { May },
year = { 2015 },
issn = { 0975-8887 },
pages = { 5-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume117/number9/20580-2998/ },
doi = { 10.5120/20580-2998 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:58:53.207625+05:30
%A Abdulkadir Karaci
%T Fuzzy Logic based Assessment Model Proposal for Online Problem-based Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 117
%N 9
%P 5-8
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this research, problem based learning over web is suggested. This model includes fuzzy logic and MYCIN trust factor. Through the model, MYCIN trust factor and the number of attempts to solve the problem are used in order to identify students' learning levels. MYCIN confidence factor value and number of attempts to solve the problem are to be entered to fuzzy logic decision system. The output of the fuzzy logic decision system becomes the new level of learning. This level of learning is calculated through fuzzy logic in linguistic term as well as numeric expression. In every try, a hint given to student. Each used hint lowers the score of the student. Thus the students who solved the problem in one try will score more than the ones who solved the problem in more than one try.

References
  1. Hmelo-Silver, C. E. 2004. Problem-Based Learning: What and How Do Students Learn?, Educational Psychology Review,16(3), 235-266.
  2. Gürsula F. , Keser, H. 2009. The effects of online and face to face problem based learning environments in mathematics education on student's academic achievement, Procedia Social and Behavioral Sciences, 1 2817–2824.
  3. O?uz-Ünver, A. , Arabac?o?lu, S. 2011. Overviews On Inquiry Based And Problem Based Learning Methods, Western Anatolia Journal of Educational Sciences (WAJES), Special Issue: Selected papers presented at WCNTSE, 303-310,
  4. Ferreira, M. M. , Trudel, A. R. 2012. The Impact of Problem-Based Learning (PBL) on Student Attitudes Toward Science, Problem-Solving Skills, and Sense of Community in the Classroom, Journal of Classroom Interaction, 47. 1, 23-30.
  5. McParland, M. , Noble, L. M. , Livingston, G. 2004. The effectiveness of problem-based learning compared to traditional teaching in undergraduate psychiatry, Medical Education, 38(8), 589-867.
  6. Malopinsky, L. , Kirkley,J. , Stein, R. Duffy, T. 2000. An instructional design model for online problem based learning (pbl) environments: The learning to teach with technology studio. annual proceedings of selected research and development papers. Presented at the National Convention of the Association for Educational Communications and Technology (23rd, Denver, CO, October 25-28). Volumes 1-2. , 244-252.
  7. Morgado, S. , Leite, L. 2013. Science and Geography Teachers' Conceptions Regarding Problem-based Learning Related Concepts, Procedia - Social and Behavioral Sciences, 106, 2343–2347.
  8. Sockalingam, N. , Schmidt, H. G. 2013. Does the extent of problem familiarity in?uence students' learning in problem-based learning?, Instructional Science, 41(5), 921-932.
  9. Leppink, J. , Broers, N. J. , Imbos, T. , van der Vleuten, C. P. M. , Berger, M. P. F. , 2014. The Effect of Guidance in Problem-Based Learning of Statistics, The Journal Of Experimental Education, 82(3), 391–407.
  10. Gürsula, F. 2008. Çevrimiçi Ve Yüzyüze Problem Tabanli Ö?renme Yakla??mlar?n?n Ö?rencilerin Matemati?e Yönelik Tutumlarina Etkisi, Yüzüncü Y?l Üniversitesi, E?itim Fakültesi Dergisi, 5(1), 1-19.
  11. Karac?, A. , Ar?c?, N. 2012. An adaptive exam module based on fuzzy logic for intelligent tutorial systems, Energy Education Science and Technology Part B: Social and Educational Studies, Special Issue, 829-835.
  12. Kavcic A. 2004. Fuzzy user modeling for adaptation in educational hypermedia. IEEE Transact Systems Man Cybern Part C Appl Rev, 34:439–449.
  13. Baets B, Fodor J. 1999. Van Melle's combining function in MYCIN is a representable uninorm: An alternative proof. Fuzzy Sets Syst, 104:133–13.
  14. Dogan B. 2006. Zeki Ogretim Sisteminde Veri Madenciligi Kullan?lmas?, Ph. D. Dissertation Univ. of Marmara, Istanbul.
  15. Anjaneyulu K. 1997. Concept Level Modelling on the WWW, Proceedings of the workshop "Intelligent Educational Systems on the World Wide Web, 8th World Conference of the AIED Society, Kobe, Japan, August 18-22.
  16. Tsadiras AKK, Margaritis G. 1998. The MYCIN certainty factor handling function as uninorm operator and its use as a threshold function in artificial neurons. Fuzzy Sets Syst, 93:263–274.
  17. Horvitz EJ, Heckerman DE, Langlotz C. P. 1986. A framework for comparing alternative formalisms for plausible reasoning, Proc. of the 5th National Conf. On Artificial Intelligence (AAAI'86), Philadelphia, PA, Aug. 11-15, Vol. 1, pp. 210–214.
  18. Bezdek J. C. 1993. Fuzzy models—What are they, and why?, IEEE Transactions On Fuzzy Systems, 1(1):1-6. doi: 10. 1109/TFUZZ. 1993. 6027269
  19. Medasania S, Kimb J, Krishnapuram R. 1998. An overview of membership function generation techniques for pattern recognition, International Journal of Approximate Reasoning, 19(3-4):391-417. doi: 10. 1016/S0888-613X(98)10017-8
  20. Lee C. C. 1990. Fuzzy Logic in Control Systems: Fuzzy Logic Controller, Part II, Systems, Man and Cybernetics, IEEE Transactions on, 20(2): 419-435. doi:10. 1109/21. 52552
  21. Al-Humaidi H. M. 2007. A Fuzzy Logic Approach To Model Delays In Construction Projects, Ph. D. Dissertation Univ. of Ohio State, Ohio.
Index Terms

Computer Science
Information Sciences

Keywords

Online Problem-Based Learning Fuzy Logic MYCIN Trust Factor

Powered by PhDFocusTM