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 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
The week's pick
Responsive image
An Efficient Restoration Method for the Faint Dot Matrix Images

Ao Zhu Peng Cao
Random Articles
Reseach Article

Computerized Adaptive Test based on Item Response Theory in E-Learning System

by Yeni Kustiyahningsih, andharini Dwi Cahyani
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 81 - Number 6
Year of Publication: 2013
Authors: Yeni Kustiyahningsih, andharini Dwi Cahyani
10.5120/14014-2022

Yeni Kustiyahningsih, andharini Dwi Cahyani . Computerized Adaptive Test based on Item Response Theory in E-Learning System. International Journal of Computer Applications. 81, 6 ( November 2013), 6-11. DOI=10.5120/14014-2022

@article{ 10.5120/14014-2022,
author = { Yeni Kustiyahningsih, andharini Dwi Cahyani },
title = { Computerized Adaptive Test based on Item Response Theory in E-Learning System },
journal = { International Journal of Computer Applications },
issue_date = { November 2013 },
volume = { 81 },
number = { 6 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 6-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume81/number6/14014-2022/ },
doi = { 10.5120/14014-2022 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:55:20.918430+05:30
%A Yeni Kustiyahningsih
%A andharini Dwi Cahyani
%T Computerized Adaptive Test based on Item Response Theory in E-Learning System
%J International Journal of Computer Applications
%@ 0975-8887
%V 81
%N 6
%P 6-11
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Computerized Adaptive Test (CAT) is a computer-based test framework which has ability to customize questions items given to the learner based on their estimated ability. In this research, the CAT system is build using Item Response Theory (IRT) techniques to develop an adaptive system based on question item's difficulty level and students' ability level. Moreover, to figure out the effectiveness of this CAT system, we do some experiments by comparing the average post-test score of students in CAT system and conventional system. The experiments result reveals that the average post-test score of students in the CAT system is much higher than the average post-test score of students in traditional test system.

References
  1. Andharini, D. C. , & Ari Basuki. (2012). Personalized Learning Path of a Web-based Learning System. International Journal of Computer Applications 53(7):17-22
  2. Alavi, M. , & Leidner, D. E. (2001). Research commentary: Technologymediated
  3. Brusilovsky, P. (1999). Adaptive and intelligent technologies for web-based education. Künstliche Intelligenz, 13(4), 19–25.
  4. Baker, F. (2001). The basics of item response theory. ERIC clearing house on assessment and evaluation.
  5. Barker, K. (2002). E-learning in three step. In School business affairs. Available http://www. asbointl. org
  6. Davidovic, A. , Warren, J. , & Trichina, E. (2003). Learning benefits of structural example-based adaptive tutoring systems. IEEE Transactions on Education, 46(2), 241–251.
  7. Georgieva, G. , Todorov, G. , & Smrikarov, A. (2003). A model of a Virtual Universitysome problems during its development. In Proceedings of the 4th international conference on Computer systems and technologies: e-Learning. Bulgaria: ACM Press.
  8. Hamdi, M. S. (2007). MASACAD: A multi-agent approach to information customization for the purpose of academic advising of students. Applied Soft Computing, 7, 746–771.
  9. Huang, M. J. , Huang, H. S. , & Chen, M. Y. (2007). Constructing a personalized elearning system based on genetic algorithm and case-based reasoning approach. Expert Systems with Applications, 33, 551–564.
  10. Kabassi, K. , & Virvou, M. (2004). Personalised adult e-training on computer use based on multiple attribute decision making. Interacting with Computers, 16, 115–132.
  11. Kim, S. (2006). A comparative study of IRT fixed parameter calibration methods. Journal of Educational Measurement, 43(4), 355–381.
  12. L Jayasimman & George Dharma Prakash E Raj. (2013). A Soft Computing Approach for User Preference in Web based Learning. International Journal of Computer Applications 61(21):25-29
  13. Learning—A call for greater depth and breadth of research. Information Systems Research, 12(1), 1–10.
  14. Ozaki, K. , & Toyoda, H. (2006). Paired comparison IRT model by 3-value judgment: estimation of item parameters prior to the administration of the test. Behaviormetrika, 33(2), 131–147.
  15. Van der Linden, W. J. , & Hambleton, R. K. (1997). Handbook of modern item response theory. New York: Springer.
  16. Wauters, K. , Desmet, P. , & Van den Noortgate, W. (2010). Adaptive item-based learning environments based on the item response theory: possibilities and challenges. Journal of Computer Assisted Learning, 26(6), 549–562.
  17. Wiliam, D. (2011). What is assessment for learning? Studies in Educational Evaluation, 37(1), 3–14.
  18. Wang, F. -H. (2006). Application of componential IRT model for diagnostic test in a standard conformant elearning system. In Sixth international conference on advanced learning technologies (ICALT'06).
  19. Xu, D. , & Wang, H. (2006). Intelligent agent supported personalization for virtual learning environments. Decision Support Systems, 42, 825–843.
Index Terms

Computer Science
Information Sciences

Keywords

Computerized Adaptive Test Item Response Theory students ability level maximum likelihood estimation

Powered by PhDFocusTM