Uyarlamalı Bilgisayar Testlerinde Bulanık Mantık Kullanılması

Year 2020, Volume: 13 Issue: 3, 289 - 300, 31.07.2020

Atila Bostan

https://doi.org/10.17671/gazibtd.669791

Abstract

Bu çalışmada, uyarlamalı test soru seçim algoritmalarında uzman bigisinden faydalanmanın katkısı incelenmiş ve bu yaklaşımın bilgisayar destekli uayarlamalı testlerde kullanılması sınanmıştır. İlave olarak çok boyutlu soruların ölçme başarısı üzerine etkileri de incelenmiştir. Bilgisayar destekli uyarlamalı testlerde uzman bilgisinden faydalanmak üzere bir algoritma önerilmiş ve deney ortamında test edilmiştir. Bulanık mantık hesaplama yöntemini kullanan, öğrenci başarı ölçüm algritması ile yedinci sınıf fen bilgisi dersinde bir durum çalışması gerçekleştirilmiştir. Sonuç olarak, bulanık mantık hesaplama yönetimini esas alan uyarlamalı bilgisayar testi ile yapılan ölçümlerin, öğrenci başarıları arasındaki farklılıkları daha ayırıcı şekilde vurguladığı istatistiksel yöntemler ile gösterilmiştir. Ayrıca, yedi farklı boyutu içeren uyarlamalı test uygulamasında öğrencilere en az 22, en çok 31 soru yöneltmek sureti ile yeterli doğrulukta bir değerlendirme yapılabildiği gözlenmiştir. Kendi kendine öğrenme ve uzaktan eğitim ortamlarında etkin olarak kullanılacağı değerlendirilen uyarlamalı testlerde, bulanık mantık hesaplaması kullanmanın uygun bir çözüm olabileceği tespit edilmiştir.

Keywords

Uyarlamalı bilgisayar testleri, bulanık mantık, çok-boyutlu sorular

Using Fuzzy Logic in Computerized Adaptive Tests

Abstract

In this research, the contribution of expert knowledge in question selection algorithms of adaptive tests is studied and the employment of this approach in computerized adaptive tests is examined. In addition, the effect of multi-dimensional questions on measurement is also addressed. An algorithm utilizing the expert knowledge in computerized adaptive tests is proposed and tested in the experiments. A case study was conducted on seventh-grade science course with the utilization of fuzzy-logic based adaptive test. Consequently, by the help of the statistical studies, it is shown that computerized adaptive testing expresses the differences in student achievement levels in more visible way. Furthermore, 31 questions in maximum and 22 questions in minimum were observed to be effective for accurate assessment or student achievement levels on seven dimensions in adaptive testing. Using fuzzy logic in adaptive tests which were assessed to be effective in self-learning and distance education environments is found to be plausible.

Keywords

Computerized Adaptive Tests, fuzzy logic, multi-dimensional questions

References

• Hoge D. Robert and Coladarci Thedore, “Teacher Based Judgements of Academic Achievement: A Review of Literature”, Review of Educational Research, 59(3), 297-313, 1989.
• H. Borko, R. Cone, N. A. Russo, R. J. Shavelson, Teachers’ decision making. Research on Teaching: Concepts, findings and Implications, 136-160. Berkeley, CA: McCutchan Publishing Corporation, 1979.
• Penelope L. Peterson, "Teachers' and students' cognitional knowledge for classroom teaching and learning." Educational researcher, 17(5), 5-14, 1988.
• J. B. Carroll, “Measurement and Educational Psychology (Chapter 5)”, Historical Foundations of Educational Psychology, J. A. Glover, Ronning R. R. Springer - Lenum Press New York and London, 89-106, 1987.
• Thomas C. Reeves, "Keys to successful e-learning: Outcomes, assessment and evaluation." Educational Technology, 42(6), 23-29, 2002.
• E. L.Baker, O. F. Harold., Assessing Instructional Outcomes, U.S. Department of Education National Institute of Education Educational Resources Information Center (ERIC), Washington DC.,URL: https://files.eric.ed.gov/fulltext/ED266175.pdf, 02.01.2020.
• T. Evangelos, E. Georgidau, A. A. Economides, “The design and evaluation of a computerized adaptive test on mobile devices”, Computers & Education, 50(4), 1319–1330, 2008.
• H. Özcan, B. G. Emiroğlu. "Bulut Tabanlı Öğrenme Yönetim Sistemi Seçiminde Bulanık Çok Kriterli Karar Analizi Yaklaşımı." Bilişim Teknolojileri Dergisi 13(1), 97-111, 2020.
• O. Güler, O. Erdem. "Mesleki Eğitimde İnteraktif 3D Eğitimin Uygulanması ve Stereoskopik 3D Teknolojisi Kullanımı." Bilişim Teknolojileri Dergisi 7(3), 11.
• D. J. Weiss, "Improving measurement quality and efficiency with adaptive testing.", Applied psychological measurement, 6(4), 473-492, 1982.
• Ho, Rong-Guey, Yung-Chin Yen, "Design and evaluation of an XML-based platform-independent computerized adaptive testing system.", IEEE Transactions on Education, 48(2), 230-237, 2005.
• B.S. Ahmed, K. Z. Zamli, "A variable strength interaction test suites generation strategy using particle swarm optimization.", Journal of Systems and Software, 84(12), 2171-2185, 2011.
• S. Zygouris, M. Tsolaki, "Computerized cognitive testing for older adults: a review.", American Journal of Alzheimer's Disease & Other Dementias, 30(1), 13-28, 2015.
• P.J. Muñoz-Merino, M.F. Molina, M. Muñoz-Organero, C.D. Kloos, “An adaptive and innovative question-driven competition-based intelligent tutoring system for learning.”, Expert Systems with Applications, 39(8), 6932-6948, 2012.
• Internet: L.M. Rudner, “An On-Line, Interactive, Computer Adaptive Testing Tutorial”, http://EdRes.org/scripts/cat, 02.01.2020.
• M. Lilley, T. Barker and Carol Britton, "The development and evaluation of a software prototype for computer-adaptive testing.", Computers & Education, 43(1-2), 109-123, 2004.
• R.D. Carlson, "Computer adaptive testing: A shift in the evaluation paradigm.", Journal of Educational Technology Systems, 22(3), 213-224, 1994.
• R. L. Jacobson, “New computer technique seen producing a revolution in educational testing.”, Chronicle of Higher Education, 40(4),22–23, 1993.
• A.M. Boyd, Strategies for controlling testlet exposure rates in computerized adaptive testing systems, PhD Thesis, The University of Texas at Austin, May 2003.
• T.J.H.M. Eggen, Overexposure and underexposure of items in computerized adaptive testing, Measurement and Research Department Reports 2001-1, Citogroep Arnheim.
• A. Coşkun, R. Kılıç. "Meslek liselerinde modül değerlendirme sınavlarının çevrimiçi uygulanması”, Bilişim Teknolojileri Dergisi 4(1), 2011.
• F.M. Lord, M.R. Novick, A. Birnbaum, Statistical theories of mental test scores, Information Age Publishing, 2008.
• M.K. Sugeno, K. Asai, T. Terano, Fuzzy Systems Theory and Its Applications, Academic Press Limited, London, 1992.
• J. Ma and D. Zhou. "Fuzzy set approach to the assessment of student-centered learning.", IEEE Transactions on Education, 43(2), 237-241, 2000.
• I. Saleh, S. Kim, "A fuzzy system for evaluating students’ learning achievement", Expert systems with Applications, 36(3), 6236-6243, 2009.
• S.M. Bai, S.M. Chen, “Automatically constructing grade membership functions of fuzzy rules for students’ evaluation”, Expert Systems with Applications, 35(3), 1408–1414, 2008
• K.Z. Zamli, F. Din, S. Baharom, B.S. Ahmed, “Fuzzy adaptive teaching learning-based optimization strategy for the problem of generating mixed strength t-way test suites”, Engineering Applications of Artificial Intelligence, 59, 35-50, 2017.
• M. Badaracco, L. MartíNez, “A fuzzy linguistic algorithm for adaptive test in Intelligent Tutoring System based on competences”, Expert Systems with Applications, 40(8), 3073-3086, 2013.
• J. Marciniak, “Building intelligent tutoring systems immersed in repositories of e-learning content”, Procedia Computer Science, 35, 541-550, 2014.
• M. McAlpine, A Summary of Methods of Item Analysis, Computer Assisted Assessment Center, Blue Paper 2, b21, Luthon, 2002.
• Internet: L.M. Rudner, Item Response Theory (IRT), http://edres.org/irt, 02.01.2020.
• A. Sadollah, "Introductory chapter: which membership function is appropriate in fuzzy system?", Fuzzy logic based in optimization methods and control systems and its applications. IntechOpen, 2018.
• O. A. M. Ali, A. Y. Ali, B. S. Sumait, "Comparison between the effects of different types of membership functions on fuzzy logic controller performance", International Journal, 76, 76-83, 2015.
• J. M. Keller, D. Liu, D. B. Fogel, Fundamentals of computational intelligence: Neural networks, fuzzy systems, and evolutionary computation, John Wiley & Sons, 2016.
• S. Rajasekaran, G. A. Vijayalakshmi Pai, Neural Networks, Fuzzy Systems and Evolutionary Algorithms: Synthesis and Applications, PHI Learning Pvt. Ltd., 2017
• J. Suarez-Cansino, R. A. Hernandez-Gomez, "Adaptive testing system modeled through fuzzy logic", 2nd WSEAS Int. Conf on Computer Engineering and Applications (CEA 2008), Acapulco, Mexico, January. 2007.
• Balas-Timar, Dana V., Valentina E. Balas, "Ability estimation in CAT with fuzzy logic", 2009 4th International Symposium on Computational Intelligence and Intelligent Informatics. IEEE, 2009.
• V. M. Sineglazov, A. V. Kusyk, "Adaptive testing system based on the fuzzy logic", Electronics and control systems, 2, 85-91, 2018.