The method of learning outcomes assessment based on fuzzy relations

• Autorzy: Mreła A. , Sokolov O. , Urbaniak W.

Identyfikatory

DOI

10.24425/bpasts.2019.129651

• Języki publikacji: EN

Abstrakty

EN

The paper presents the method of assessment of learning outcomes acquirement by students. The analysis is based on the results of the final matriculation exam in mathematics. For crisp and both types of fuzzy relations, cut scores (passing scores) can be defined along with the method of preparing rankings of students. The advantage of applying type 2 fuzzy relations is the lack of the necessity for experts to agree to one level (one number) of verification of learning outcomes by items created for the examination. Based on the results of the exam and experts’ knowledge, the decision support system for calculating the levels of learning outcomes acquirement, making decisions about passing the examination and preparing rankings of students, can be developed. Additionally, the rank reversal phenomenon does not burden the proposed method.

Słowa kluczowe

EN

mathematical method; fuzzy relations; education; learning outcomes; ranking of students

PL

metoda matematyczna; edukacja; efekt kształcenia; ranking studentów

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2019
• Tom: Vol. 67, nr 3
• Strony: 527--533
• Opis fizyczny: Bibliogr. 27 poz., wykr., tab.

Twórcy

autor

Mreła A.

• Faculty of Technology, Kujawy and Pomorze University, 55-57 Toruńska St., 85-023 Bydgoszcz, Poland

autor

Sokolov O.

• Faculty of Physics, Astronomy and Informatics, Nicolaus Copernicus University, 5 Grudziądzka St., 87-100 Toruń, Poland

autor

Urbaniak W.

• Faculty of Mathematics, Physics & Technical Sciences, Kazimierz Wielki University, 1 Kopernika St., 85-074 Bydgoszcz, Poland

Bibliografia

• [1] EHEA, European Higher Education Area and Bologna Process, www.ehea.info, (access date: 12.07.2016).
• [2] A.Varghese, J. P. Sreedhar, S. Kolamban, and S. Nayaki, “Outcome based Assessment using Fuzzy Logic”, International Journal of Advanced Computer Science and Applications, Vol. 8, No. 1, 2017.
• [3] M. Lambovska, “A Fuzzy Model for Team Control and its Application”, Management and Production Engineering Review 9 (3), (2018), 108–119 DOI: 10.24425/119540.
• [4] D.H. Rao, S.R. Mangalwede, and V. Deshmukh. “Student performance evaluation model based on scoring rubric tool for network analysis subject using fuzzy logic”, 1?5, 2017. 10.1109/ICEECCOT.2017.8284623.
• [5] R. Ilahi, I. Widiaty, and A.G. Abdullah, “Fuzzy system application in education”, IOP Conference Series: Materials Science and Engineering. 2018, 434. 012308. 10.1088/1757?899X/434/1/012308.
• [6] M.G. Voskoglou and A. Papadopoulou, “A Fuzzy Logic Evaluation of Beliefs and Attitudes of Greek Secondary Teachers about Teaching the Euclidian GeometryGeometry”, American Journal of Computational and Applied Mathematics, 2017; 7(3): 80?85, doi:10.5923/j.ajcam.20170703.03.
• [7] A. Mreła and O. Sokolov, “Compositions of fuzzy relations applied to verification learning outcomes on the example of the major Geodesy and Cartography”, Journal of Education, Health and Sport, 5(5), 2015.
• [8] M. Mitra and A. Das, “A Fuzzy Logic Approach to Assess Web Learner’s Joint Skills”, IJMECS, vol.7, no.9, pp.14?21, 2015. DOI: 10.5815/ijmecs.2015.09.02
• [9] H. Coates, “Assessment of Learning Outcomes”. In: A. Curaj, I. Matei, T. Pricopie, J. Salmi, P. Scott (eds) The European Higher Education Area. Springer, Cham, 2015, DOI https://doi.org/10.1007/978?3-319?20877?0_26.
• [10] Regulation of the Minister of National Education of 25 April 2013 amending the regulation on the conditions and manner of assessing, classifying and promoting pupils and students as well as conducting tests and public examinations, 2013, (access date: 14.07.2016), [in Polish].
• [11] L.A. Zadeh, “Fuzzy sets”, Information and Control 8, pp. 338?353, 1965.
• [12] L. A. Zadeh, “The Concept of a Linguistic Variable and Its Application to Approximate Reasoning–1”, Information Sciences, vol. 8, pp. 199–249, 1975.
• [13] Rules for assessing task solutions, matriculation examination in 2014/2015, Central Examination Board, 2015, www.cke.edu.pl, (access date: 12.07.2016), [in Polish].
• [14] W.H. Angoff, “Norms, and equivalent scores”, in R.L. Thorndike (ed.), “Educational Measurement”, American Council on Education, Washington D.C., pp.514?515, 1971.
• [15] W.T. Dobrosielski, J. Szczepański, and H. Zarzycki, “A Proposal for a Method of Defuzzification based on the Golden Ratio – GR”, conference IWIFSGN, Kraków 2015, ISBN 978?3-319?26211?6, ISSN 2194?5357, Novel Developments in Uncertainty Representation and Processing, pp 75?84, Springer International Publishing, 2016.
• [16] W. Duch, T. Winiarski, J. Biesiada, and A. Kachel, “Feature Ranking, Selection, and Discretization”, Proc. Joint Int. Conf. on Artificial Neural Networks (ICANN) and Int. Conf. on Neural Information Processing (ICONIP), Istanbul, pp. 251?254, 2003.
• [17] W. Duch, T. Wieczorek, J. Biesiada, and M. Blachnik, “Comparison of feature ranking methods based on information entropy”, Proc. of Int. Joint Conf. on Neural Networks (IJCNN), Budapest 2004, IEEE Press, pp. 1415?1420, 2004.
• [18] L. Rutkowski, “Metody i techniki sztucznej inteligencji”, PWN, Warszawa 2009, [in Polish].
• [19] A. Mreła, O. Sokołov and T. Katafiasz, “Types of fuzzy relations’ composition applied to validation of learning outcomes at mathematics during final high school examination”, in: A. Mreła and P. Wilkoszewski (ed.), “Nauka i technika u progu III tysiąclecia”, Wydawnictwo Kujawsko-Pomorskiej Szkoły Wyższej w Bydgoszczy, Bydgoszcz, pp.119?132, 2015.
• [20] C. Cornelis, G. Deschrijver, and E.E. Kerre, “Advances and challenges in interval-valued fuzzy logic”, Fuzzy Sets and Systems, 157, 2006, pp. 622?627.
• [21] I.A. Hameed, A Simplified Implementation of Interval Type-2 Fuzzy System and its Application in Students’ Academic Evaluation, 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2016, DOI: 10.1109/FUZZ-IEEE.2016.7737748.
• [22] H.B. Barón, R.G. Crespo, J.P. Espada, and O.S. Martínez, “Assessment of learning in environments interactive through fuzzy cognitive maps”, Methodologies and Applications, Soft Comput 19:1037–1050, 2015, DOI 10.1007/s00500?014?1313-x.
• [23] I.A. Hameed and C.G. Sorensen, Fuzzy Systems in Education: A More Reliable System for Student Evaluation, in: A.T. Azar (ed.), Fuzzy Systems, IntechOpen, Rijeka 2010, DOI 10.5772/7216, https://doi.org/10.5772/7216.
• [24] A. Bolshoy, Z. Volkovich, V. Kirzhner, and Z. Barzily, “Mathematical Models for the Analysis of Natural-Language Documents”, in: “Genome Clustering”. Studies in Computational Intelligence, vol 286. Springer, Berlin, Heidelberg, 2010.
• [25] Z. Volkovich, O. Granichinc, O. Redkinc, and O. Bernikovac, “Modeling and visualization of media in Arabic”, Journal of Informetrics, Elsevier, Vol. 10, Issus 2, May 2016, pp. 439?453, 2016.
• [26] A. Darko, A.P.C. Chan, E.E. Ameyaw, E.K. Owusu, E. Pärn, and D. J. Edwards, “Review of application of analytic hierarchy process (AHP) in construction”, International Journal of Construction Management, 2018, DOI: 10.1080/15623599.2018.1452098.
• [27] M.S.D. Putra, S. Andyana, Fauziah, and A. Gunaryati, “Fuzzy Analytical Hierarchy Process Method to Determine the Quality of Gemstones”, Advances in Fuzzy Systems, Vol. 2018, Article ID 9094380, https://doi.org/10.1155/2018/9094380.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).