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ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) APPROACH FOR MODELLING THE EFFECT OF ACHIEVEMENT IN STATISTICS TO STUDENTS’ ATTITUDES TOWARD STATISTICS

Yıl 2017, Cilt: 1 Sayı: 2, 38 - 53, 28.12.2017

Öz

This study investigates the effect of achievement in statistics to students’
attitude toward statistics using the Adaptive Neuro-Fuzzy Inference System
(ANFIS).
Attitude toward statistics is
obtained by means of a statistics attitude scale, and achievement in statistics
is assessed by the midterm exam grades of the students. For fuzzy clustering
membership function is selected to be triangular and subtractive clustering is
used. As a result of clustering, three fuzzy clusters are obtained for
statistical achievement, which are named unsuccessful/ moderate/successful. The
model established from ANFIS showed
that the attitude of students receiving low achievement grades in statistics is
negative and that attitude is more positive as the achievement increases. This
study also showed that fuzzy methods are used successfully in social sciences. 

Kaynakça

  • Ashaari, N. S., Judi, H. M., Mohamed, H. and Wook, T.M.T. (2011). Student's attitude towards statistics course, Procedia - Social and Behavioral Sciences, 18, 287-294.
  • Baloğlu, M. (2004). Statistics anxiety and mathematics anxiety: Some interesting differences. Educational Research Quarterly, 27(3), 38–48.
  • Cheng, C.B. and Lee, E.S. (1999). Applying fuzzy adaptive network to fuzzy regression analysis. Computers and Mathematics with Applications, 38, 123-140.
  • Cheng, C.B. and Lee, E.S. (2001). Fuzzy regression with radial basis function network. Fuzzy Sets and Systems, 119, 291-301.
  • Chew, P. K. H., and Dillon, D. B. (2015). Statistics anxiety and attitudes toward statistics. In D. Chhabra (Ed.), Proceedings of the 4th Annual International Conference on Cognitive and Behavioral Psychology (CBP 2015). Singapore, Singapore: Global Science & Technology Forum (GSTF).
  • Cobb, G.W. and Moore, D.S. (1997). Mathematics, statistics and teaching. American Mathematical Monthly, 104(9), 801–823.
  • Coetzee, S. and Merwe, P. (2010). Industrial psychology students’ attitudes towards statistics. SA Journal of Industrial Psychology, 36(1), 843-850.
  • De Vaney, T.A. (2010). Anxiety and attitude of graduate students in on-campus vs. online statistics courses. Journal of Statistics Education, 18(1), 1-15.
  • Diri, F.Ü. (2007). The Analysis of the attitudes towards statistics case of vocational school. Gazi University, Institute of Science and Technology, MSc. Thesis. Ankara/Türkiye.
  • Gal, I. and Ginsburg, L. (1994). The role of beliefs and attitudes in learning statistics: towards an assessment framework. Journal of Statistics Education, 2(2).
  • Garfield, J. and Ben–Zvi, D. (2007). How students learn statistics revisited: A current review of research on teaching and learning statistics. International Statistical Review, 75(3), 372–396.
  • Ghulami, H.R., Ab Hamid, M.R. and Zakaria, R. (2015), Comperative analysis of positive and negative attitudes towards statistics. AIP Conference Proceedings, 1643 (1).
  • Hilton, S.C., Schau C. and Olsen, J.A. (2011). Survey of attitudes toward statistics: factor structure invariance by gender and by administration time, Structural Equation Modeling, 11(1), 92–109.
  • Ishibuchi, H. and Tanaka, H. (1993). Fuzzy neural networks with fuzzy weights and fuzzy biases. In proceedings of 1993 IEEE International Conference on Neural Networks, San Francisco, 1650-1655.
  • Jang, J.S.R. (1993). ANFIS: Adaptive-Network-based Fuzzy Inference Systems., IEEE Transactions on Systems, Man and Cybernetics, 23, 665–685.
  • Jang, J.S.R. and Sun, C.T. (1995). Neuro-fuzzy IEEE Transactions on Systems, Man, and Cybernetics modeling and control, Proceedings of the IEEE, 83(3), 378-406.
  • Judi, H. M., Ashaari, N. S., Mohamed, H. and Wook, T.M.T. (2011). Students profile based on attitude towards statistics, Procedia - Social and Behavioral Sciences, 18, 266-272.
  • Kaufman, A. and Gupta, M.M. (1988). Fuzzy mathematical models in engineering and management science, Elsevier Science Publishers B.V., The Netherlands.
  • Khavenson, T., Orel, E. and Tryakshina, M. (2012). Adaptation of survey of attitudes towards statistics (SATS 36) for Russian sample, Procedia - Social and Behavioral Sciences, 46, 2126-2129.
  • Klir, G.J. and Yuan, B. (1995). Fuzzy sets and fuzzy logic, Prentice Hall. PTR., USA.
  • Kolar, D.V. and Mc Bride, C.A. (2003). Creating problems to solve problems: an interactive teaching technique for statistics courses. Teaching of Psychology, 30 (1), 67-68.
  • Mills, J.D. (2004). Students’ attitudes toward statistics: implications for the future, College Student Journal, 38, 349–361.
  • Mvududu, N. and Kanyongo, G.Y. (2011). Using real life examples to teach abstract statistical concepts, Teaching Statistics, 33(1), 12-16.
  • Onwuegbuzie, A.J. (2004). Academic procrastination and statistics anxiety, Assessment and Evaluation in Higher Education, 29(1), 4–19.
  • Onwuegbuzie A.J. and Daley, C.E. (1996). The relative contributions of examination taking coping strategies and study coping strategies to test anxiety: A concurrent analysis, Cognitive Therapy and Research, 20, 287-303.
  • Onwuegbuzie, A.J. and Leech, N. L. (2003). Assessment in statistics courses: more than a tool for evaluation, Assessment & Evaluation in Higher Education, 28(2), 115‐127.
  • Onwuegbuzie, AJ. and Seaman, M. (1995). The effect of time constraints and statistics text anxiety on test performance in a statistics course, Journal of Experimental Education, 63, 115–124.
  • Ridgeway, J., Nicholson, J. and Mc Cusker, S. (2007). Teaching statistics – despite its applications. Teaching Statistics, 29(2), 44–48.
  • Roberts, D.M. and Bilderback, E.W. (1980). Reliability and validity of a statistics attitude survey. Educational and Psychological Measurement, 40, 235-238.
  • Salim, N.R. and Ayub, A.F.M. (2017). Relationship between mathematics statistics engagement and attitudes towards statistics among undergraduate students in Malaysia, AIP Conference Proceedings, 1795 (1).
  • Santillan, A. G., Garcia, E. M., Castro, C., Abdala, J.J.H. Z. and Trejo, J.G. (2012). Cognitive, affective and behavioral components that explain attitude toward statistics. Journal of Mathematics Research, 4(5), 8-16.
  • Santillan, A.G., Chavez, M. E.E., Kramer, C.A.R., Rangel, A.C. and Texon, F.P. (2016). Students’ attitudes toward statistics: a comparison between universities, The Online Journal of New Horizons in Education, 6 (1), 136-150.
  • Sesé, A., Jiménez, R., Montaño, J-J and Palmer, A. (2015). Can attitudes toward statistics and statistics anxiety explain students’ performance? Revista de Psicodidáctica, 20 (2), 285-304.
  • Takagi, T. and Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, 15(1), 116–132.
  • Zadeh, L.A. (1965). Fuzzy Sets, Information and Control, 8, 338-353.
  • Zanakis, S.H. and Valenzi, E.R. (.1997). Student anxiety and attitudes in business statistics. Journal of Education for Business, 73(1), 10–16.

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) APPROACH FOR MODELLING THE EFFECT OF ACHIEVEMENT IN STATISTICS TO STUDENTS’ ATTITUDES TOWARD STATISTICS

Yıl 2017, Cilt: 1 Sayı: 2, 38 - 53, 28.12.2017

Öz

This study investigates the effect of achievement in statistics to students’ attitude toward statistics using the Adaptive Neuro-Fuzzy Inference System (ANFIS). Attitude toward statistics is obtained by means of a statistics attitude scale, and achievement in statistics is assessed by the midterm exam grades of the students. For fuzzy clustering membership function is selected to be triangular and subtractive clustering is used. As a result of clustering, three fuzzy clusters are obtained for statistical achievement, which are named unsuccessful/ moderate/successful. The model established from ANFIS showed that the attitude of students receiving low achievement grades in statistics is negative and that attitude is more positive as the achievement increases. This study also showed that fuzzy methods are used successfully in social sciences. 

Kaynakça

  • Ashaari, N. S., Judi, H. M., Mohamed, H. and Wook, T.M.T. (2011). Student's attitude towards statistics course, Procedia - Social and Behavioral Sciences, 18, 287-294.
  • Baloğlu, M. (2004). Statistics anxiety and mathematics anxiety: Some interesting differences. Educational Research Quarterly, 27(3), 38–48.
  • Cheng, C.B. and Lee, E.S. (1999). Applying fuzzy adaptive network to fuzzy regression analysis. Computers and Mathematics with Applications, 38, 123-140.
  • Cheng, C.B. and Lee, E.S. (2001). Fuzzy regression with radial basis function network. Fuzzy Sets and Systems, 119, 291-301.
  • Chew, P. K. H., and Dillon, D. B. (2015). Statistics anxiety and attitudes toward statistics. In D. Chhabra (Ed.), Proceedings of the 4th Annual International Conference on Cognitive and Behavioral Psychology (CBP 2015). Singapore, Singapore: Global Science & Technology Forum (GSTF).
  • Cobb, G.W. and Moore, D.S. (1997). Mathematics, statistics and teaching. American Mathematical Monthly, 104(9), 801–823.
  • Coetzee, S. and Merwe, P. (2010). Industrial psychology students’ attitudes towards statistics. SA Journal of Industrial Psychology, 36(1), 843-850.
  • De Vaney, T.A. (2010). Anxiety and attitude of graduate students in on-campus vs. online statistics courses. Journal of Statistics Education, 18(1), 1-15.
  • Diri, F.Ü. (2007). The Analysis of the attitudes towards statistics case of vocational school. Gazi University, Institute of Science and Technology, MSc. Thesis. Ankara/Türkiye.
  • Gal, I. and Ginsburg, L. (1994). The role of beliefs and attitudes in learning statistics: towards an assessment framework. Journal of Statistics Education, 2(2).
  • Garfield, J. and Ben–Zvi, D. (2007). How students learn statistics revisited: A current review of research on teaching and learning statistics. International Statistical Review, 75(3), 372–396.
  • Ghulami, H.R., Ab Hamid, M.R. and Zakaria, R. (2015), Comperative analysis of positive and negative attitudes towards statistics. AIP Conference Proceedings, 1643 (1).
  • Hilton, S.C., Schau C. and Olsen, J.A. (2011). Survey of attitudes toward statistics: factor structure invariance by gender and by administration time, Structural Equation Modeling, 11(1), 92–109.
  • Ishibuchi, H. and Tanaka, H. (1993). Fuzzy neural networks with fuzzy weights and fuzzy biases. In proceedings of 1993 IEEE International Conference on Neural Networks, San Francisco, 1650-1655.
  • Jang, J.S.R. (1993). ANFIS: Adaptive-Network-based Fuzzy Inference Systems., IEEE Transactions on Systems, Man and Cybernetics, 23, 665–685.
  • Jang, J.S.R. and Sun, C.T. (1995). Neuro-fuzzy IEEE Transactions on Systems, Man, and Cybernetics modeling and control, Proceedings of the IEEE, 83(3), 378-406.
  • Judi, H. M., Ashaari, N. S., Mohamed, H. and Wook, T.M.T. (2011). Students profile based on attitude towards statistics, Procedia - Social and Behavioral Sciences, 18, 266-272.
  • Kaufman, A. and Gupta, M.M. (1988). Fuzzy mathematical models in engineering and management science, Elsevier Science Publishers B.V., The Netherlands.
  • Khavenson, T., Orel, E. and Tryakshina, M. (2012). Adaptation of survey of attitudes towards statistics (SATS 36) for Russian sample, Procedia - Social and Behavioral Sciences, 46, 2126-2129.
  • Klir, G.J. and Yuan, B. (1995). Fuzzy sets and fuzzy logic, Prentice Hall. PTR., USA.
  • Kolar, D.V. and Mc Bride, C.A. (2003). Creating problems to solve problems: an interactive teaching technique for statistics courses. Teaching of Psychology, 30 (1), 67-68.
  • Mills, J.D. (2004). Students’ attitudes toward statistics: implications for the future, College Student Journal, 38, 349–361.
  • Mvududu, N. and Kanyongo, G.Y. (2011). Using real life examples to teach abstract statistical concepts, Teaching Statistics, 33(1), 12-16.
  • Onwuegbuzie, A.J. (2004). Academic procrastination and statistics anxiety, Assessment and Evaluation in Higher Education, 29(1), 4–19.
  • Onwuegbuzie A.J. and Daley, C.E. (1996). The relative contributions of examination taking coping strategies and study coping strategies to test anxiety: A concurrent analysis, Cognitive Therapy and Research, 20, 287-303.
  • Onwuegbuzie, A.J. and Leech, N. L. (2003). Assessment in statistics courses: more than a tool for evaluation, Assessment & Evaluation in Higher Education, 28(2), 115‐127.
  • Onwuegbuzie, AJ. and Seaman, M. (1995). The effect of time constraints and statistics text anxiety on test performance in a statistics course, Journal of Experimental Education, 63, 115–124.
  • Ridgeway, J., Nicholson, J. and Mc Cusker, S. (2007). Teaching statistics – despite its applications. Teaching Statistics, 29(2), 44–48.
  • Roberts, D.M. and Bilderback, E.W. (1980). Reliability and validity of a statistics attitude survey. Educational and Psychological Measurement, 40, 235-238.
  • Salim, N.R. and Ayub, A.F.M. (2017). Relationship between mathematics statistics engagement and attitudes towards statistics among undergraduate students in Malaysia, AIP Conference Proceedings, 1795 (1).
  • Santillan, A. G., Garcia, E. M., Castro, C., Abdala, J.J.H. Z. and Trejo, J.G. (2012). Cognitive, affective and behavioral components that explain attitude toward statistics. Journal of Mathematics Research, 4(5), 8-16.
  • Santillan, A.G., Chavez, M. E.E., Kramer, C.A.R., Rangel, A.C. and Texon, F.P. (2016). Students’ attitudes toward statistics: a comparison between universities, The Online Journal of New Horizons in Education, 6 (1), 136-150.
  • Sesé, A., Jiménez, R., Montaño, J-J and Palmer, A. (2015). Can attitudes toward statistics and statistics anxiety explain students’ performance? Revista de Psicodidáctica, 20 (2), 285-304.
  • Takagi, T. and Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, 15(1), 116–132.
  • Zadeh, L.A. (1965). Fuzzy Sets, Information and Control, 8, 338-353.
  • Zanakis, S.H. and Valenzi, E.R. (.1997). Student anxiety and attitudes in business statistics. Journal of Education for Business, 73(1), 10–16.
Toplam 36 adet kaynakça vardır.

Ayrıntılar

Bölüm olgu sunumları
Yazarlar

Nuray Güneri Tosunoğlu

Yayımlanma Tarihi 28 Aralık 2017
Gönderilme Tarihi 22 Kasım 2017
Kabul Tarihi 27 Aralık 2017
Yayımlandığı Sayı Yıl 2017 Cilt: 1 Sayı: 2

Kaynak Göster

APA Güneri Tosunoğlu, N. (2017). ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) APPROACH FOR MODELLING THE EFFECT OF ACHIEVEMENT IN STATISTICS TO STUDENTS’ ATTITUDES TOWARD STATISTICS. Kapadokya Akademik Bakış, 1(2), 38-53.