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Prediction of students’ perceptions of problem solving skills with a neuro-fuzzy model and hierarchical regression method: A quantitative study

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Abstract

Traditionally, students’ various educational characteristics are evaluated according to the grades they get or the results of their answers to the scales. There are some limitations in making an evaluation based on the results. The fuzzy logic approach, which tries to eliminate these limitations, has recently been used in the field of education. While applying the fuzzy logic method to education, students’ qualifications are determined qualitatively without using formulas in calculating student performance. However, fuzzy systems lack learning abilities. By combining fuzzy rules and neural networks, the evaluation tool will have greater adaptability to changing conditions. Thus, an educationally robust and easy-to-use assessment tool will be obtained. In this study, in the first stage, students’ perceptions of problem solving skills, which is one of their educational characteristics, were modeled with the ANFIS approach, which is one of the neuro-fuzzy systems apart from traditional methods, through creative problem solving features. ANFIS is an adaptive network that allows neural network topology to be combined with fuzzy logic. It not only incorporates the benefits of both strategies but also eliminates some of their drawbacks when used alone. The inputs of the research were determined as students’ creative problem-solving characteristics and the output was their perceptions of problem-solving skills. As a second step, statistical methods (correlation and hierarchical regression) were used to examine whether there was a relationship between students’ PoPS skills and CPS characteristics. Afterwards, students’ artificial PoPS skill scores obtained with ANFIS in the first step and real PoPS skill scores obtained from their answers to the scale were compared. 360 students from Turkey took part in the study. Depending on the findings of the study, real PoPS scores and artificial ANFIS PoPS scores do not statistically differ significantly. Therefore, the ANFIS results based on creative problem solving features accurately predict students’ PoPS scores. Additionally, there is a clear relationship between PoPS talents and CPS features. One of the study's most startling conclusions is that the environment, which is accepted as one of the components affecting creative problem solving in this research, predicts students’ perceptions of problem solving skills. These results also prove that the variable of creative problem solving characteristics, which is used to predict students’ perceptions of problem solving, is an appropriate variable. It is possible to create the ANFIS system employed in this study utilizing a variety of fuzzy functions and other neuro/fuzzy techniques, and the systems can be compared with each other.

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Data availability

Data is available upon request from the corresponding author.

References

  • Akgul, A., & Çevik, O. (2003). Istatistiksel analiz teknikleri. Emek Ofset.

  • Arslan, M., & Zirhlioğlu, G. (2021). Öğretmen Performanslarının Bulanık Mantık Yöntemiyle Değerlendirilmesi. Yüzüncü Yıl Üniversitesi Eğitim Fakültesi Dergisi, 18(1), 569–594.

    Google Scholar 

  • Atmaca, H., Cetisli, B., & Yavuz, H. S. (2001). The comparison of fuzzy inference systems and neural network approaches with ANFIS method for fuel consumption data. In Second international conference on electrical and electronics engineering papers ELECO, 6, 1-4.

  • Bai, Y., & Wang, D. (2006). Fundamentals of fuzzy logic control—fuzzy sets, fuzzy rules and defuzzifications. In Advanced fuzzy logic technologies in industrial applications (pp. 17–36). Springer.

  • Baran-Bulut, B., Ipek, A. S., & Aygun, B. (2018). Adaptation study of creative problem solving features inventory to turkish. Abant Izzet Baysal University Journal of Faculty of Education, 18(3), 1360–1377.

    Google Scholar 

  • Barlybayev, A., Sharipbay, A., Ulyukova, G., Sabyrov, T., & Kuzenbayev, B. (2016). Student’s performance evaluation by fuzzy logic. Procedia Computer Science, 102, 98–105.

    Google Scholar 

  • Bingham, A. (1998). Developing the skills of problem solving of children. (Translation, A. F. Oğuzhan). National Education.

  • Bolat, Y. (2006). Matlab-Sımulınk+Pıc Tabanlı Bulanık Mantık Denetleyici Tasarımı ve Gerçek Zamanlı Sıcaklık Kontrolü Uygulaması. Yüksek Lisans Tezi. Marmara Üniversitesi Fen Bilimleri Enstitüsü.

  • Brophy, D. R. (1998). Understanding, measuring, and enhancing individual creative problem-solving efforts. Creativity Research Journal, 11(2), 123–150.

    Google Scholar 

  • Brophy, D. R. (2001). Comparing the attributes, activities, and performance of divergent, convergent, and combination thinkers. Creativity Research Journal, 13, 439–455.

    Google Scholar 

  • Büyüköztürk, Ş. (2012). Sosyal bilimler için veri analizi el kitabı. Pegem Akademi.

  • Büyüköztürk, Ş. (2016). Sosyal bilimler için veri analizi el kitabı [Data analysis handbook for social sciences] (3rd ed.). Pegem Akademi.

  • Büyüköztürk, Ş., Çokluk, Ö., & Köklü, N. (2010). Sosyal bilimler için istatistik. Pegem Akademi.

  • Cavallaro, F. A. (2015). Takagi-Sugeno Fuzzy Inference System for Developing a Sustainability Index of Biomass. Sustainability, 12359–12371.

  • Chen, C. R., Ouedraogo, F. B., Chang, Y. M., Larasati, D. A., & Tan, S. W. (2021). Hour-Ahead Photovoltaic Output Forecasting Using Wavelet-ANFIS. Mathematics, 9(19), 2438.

    Google Scholar 

  • Cho, S. (2003). Creative problem solving in science: Divergent, convergent, or both. In 7th Asia-pacific Conference on Giftedness, 169–174.

  • Chowdhury, M. M., & Li, Y. (1998). Learning fuzzy control by evolutionary and advantage reinforcements. International Journal of Intelligent Systems, 13, 949–974.

    Google Scholar 

  • Corazza, G. E. (2016). Potential originality and effectiveness: The dynamic definition of creativity. Creativity Research Journal, 28(3), 258–267.

    Google Scholar 

  • Cropley, A. J. (2006). In praise of convergent thinking. Creativity Research Journal, 18, 391–404.

    Google Scholar 

  • Çobanoğlu, B. (2000). Bulanık Mantık ve Bulanık Küme Teorisi. Niksar MYO /GOP Universitesi.

  • Daneshvar, A., Homayounfar, M., FadaeiEshkiki, M., & Doshmanziari, E. (2021). Developing a Model for Performance Evaluaion of Teachers in Electronic Education System Using Adaptive Neuro Fuzzy Inference System (ANFIS). Journal of New Approaches in Educational Administration, 12(4), 176–190.

    Google Scholar 

  • Del Rincon, D., Arnal, J., Latorre, A., & Sans, A. (2003). Técnicas de investigación en ciencias sociales. Dykinson.

    Google Scholar 

  • Dixon, W. A., Heppner, P. P., & Anderson, W. P. (1991). Problem-solving appraisal, stress, hopelessness, and suicide ideation in a college population. Journal of Counseling Psychology, 38(1), 51–56.

    Google Scholar 

  • Dragovic, I., Turajlic, N., Pilcevic, D., Petrovic, B., & Radojevic, D. A. (2015). Boolean Consistent Fuzzy Inference System for Diagnosing Diseases and Its Application for Determining Peritonitis Likelihood. Computational and Mathematical Methods in Medicine.

  • Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. In MHS’95 Proceedings of the sixth international symposium on micro machine and human science (pp. 39–43).

  • Eisenberger, R., & Rhoades, L. (2001). Incremental effects of reward on creativity. Journal of Personality and Social Psychology, 81(4), 728.

    Google Scholar 

  • Eisenberger, R., & Aselage, J. (2009). Incremental effects of reward on experienced performance pressure: Positive outcomes for intrinsic interest and creativity. Journal of Organizational Behavior: The International Journal of Industrial, Occupational and Organizational Psychology and Behavior, 30(1), 95–117.

    Google Scholar 

  • Ekici, D. İ, & Balım, A. G. (2013). Problem solving skills perception scale for secondary students: A study of validity and reliability. Yuzuncu Yil University Journal of Education, 10(1), 67–86.

    Google Scholar 

  • Fraenkel, J. R., & Wallen, N. E. (2006). How to design and evaluate research in education. McGaw-Hill International Edition.

  • Gaglione, M. (2021). Nurturing creative problem solving in social sciences in middle school students (Doctoral dissertation, St. John's University (New York)).

  • Gangadwala, H., & Gulati, R. M. (2012). Grading & analysis of oral presentation-a fuzzy approach. International Journal of Engineering Research and Development, 2, 1–4.

    Google Scholar 

  • Gaur, A. S., & Gaur, S. S. (2009). Statistical Methods for Practice and Research. Response Books.

  • Gong, S. (2020). On the cultivation of middle school students’ creativity. English Language Teaching, 13(1), 134–140.

    MathSciNet  Google Scholar 

  • Guilford, J. P. (1950). Creativity. American Psychologist, 5, 444–454.

    Google Scholar 

  • Guilford, J. P. (1956). The structure of intellect. Psychological Bulletin, 53, 267–293.

    Google Scholar 

  • Guruprasad, M., Sridhar, R., & Balasubramanian, S. (2016). Fuzzy logic as a tool for evaluation of performance appraisal of faculty in higher education institutions. In SHS Web of Conferences.

  • Heppner, P. P., & Petersen, C. H. (1982). The development of implications of a personal problem solving inventory. Journal of Counseling Psychology, 29, 66–75.

    Google Scholar 

  • Heppner, P. P., & Baker, C. E. (1997). Application of problem solving inventory. Measurement and Evaluation in Counseling and Development, 29(4), 129–143.

    Google Scholar 

  • Heppner, P. P. & Wang, Y.-W. (2003). Problem-solving appraisal. In Positive psychological assessment: A handbook of models and measures, 127–138.

  • Heppner, P. P., Witty, T. E., & Dixon, W. A. (2004). Problem solving appraisal and human adjustment: A review of 20 years of research using the problem solving inventory. The Counseling Psychologist, 32, 344–428.

    Google Scholar 

  • Houtz, J. C. (1994). Creative problem solving in the classroom: Contributions of four psychological approaches. Problem finding, problem solving, and creativity, 153–173.

  • Hsia, L. H., Lin, Y. N., & Hwang, G. J. (2021). A creative problem solving-based flipped learning strategy for promoting students’ performing creativity, skills and tendencies of creative thinking and collaboration. British Journal of Educational Technology, 52(4), 1771–1787.

    Google Scholar 

  • Hunter, S. T., Bedell, K. E., & Mumford, M. D. (2007). Climate for creativity: A quantitative review. Creativity Research Journal, 19(1), 69–90.

    Google Scholar 

  • Isaksen, S. G., & Treffinger, D. J. (2004). Celebrating 50 years of reflective practice: Versions of creative problem solving. The Journal of Creative Behavior, 38(2), 75–101.

    Google Scholar 

  • Ishibuchi, H., Nii, M., & Turksen, I. B., (1998). Bidirectional bridge between neural networks and linguistic knowledge: Linguistic rule extraction and learning from linguistic rules. In Proceedings of the IEEE International Conference Fuzzy Systems (pp. 1112–1117).

  • Jafarkhani, F. (2018). Application of fuzzy system in assessment of practical courses: Developing educational multimedia. Faculty of Education Sciences, 12(4), 339–346.

    Google Scholar 

  • Jamsandekar, S. S., & Mudholkar, R. R. (2013). Performance Evaluation by Fuzzy Inference Technique. International Journal of Soft Computing and Engineering, 158–164.

  • Jang, J. S. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665–685.

  • Jang, J. S., & Sun, C. T. (1995). Neuro-fuzzy modeling and control. Proceedings of the IEEE, 83(3), 378–406.

    Google Scholar 

  • Jang, J. S. R. (1996). Input selection for ANFIS learning. In Proceedings of the Fifth IEEE International Conference on Fuzzy Systems.

  • Johnson, R. B., & Christensen, L. B. (2014). Educational research: Quantitative, qualitative, and mixed approaches (5th ed.). Sage.

    Google Scholar 

  • Kaplan, A., Duran, M., & Baş, G. (2016). Examination with the structural equation modeling of the relationship between mathematical metacognition awareness with skill perception of problem solving of secondary school students. The Inonu University Journal of the Faculty of Education, 17(1), 01–16.

    Google Scholar 

  • Kaptan, S. (1998). Bilimsel Araştırma ve İstatistik Teknikleri (11.Baskı). Tek Işık Web Ofset.

  • Kashani-Vahid, L., Afrooz, G., Shokoohi-Yekta, M., Kharrazi, K., & Ghobari, B. (2017). Can a creative interpersonal problem solving program improve creative thinking in gifted elementary students? Thinking Skills and Creativity, 24, 175–185.

    Google Scholar 

  • Kaufman, J. C., & Sternberg, R. J. (2007). Creativity. Change: The Magazine of Higher Learning, 39(4), 55–60.

  • Kay, K. (2010). 21st century skills: Why they matter, what they are, and how we get there. Foreword in: 21st Century skills: Rethinking how students learn. Learning Tree.

  • Khalid, M., Saad, S., Hamid, S. R. A., Abdullah, M. R., Ibrahim, H., & Shahrill, M. (2020). Enhancing creativity and problem solving skills through creative problem solving in teaching mathematics. Creativity Studies, 13(2), 270–291.

    Google Scholar 

  • Kim, S., Chung, K., & Yu, H. (2013). Enhancing digital fluency through a training program for creative problem solving using computer programming. The Journal of Creative Behavior, 47(3), 171–199.

    Google Scholar 

  • Koç Aytekin, G., & Işık Tertemiz, N. (2018). Pisa sonuçlarinin 2003–2015 eğitim sistemi ve ekonomik göstergeler kapsaminda incelenmesi: Türkiye ve güney kore örneği. Ahi Evran Üniversitesi Kırşehir Eğitim Fakültesi Dergisi, 19(1), 103–128.

  • Latah, M. (2016). ANFIS approach with genetic feature selection for prediction of students’ academic performance in distance education environment. International Journal of Engineering Applied Sciences and Technology, 1(8), 2455–2143.

    Google Scholar 

  • Lin, C. Y. (2010). Analyses of attribute patterns of creative problem solving ability among upper elementary students in Taiwan. ProQuest LLC. 789 East Eisenhower Parkway.

  • Lin, C. Y., & Cho, S.(2011). Predicting creative problem-solving in math from a dynamic system model of creative problem solving ability. Creativity Research Journal, 23(3), 255–261.

  • Lumsdaine, M., & Lumsdaine, E. (1995). Thinking preferences of engineering students: Implications for curriculum restructuring. Journal of Engineering Education, 84(2), 193–204.

    Google Scholar 

  • Mendel, J. M. (1995). Fuzzy Logic Systems for Engineering: a tutorial. Proceedings of the IEEE, 345–377.

  • MacNair, R. R., & Elliott, T. R. (1992). Self-perceived problem solving ability, stress appraisal, and coping over time. Journal of Research in Personality, 26, 150–164.

    Google Scholar 

  • Meenakshi, N., & Pankaj, N. (2015). Application of fuzzy logic for evaluation of academic performance of students of computer application course. International Journal for Research in Applied Science & Engineering Technology (IJRASET), 3, 260–267.

    Google Scholar 

  • Mehdi, R., & Nachouki, M. (2022). A neuro-fuzzy model for predicting and analyzing student graduation performance in computing programs. Education and Information Technologies. https://doi.org/10.1007/s10639-022-11205-2

    Article  Google Scholar 

  • Ministry of National Education (2018). Ortaokul matematik dersi öğretim programı [Middle school mathematics curriculum]. Author. Retrieved from http://mufredat.meb.gov.tr/Dosyalar/201813017165445-MATEMAT%C4%B0K%20%C3%96%C4%9ERET%C4%B0M%20PROGRAMI%202018v.pdf.

  • Mitra, S., & Hayashi, Y. (2000). Neuro-fuzzy rule generation: Survey in soft computing framework. IEEE Transactions on Neural Networks, 11(3), 748–768.

    Google Scholar 

  • Mumford, M. D., & Gustafson, S. B. (2007). Creative thought: Cognition and problem solving in a dynamic system. Creativity Research Handbook, 2, 33–77.

    Google Scholar 

  • Mumford, M. D., Medeiros, K. E., & Partlow, P. J. (2012). Creative thinking: Processes, strategies, and knowledge. Journal of Creative Behavior, 46, 30–47.

    Google Scholar 

  • Negnevitsky, M. (2017). Artificial Intelligence: A guide to intelligent systems. Addison Wesley.

  • Özdemir, O., & Tekin, A. (2016). Evaluation of the presentation skills of the pre-service teachers via fuzzy logic. Computers in Human Behavior, 61, 288–299.

    Google Scholar 

  • Özdemir, G., & Dikici, A. (2017). Relationships between scientific process skills and scientific creativity: Mediating role of nature of science knowledge. Journal of Education in Science, Environment and Health (JESEH), 3(1), 52–68.

  • Özkan, M. (2018). Bulanık çıkarım sistemi ile bireysel personel performansının değerlendirilmesinde bir uygulama. C.Ü. İktisadi ve İdari Bilimler Dergisi, 19(2), 372–388.

  • Palmiero, M., Nori, R., Piccardi, L., & D’Amico, S. (2020). Divergent Thinking: The Role of Decision-Making Styles. Creativity Research Journal, 32(4), 323–332.

    Google Scholar 

  • Piersel, C. W., Larson, M. L., Allen, S. J., & Imao, A. K. (1993). Self perceived effective and ineffective problem solvers’ differential views of their partners’ problem solving styles. Journal of Counseling and Development, 71, 528–538.

    Google Scholar 

  • Plucker, J. A., Runco, M. A., & Lim, W. (2006). Predicting ideational behavior form divergent thinking and discretionary time on task. Creativity Research Journal, 18, 55–63.

    Google Scholar 

  • Proctor, R. M., & Burnett, P. C. (2004). Measuring cognitive and dispositional characteristics of creativity in elementary students. Creativity Research Journal, 16, 421–429.

    Google Scholar 

  • Rosenberg, M. (1989). Society and the adolescent self-image. Wesleyan University Press.

    Google Scholar 

  • Runco, M. A., & Acar, S. (2012). Divergent thinking as an indicator of creative potential. Creativity Research Journal, 24(1), 66–75.

    Google Scholar 

  • Runco, M. A., & Jaeger, G. J. (2012). The standard definition of creativity. Creativity Research Journal, 24(1), 92–96.

    Google Scholar 

  • Weisberg, R. W. (2015). On the usefulness of “value” in the definition of creativity. Creativity Research Journal, 27(2), 111–124.

    Google Scholar 

  • Runscio, A. M., & Amabile, T. M. (1999). Effects of instructional styles on problem-solving creativity. Creativity Research Journal, 12, 251–266.

    Google Scholar 

  • Saxon, J. A., Treffinger, D. J., Young, G. C., & Wittig, C. V. (2003). Camp Invention®: A creative, inquiry-based summer enrichment program for elementary students. The Journal of Creative Behavior, 37, 64–74.

    Google Scholar 

  • Schoevers, E. M., Leseman, P. P., Slot, E. M., Bakker, A., Keijzer, R., & Kroesbergen, E. H. (2019). Promoting pupils’ creative thinking in primary school mathematics: A case study. Thinking Skills and Creativity, 31, 323–334.

    Google Scholar 

  • Schunk, D.H. (2004). Learning theories: An educational perspective (4th ed.), Merrill/Prentice Hall.

  • Shleeg, A. A., & Ellabib, I. M. (2013). Comparison of Mamdani and Sugeno Fuzzy Interference Systems for the Breast Cancer Risk. International Journal of Computer, Electrical, Automation, Control and Information Engineering, 1343–1347.

  • Simonton, D. K. (2012). Taking the US Patent Office criteria seriously: A quantitative three-criterion creativity definition and its implications. Creativity Research Journal, 24(2–3), 97–106.

  • Sternberg, R. J. (2006). The nature of creativity. Creativity Research Journal, 18(1), 87.

    Google Scholar 

  • Şahin, N. H., Şahin, N., & Heppner, P. (1993). Psychometrics properties of the problem solving inventory in a group of turkish university students. Cognitive Theraphy and Research, 17(3), 379–385.

    Google Scholar 

  • Tailor, B., Shah, R., Dhodiya, J., & Joshi, D. (2014). An evaluation of faculty performance in teaching using fuzzy modeling approach. International Journal of Advance Engineering and Research Development (IJAERD), 1(3), 1–6.

    Google Scholar 

  • Taylan, O., & Karagözoğlu, B. (2009). An adaptive neuro-fuzzy model for prediction of student’s academic performance. Computers & Industrial Engineering, 57(3), 732–741.

    Google Scholar 

  • Tierney, P., & Farmer, S. M. (2004). The Pygmalion process and employee creativity. Journal of Management, 30(3), 413–432.

    Google Scholar 

  • Titus, P. A. (2000). Marketing and the creative problem-solving process. Journal of Marketing Education, 22(3), 225–235.

    Google Scholar 

  • Urban, K. (2003). Toward a componential model of creativity. Creative intelligence: Toward theoretic integration, 81 – 112.

  • Van Hooijdonk, M., Mainhard, T., Kroesbergen, E. H., & Van Tartwijk, J. (2020). Creative problem solving in primary education: Exploring the role of fact finding, problem finding, and solution finding across tasks. Thinking Skills and Creativity, 37, 100665.

    Google Scholar 

  • Vasileva-Stojanovska, T., Vasileva, M., Malinovski, T., & Trajkovik, V. (2015). An ANFIS model of quality of experience prediction in education. Applied Soft Computing, 34, 129–138.

    Google Scholar 

  • Vieira, J., Dias, F. M., & Mota, A. (2004). Neuro-fuzzy systems: a survey. In 5th WSEAS NNA international conference on neural networks and applications, Udine, Italia (pp. 1–6).

  • Vincent, A. S., Decker, B. P., & Mumford, M. D. (2002). Divergent thinking, intelligence, and expertise: A test of alternative models. Creativity Research Journal, 14(2), 163–178.

    Google Scholar 

  • Wang, C. (2015). A Study of Membership Functions on Mamdani-Type Fuzzy Inference System For Industrial Decision-Making. A Thesis Presented to the Graduate and Research Committee of Lehigh University in Candidacy for the Degree of Masters of Science.

  • Walia, N., Singh, H., & Sharma, A. (2015). ANFIS: Adaptive neuro-fuzzy inference system-a survey. International Journal of Computer Applications, 123(13), 32–38.

    Google Scholar 

  • Wickes, K., & Ward, T. B. (2006). Measuring gifted adolescents’ implicit theories of creativity. Roeper Review, 28, 131–139.

    Google Scholar 

  • Wismath, S., Orr, D., & Zhong, M. (2014). Student perception of problem solving skills. Transformative Dialogues: Teaching and Learning Journal, 7(3), 1–17.

    Google Scholar 

  • Yavuz, G., Deringol, Y., & Arslan, Ç. (2017). Elementary school students perception levels of problem solving skills. Universal Journal of Educational Research, 5(11), 1896–1901.

    Google Scholar 

  • Yildiz, C., & Yildiz, T. G. (2021). Exploring the relationship between creative thinking and scientific process skills of preschool children. Thinking Skills and Creativity, 39, 100795.

    Google Scholar 

  • Zadeh, L. (1965). Fuzzy Sets. Information and Control, 338–353.

Download references

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Authors and Affiliations

Authors

Contributions

Conceptualization, S.G.K. and S.G.Y.; methodology, S.G.Y.; software, S.G.K.; validation, S.G.Y. and S.G.K.; formal analysis, S.G.Y. and S.G.K.; investigation, S.G.Y. and S.G.K.; resources, S.G.Y. and S.G.K.; data curation, S.G.Y. and S.G.K.; writing—original draft preparation, S.G.Y. and S.G.K.; writing—review and editing, S.G.Y. and S.G.K.; visualization, S.G.Y. and S.G.K.; supervision, S.G.Y. and S.G.K.; project administration, S.G.Y. and S.G.K.; funding acquisition, S.G.Y. and S.G.K. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Sevda Göktepe Yıldız.

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Appendix

Appendix

Student

Real scores

Artifical scores

S1

78

69

S2

96

91

S3

72

72

S4

73

71

S5

70

64

S6

67

67

S7

90

93

S8

44

44

S9

72

72

S10

68

68

S11

70

70

S12

67

67

S13

71

71

S14

78

78

S15

80

75

S16

69

69

S17

62

62

S18

80

80

S19

85

85

S20

77

59

S21

76

76

S22

74

73

S23

72

73

S24

76

66

S25

91

83

S26

95

85

S27

93

57

S28

72

72

S29

76

73

S30

64

64

S31

82

87

S32

98

87

S33

89

85

S34

95

65

S35

100

100

S36

89

84

S37

83

80

S38

92

72

S39

104

85

S40

82

82

S41

64

49

S42

89

81

S43

48

41

S44

80

78

S45

94

93

S46

90

90

S47

97

95

S48

93

93

S49

91

76

S50

98

85

S51

81

99

S52

53

52

S53

88

85

S54

96

78

S55

70

69

S56

73

82

S57

52

52

S58

51

51

S59

71

71

S60

72

71

S61

60

50

S62

61

61

S63

54

63

S64

50

44

S65

48

47

S66

57

54

S67

57

58

S68

56

55

S69

50

50

S70

72

72

S71

58

63

S72

47

47

S73

26

58

S74

51

63

S75

43

43

S76

32

55

S77

68

71

S78

52

53

S79

45

45

S80

31

43

S81

51

43

S82

60

86

S83

51

54

S84

30

30

S85

75

68

S86

37

52

S87

51

56

S88

56

40

S89

42

42

S90

41

41

S91

47

53

S92

44

44

S93

32

61

S94

22

53

S95

74

74

S96

39

38

S97

37

55

S98

50

57

S99

58

60

S100

48

50

S101

44

44

S102

62

62

S103

48

53

S104

47

47

S105

45

45

S106

27

26

S107

35

35

S108

43

42

S109

42

43

S110

53

54

S111

53

51

S112

61

52

S113

41

74

S114

52

61

S115

40

46

S116

40

60

S117

50

49

S118

57

60

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Göktepe Yıldız, S., Göktepe Körpeoğlu, S. Prediction of students’ perceptions of problem solving skills with a neuro-fuzzy model and hierarchical regression method: A quantitative study. Educ Inf Technol 28, 8879–8917 (2023). https://doi.org/10.1007/s10639-022-11446-1

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