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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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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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 |
S119 | 66 | 65 |
S120 | 44 | 43 |
S121 | 57 | 78 |
S122 | 26 | 37 |
S123 | 58 | 61 |
S124 | 37 | 87 |
S125 | 37 | 37 |
S126 | 47 | 48 |
S127 | 57 | 47 |
S128 | 59 | 59 |
S129 | 31 | 58 |
S130 | 41 | 43 |
S131 | 56 | 45 |
S132 | 45 | 50 |
S133 | 76 | 49 |
S134 | 62 | 61 |
S135 | 38 | 38 |
S136 | 60 | 60 |
S137 | 62 | 62 |
S138 | 42 | 63 |
S139 | 24 | 45 |
S140 | 57 | 57 |
S141 | 43 | 47 |
S142 | 41 | 64 |
S143 | 33 | 39 |
S144 | 37 | 37 |
S145 | 38 | 34 |
S146 | 32 | 34 |
S147 | 46 | 45 |
S148 | 37 | 47 |
S149 | 28 | 27 |
S150 | 38 | 40 |
S151 | 22 | 59 |
S152 | 43 | 54 |
S153 | 32 | 32 |
S154 | 52 | 52 |
S155 | 28 | 41 |
S156 | 104 | 10 |
S157 | 48 | 57 |
S158 | 38 | 50 |
S159 | 51 | 45 |
S160 | 59 | 59 |
S161 | 62 | 53 |
S162 | 50 | 51 |
S163 | 41 | 51 |
S164 | 54 | 54 |
S165 | 73 | 62 |
S166 | 49 | 48 |
S167 | 36 | 46 |
S168 | 42 | 69 |
S169 | 54 | 53 |
S170 | 47 | 46 |
S171 | 26 | 26 |
S172 | 45 | 45 |
S173 | 73 | 73 |
S174 | 64 | 63 |
S175 | 92 | 92 |
S176 | 62 | 62 |
S177 | 54 | 58 |
S178 | 57 | 58 |
S179 | 66 | 66 |
S180 | 48 | 53 |
S181 | 78 | 69 |
S182 | 96 | 90 |
S183 | 72 | 72 |
S184 | 73 | 70 |
S185 | 70 | 64 |
S186 | 67 | 67 |
S187 | 90 | 93 |
S188 | 44 | 44 |
S189 | 72 | 71 |
S190 | 68 | 68 |
S191 | 70 | 70 |
S192 | 67 | 67 |
S193 | 71 | 71 |
S194 | 78 | 78 |
S195 | 80 | 75 |
S196 | 69 | 69 |
S197 | 62 | 62 |
S198 | 80 | 80 |
S199 | 85 | 85 |
S200 | 77 | 59 |
S201 | 76 | 76 |
S202 | 74 | 74 |
S203 | 72 | 73 |
S204 | 76 | 66 |
S205 | 91 | 83 |
S206 | 95 | 85 |
S207 | 93 | 57 |
S208 | 72 | 72 |
S209 | 76 | 73 |
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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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DOI: https://doi.org/10.1007/s10639-022-11446-1