AI-Driven Predictions of Mathematical Problem-Solving Beliefs: Fuzzy Logic, Adaptive Neuro-Fuzzy Inference Systems, and Artificial Neural Networks
Considering that creative thinkers are individuals who can think outside of the box, exhibit original thoughts, and demonstrate problem-solving skills, it is likely that there is a relationship between mathematical problem-solving beliefs (MPSBs) and creative thinking dispositions (CTDs). This study...
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MDPI AG
2025-01-01
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| author | Seda Göktepe Körpeoğlu Ahsen Filiz Sevda Göktepe Yıldız |
| author_facet | Seda Göktepe Körpeoğlu Ahsen Filiz Sevda Göktepe Yıldız |
| author_sort | Seda Göktepe Körpeoğlu |
| collection | DOAJ |
| description | Considering that creative thinkers are individuals who can think outside of the box, exhibit original thoughts, and demonstrate problem-solving skills, it is likely that there is a relationship between mathematical problem-solving beliefs (MPSBs) and creative thinking dispositions (CTDs). This study aimed to predict teachers’ MPSBs with their CTDs and some demographic features. Three different artificial intelligence models (fuzzy logic, artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS)) were developed, and artificial data were obtained. The inputs of the research were determined as CTD, gender, age, educational level, school level, and teaching experiences, and the output was determined as MPSBs. Afterward, whether there was a relationship between real and artificial results was examined with statistical analysis. The research results show that there is a statistically significant, positive, and moderate relationship between artificial ANN MPSB scores and real MPSB scores (r = 0.422; <i>p</i> < 0.05), as well as artificial ANFIS MPSB scores and real MPSB scores (r = 0.564; <i>p</i> < 0.05). These results are important sources of evidence indicating that artificial intelligence methods accurately predict teachers’ MPSB scores. |
| format | Article |
| id | doaj-art-74c66675c48849b3bbd3128449b5b8a3 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-74c66675c48849b3bbd3128449b5b8a32025-01-24T13:19:34ZengMDPI AGApplied Sciences2076-34172025-01-0115249410.3390/app15020494AI-Driven Predictions of Mathematical Problem-Solving Beliefs: Fuzzy Logic, Adaptive Neuro-Fuzzy Inference Systems, and Artificial Neural NetworksSeda Göktepe Körpeoğlu0Ahsen Filiz1Sevda Göktepe Yıldız2Department of Mathematical Engineering, Yildiz Technical University, Davutpasa Campus, 34220 Istanbul, TurkeyDepartment of Mathematics and Science Education, Biruni University, 34015 Istanbul, TurkeyDepartment of Mathematics and Science Education, Biruni University, 34015 Istanbul, TurkeyConsidering that creative thinkers are individuals who can think outside of the box, exhibit original thoughts, and demonstrate problem-solving skills, it is likely that there is a relationship between mathematical problem-solving beliefs (MPSBs) and creative thinking dispositions (CTDs). This study aimed to predict teachers’ MPSBs with their CTDs and some demographic features. Three different artificial intelligence models (fuzzy logic, artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS)) were developed, and artificial data were obtained. The inputs of the research were determined as CTD, gender, age, educational level, school level, and teaching experiences, and the output was determined as MPSBs. Afterward, whether there was a relationship between real and artificial results was examined with statistical analysis. The research results show that there is a statistically significant, positive, and moderate relationship between artificial ANN MPSB scores and real MPSB scores (r = 0.422; <i>p</i> < 0.05), as well as artificial ANFIS MPSB scores and real MPSB scores (r = 0.564; <i>p</i> < 0.05). These results are important sources of evidence indicating that artificial intelligence methods accurately predict teachers’ MPSB scores.https://www.mdpi.com/2076-3417/15/2/494fuzzy logicANFISANNbeliefs about mathematical problem solvingcreative thinking dispositions |
| spellingShingle | Seda Göktepe Körpeoğlu Ahsen Filiz Sevda Göktepe Yıldız AI-Driven Predictions of Mathematical Problem-Solving Beliefs: Fuzzy Logic, Adaptive Neuro-Fuzzy Inference Systems, and Artificial Neural Networks Applied Sciences fuzzy logic ANFIS ANN beliefs about mathematical problem solving creative thinking dispositions |
| title | AI-Driven Predictions of Mathematical Problem-Solving Beliefs: Fuzzy Logic, Adaptive Neuro-Fuzzy Inference Systems, and Artificial Neural Networks |
| title_full | AI-Driven Predictions of Mathematical Problem-Solving Beliefs: Fuzzy Logic, Adaptive Neuro-Fuzzy Inference Systems, and Artificial Neural Networks |
| title_fullStr | AI-Driven Predictions of Mathematical Problem-Solving Beliefs: Fuzzy Logic, Adaptive Neuro-Fuzzy Inference Systems, and Artificial Neural Networks |
| title_full_unstemmed | AI-Driven Predictions of Mathematical Problem-Solving Beliefs: Fuzzy Logic, Adaptive Neuro-Fuzzy Inference Systems, and Artificial Neural Networks |
| title_short | AI-Driven Predictions of Mathematical Problem-Solving Beliefs: Fuzzy Logic, Adaptive Neuro-Fuzzy Inference Systems, and Artificial Neural Networks |
| title_sort | ai driven predictions of mathematical problem solving beliefs fuzzy logic adaptive neuro fuzzy inference systems and artificial neural networks |
| topic | fuzzy logic ANFIS ANN beliefs about mathematical problem solving creative thinking dispositions |
| url | https://www.mdpi.com/2076-3417/15/2/494 |
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