Using artificial intelligence to predict students’ STEM attitudes
An adaptive neural-network-based fuzzy logic model
Bibliographic Data
| ID | 21589548 |
|---|---|
| Authors | Seda Göktepe Körpeoğlu (0000-0001-7146-0846, Faculty of Chemical and Metallurgical Engineering, Department of Mathematical Engineering, Yildiz Technical University, İstanbul, Turkey, corresponding author), Sevda Göktepe Yıldız (0000-0002-0573-7904, Faculty of Education, Department of Mathematics and Science Education, Biruni University, İstanbul, Turkey) |
| Year | 2024 |
| Volume | 46 |
| Issue | 10 |
| Pages | 1001-1026 |
| Publication date | 2024-07-02 |
| Open Access | No |
| Type | ARTICLE |
| Venue | International Journal of Science Education, Part B (JOURNAL) |
| Journal identifiers | ISSN: 2154-8455 • E-ISSN: 2154-8463 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/09500693.2023.2269291 |
| OpenAlex | W4388084762 |
| Language | EN |
| References cited | 63 |
Numerous artificial intelligence methods have lately been applied in education. This study proposes an Adaptive Neural-network-based Fuzzy Logic (ANFIS) model combining fuzzy logic and artificial neural networks for predicting students' STEM attitudes. The inputs of the research were determined as grade levels and academic achievement scores, and the output was determined as STEM attitudes. The hybrid optimisation method trained the fuzzy inference system (FIS) to recognise the more effective ANFIS model. Afterward, the effects of the input variables on the output variable were examined with statistical techniques. Finally, the students' artificial and real STEM attitude scores were compared. 600 middle school students participated in the study, and data were gathered using a Personal Information Form and STEM Attitude Scale. The study revealed a significant positive correlation (r = 0.298; p = 0.000) between the generated ANFIS scores and real scores, the real and artificial scores didn't indicate a statistically significant difference. Therefore, the results obtained through ANFIS correctly predict students' STEM attitude scores. The grade level and academic achievement variables showed statistically significant differences in STEM attitudes. This study is a concrete example as it shows that it is possible to know some characteristics of students using artificial intelligence
Academic achievement · Adaptive neuro fuzzy inference system · Artificial neural network · Computational intelligence · Correlation · Fuzzy control system · Fuzzy logic · Inference system · Machine learning · Mathematics education · Neuro-fuzzy · Computer Science · Educational Technology and Assessment · Mathematics · Online Learning and Analytics · Psychology · Technology-Enhanced Education Studies · Artificial Intelligence
Qualitative evaluation and research methods
Using Robotics and Game Design to Enhance Children’s Self-Efficacy, STEM Attitudes, and Computational Thinking Skills
What Is STEM? A Discussion About Conceptions of STEM in Education and Partnerships
The Explanatory and Predictive Scope of Self-Efficacy Theory
Fuzzy sets
Career Development and Counseling
Examination of Middle School Students’ Interests in Stem Professions Using Educational Data Mining
STEM education in academic achievement
Middle school students’ motivational dispositions and interest in STEM careers
STEM learning attitude predicts computational thinking skills among primary school students
Math and Science Attitudes and Achievement at the Intersection of Gender and Ethnicity
A Social Learning Theory of Career Selection
| Highly cited | No |
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