Predicting the Impact of Motivational Beliefs of Engineering and Technical Students on Academic Progress in Mathematics Using Neural Networks
DOI:
https://doi.org/10.6093/2284-0184/11576Keywords:
Engineering education, motivational beliefs, neural networks, academic progressAbstract
The present study is a cross-sectional-analytical study. In this study, 107 male and female students majoring in technical and engineering fields from selected universities in Tehran participated. Sampling was conducted in two simple random stages during the first semester of the academic year 2022-2023. The Pentrich Motivational Questionnaires were distributed online and completed by the students. Data were analyzed using neural networks and the MATLAB software.
Among the components of academic motivation, intrinsic valuing is the most important and influential factor in students' academic progress. Another factor that can significantly impact student motivation is goal orientation, while self-efficacy has a very limited effect on motivation, and exam anxiety does not have any influence on student motivation.
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