Artificial Intelligence
Marzieh Keramati nojedeh sadat; milad chabok
Abstract
Background and Objectives: The theory of multiple intelligences proposed by Howard Gardner plays a significant role in the education and learning processes of students. Recognizing the various components of this theory and considering individual differences among students can enhance the teaching and ...
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Background and Objectives: The theory of multiple intelligences proposed by Howard Gardner plays a significant role in the education and learning processes of students. Recognizing the various components of this theory and considering individual differences among students can enhance the teaching and learning process, ultimately leading to improved academic performance. However, the role of the teacher alone is not sufficient; students themselves must also be diligent in their study habits. Therefore, it is essential to focus on various skills, including the adaptation of study and learning strategies among students. This study investigates the relationship between Gardner's multiple intelligences and study and learning strategies, as well as their impact on academic performance, utilizing a perceptron neural networkMethods: This research is applied in purpose and descriptive in nature, specifically correlational. The statistical population consists of 1,737 secondary school students majoring in Experimental Sciences in Baft in the academic year 2023-2024. A random sampling method was employed to select a sample of 159 male students. Data were collected using two questionnaires: the Gardner Multiple Intelligences Questionnaire and the Weinstein Study and Learning Strategies Questionnaire. Additionally, the students' academic performance was assessed. The content and face validity of the questionnaires were determined by five professors from the University of Teacher Education and secondary education teachers. The reliability of the Gardner Multiple Intelligences Questionnaire was confirmed with a Cronbach's alpha coefficient of 0.71, while the reliability of the Study and Learning Strategies Questionnaire was 0.76. Python was used to employ a perceptron neural network for determining the relationships between the variables..Findings: The results indicated that both the neural network model (with a coefficient of determination of 0.97) and the linear regression model (with a coefficient of determination of 0.99) demonstrated a significant positive relationship between the components of Gardner's multiple intelligences and study and learning strategies with academic performance. Both models exhibited high predictive capabilities. Regarding Gardner's multiple intelligences in relation to academic performance, the interpersonal intelligence component exhibited the highest feature importance (15.8), while intrapersonal intelligence showed the lowest feature importance (12.3). In terms of the study and learning strategies variable, the anxiety component had the greatest feature importance (14.8), whereas the main idea selection component had the lowest feature importance (8.8). Compared to other components of multiple intelligences and study and learning strategies, these features had the most significant influence on predicting students' academic performance scores in the model.Conclusion: The application of Gardner's multiple intelligences theory in classrooms offers numerous advantages to the educational system. Educators can create inclusive learning environments that recognize individual skills, including study and learning strategies. By understanding and integrating various intelligences, they can facilitate comprehensive educational development. Moreover, artificial intelligence plays a significant role in education, particularly through applications that predict students' academic performance based on personal information such as socioeconomic status, income, address, and more. These applications can propose and develop artificial neural network models. Consequently, by considering the different types of Gardner's multiple intelligences and the significance of each study and learning strategy, educators can utilize predictive neural network models to understand the impact of various components on students' academic performance. Therefore, it is recommended that schools and educational institutions pay special attention to improving and enhancing these aspects.
Educational Technology - Artificial Intelligence
Vi. Gohari; M. Keramati Nojedeh sadat; F. Ramezaivishki
Abstract
Background and Objectives: Education is one of the main parts of knowledge and science production in which teachers contribute to the scientific and cultural progress of any country. Inefficiency in education and training can lead to challenges and widespread problems of social, cultural, scientific, ...
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Background and Objectives: Education is one of the main parts of knowledge and science production in which teachers contribute to the scientific and cultural progress of any country. Inefficiency in education and training can lead to challenges and widespread problems of social, cultural, scientific, political, religious dimensions in society. Therefore, it seems necessary to assess the competence of student teachers in terms of knowledge, skills, and attitudes especially before entering the professional field of teaching. In this research, the competency of student teachers before entering education and training was evaluated using artificial intelligence neural network as a precise computing tool.Methods: In the present study, the research method is applied and quantitative. The statistical population consisted of students of Farhangian University, and the sample included 91 teacher students who were selected via cluster sampling in 2015-2016 in the field of Biology Education of Shahid Beheshti Higher Education Center of Farhangian University. A corpus of 500 data (80% training and 20% test) was formed based on the scores of general, educational, specialized educational, theoretical, research, internship, and total average courses. The findings were assessed using the feedforward neural network method. According to the investigations carried out on the layers and the number of neurons on the data of the algorithm, a three-layer neural network was designed with two hidden layers with a number of 300 neurons and an output layer with a number of 1 neuron.Findings: The results of the examination of professional competence are based on the scores of student teachers and the extraction of new data of the seven dimensions of theoretic- specialization, education- specialization, education- practice, general, internship, research, and total average, which show the highest average with a score of 19.8 in the educational courses and the lowest average with a score of 16.67 was in theory-specialized courses. The analysis of the findings according to the educational data of the graduates and the labeling of experts showed the level of competence as 17.77.Conclusion: These results indicate that curriculum planners should pay more attention to specialized-theory courses because teachers must have the appropriate and sufficient knowledge and scientific level to present scientific materials to students in addition to learning educational lessons. Besides, it is suggested to evaluate the competence coefficient in other studies by using special questionnaires based on students' attitudes toward the teaching profession. On the other hand, the student's handwriting can be examined and studied as another indicator. Also, the neural network model of artificial intelligence should be used to determine the competence of student teachers in other fields of basic sciences and humanities.
