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Niusha Bagheri, Margan Kian, Masoud Gramipour, Vaghar Ali Ali Azimi, Youssef Mahdavi Nesab,
Volume 11, Issue 3 (12-2024)
Volume 11, Issue 3 (12-2024)
Abstract
Objective: Virtual classes, virtual schools, smart schools, and virtual universities, and generally, electronic learning, are considered reliable capacities and capabilities for developing academic skills. The aim of this study is to evaluate the e-learning program at Kharazmi University using the HELAM conceptual model.
Method: This research is practical in terms of its objective and descriptive-survey in terms of method. A quantitative approach was used to collect data. The statistical population consisted of postgraduate students at Kharazmi University. The sample size was 536 postgraduate students, and stratified random sampling was used. A standardized researcher-made questionnaire was used for data collection. The main structure of the questionnaire is based on the HELAM model, along with an additional “overall satisfaction” factor, which was adapted and translated using specialized literature and relevant research. For data analysis, various statistical tests including one-sample t-test and one-way ANOVA in SPSS, and confirmatory factor analysis in R software were used.
Findings: The results indicated that the status of Kharazmi University’s e-learning program, evaluated using the HELAM conceptual model and its seven aspects (student attitude, instructor attitude, system quality, content quality, service quality, support issues, and overall satisfaction), is significantly above the community average with over 99% confidence. Moreover, the support issues aspect showed a significant difference compared to other dimensions, followed by content quality and service quality, which are close to each other and separated from other sub-scales, while system quality, instructor attitude, overall satisfaction, and student attitude have the lowest mean rankings.
Conclusion: Managers and experts at the Information and Communication Technology Center of Kharazmi University should take measures to improve system quality, instructor attitude, overall satisfaction, and student attitude aspects to enhance their performance in parallel with support issues.
Method: This research is practical in terms of its objective and descriptive-survey in terms of method. A quantitative approach was used to collect data. The statistical population consisted of postgraduate students at Kharazmi University. The sample size was 536 postgraduate students, and stratified random sampling was used. A standardized researcher-made questionnaire was used for data collection. The main structure of the questionnaire is based on the HELAM model, along with an additional “overall satisfaction” factor, which was adapted and translated using specialized literature and relevant research. For data analysis, various statistical tests including one-sample t-test and one-way ANOVA in SPSS, and confirmatory factor analysis in R software were used.
Findings: The results indicated that the status of Kharazmi University’s e-learning program, evaluated using the HELAM conceptual model and its seven aspects (student attitude, instructor attitude, system quality, content quality, service quality, support issues, and overall satisfaction), is significantly above the community average with over 99% confidence. Moreover, the support issues aspect showed a significant difference compared to other dimensions, followed by content quality and service quality, which are close to each other and separated from other sub-scales, while system quality, instructor attitude, overall satisfaction, and student attitude have the lowest mean rankings.
Conclusion: Managers and experts at the Information and Communication Technology Center of Kharazmi University should take measures to improve system quality, instructor attitude, overall satisfaction, and student attitude aspects to enhance their performance in parallel with support issues.
Farhad Fathi, Kourosh Fathi Vagargah, Esmaeil Jafari, Mojtaba Vahidi Asl,
Volume 11, Issue 3 (12-2024)
Volume 11, Issue 3 (12-2024)
Abstract
Businesses affected by digital transformations are facing new employee management and development needs. Employees in these companies not only need to acquire the right technical skills, but also have the mindset to help them cope with the new challenges of the digital workforce in the modern world. These changes and needs that are subsequently created in the development path lead to a digital transformation in the training of managers, as trainers and training professionals need to transition to new work forms to find, create and use digital tools to help future managers, companies and employees. The evolving literature of electronic human resource management expresses its challenges and potential. Stone et al. (2015) found that data-driven decision-making environments in the field of human interactions have a high ability to evaluate recruitment volunteers, improve staff levels, as well as provide digital tools for employee training and development. However, most studies in electronic Human Resource Management have concluded that more innovation is needed to improve the efficiency and performance of these digital tools.
In 2010, ifenthaler stated that in the not-too-distant future, when learners become active builders of their learning environments, setting individual goals and creating content structures for the knowledge and content they want to master, we may see the emergence of the true meaning of Constructivism (Ifenthaler, 2010) and that is now when eifenthaler mentioned it 12 years ago, and on this basis, the fundamental issue of research can be seen as the mismatch of the current situation.education and human resource development with new technologies. The digital age requires digital transformation in the most important context of humanity, the platform of teaching and learning. On the other hand, although the severity of the covid-19 pandemic has decreased and training has been resumed from the virtual platform, in the digital world and the volume of available data and the moment-to-moment updating of information, it is never possible to transfer them through face-to-face training. On the other hand, a person does not have the capacity to learn all the information and data available, so it is desirable that what he learns is based on his personal development, interests and expertise to make learning deeper and more effective. So this research seeks to address or adjust these issues to take a step towards improving the education and Human Resource Development situation in the country, and this will be achieved by designing a model of AI-based digital curriculum. To this end, the current research questions include:
1. What are the components of AI from the point of view of commentators?
2. What is the concept of digital curriculum from the point of view of commentators?
3. What are the coordinates of the AI-based digital curriculum model?
Methods and Materoal
Based on the purpose, the present research is applied, and in terms of data collection, it is a qualitative design. Among the various qualitative methods, the grounded theory method of the foundation was used with the constructivist approach of Charmaz. The current research community is all specialists in the field of curriculum, educational technology, educational technology and artificial intelligence, and the samples included 23 specialists. In order to collect information, semi-structured interview, observation and study of documents were used. In order to analyze the data in this research, the three-step method of Susanne Friese including noticing, collecting and thinking was done with the help of Atlas t.i software.
Resultss and Discussion
In 2010, ifenthaler stated that in the not-too-distant future, when learners become active builders of their learning environments, setting individual goals and creating content structures for the knowledge and content they want to master, we may see the emergence of the true meaning of Constructivism (Ifenthaler, 2010) and that is now when eifenthaler mentioned it 12 years ago, and on this basis, the fundamental issue of research can be seen as the mismatch of the current situation.education and human resource development with new technologies. The digital age requires digital transformation in the most important context of humanity, the platform of teaching and learning. On the other hand, although the severity of the covid-19 pandemic has decreased and training has been resumed from the virtual platform, in the digital world and the volume of available data and the moment-to-moment updating of information, it is never possible to transfer them through face-to-face training. On the other hand, a person does not have the capacity to learn all the information and data available, so it is desirable that what he learns is based on his personal development, interests and expertise to make learning deeper and more effective. So this research seeks to address or adjust these issues to take a step towards improving the education and Human Resource Development situation in the country, and this will be achieved by designing a model of AI-based digital curriculum. To this end, the current research questions include:
1. What are the components of AI from the point of view of commentators?
2. What is the concept of digital curriculum from the point of view of commentators?
3. What are the coordinates of the AI-based digital curriculum model?
Methods and Materoal
Based on the purpose, the present research is applied, and in terms of data collection, it is a qualitative design. Among the various qualitative methods, the grounded theory method of the foundation was used with the constructivist approach of Charmaz. The current research community is all specialists in the field of curriculum, educational technology, educational technology and artificial intelligence, and the samples included 23 specialists. In order to collect information, semi-structured interview, observation and study of documents were used. In order to analyze the data in this research, the three-step method of Susanne Friese including noticing, collecting and thinking was done with the help of Atlas t.i software.
Resultss and Discussion
- What are the components of AI from the point of view of commentators?
The components of artificial intelligence consisted of 5 Main and 19 sub-categories. These include charting systems (algorithm, phase logic, classification), learning systems (supervised learning ,unsupervised learning, hybrid knowledge - based systems, reinforcement learning, learning from incomplete data), semantic systems (self-learning, semantic similarity, natural language understanding, prediction), control of complex systems (dealing with nonlinear problems, expert system), neural network model (problem solving, optimization, flexibility, reasoning).
2. What is the concept of digital curriculum from the point of view of commentators?
The concept of digital curriculum has 6 Main and 33 sub-categories. These categories include digital curriculum objectives (increasing the capacity of program design by teachers, developing cognitive skills, meaningful learning experiences, participatory learning opportunities, educational dynamics, research-oriented, educational justice, self-learning), digital curriculum features (stable yet flexible, transforming learning into a lifelong process, balancing the learner and learning environment, using technology in the classroom, digital teaching culture, high compliance capacity), digital curriculum tools (educational games, digital laboratories, electronic libraries, simulators, environmental features of the digital curriculum (interactive, flexible, classroom Networking lessons, personalization of the learning environment), digital curriculum resources (Smart Textbooks,personalization of learning resources, web-based resources, open educational resources, textbooks), evaluation methods in the digital curriculum (online tests, video dialogue, video recorded by the learner, online critical texts, digital evaluation tools, quizzes).
3. What are the coordinates of the AI-based digital curriculum model?
phase curriculum model includes phase1 curriculum (learning based on specific pattern, classification and organization of content, linear learning, learning under external supervision, reinforcement learning and mutual understanding of language), phase2 curriculum (combined knowledge in learning, optimal building learning, learning from incomplete data, reasoning-based learning, predicting the learning process and facing learning problems) and phase3 curriculum (facing non-linear problems, deep learning, unsupervised learning, expertise in learning, semantic parallelism, self-directed learning and flexibility in learning).
Conclusion
Digital transformations have significantly changed teaching and learning practices. The present study examines the new needs of employee development and empowerment in the digital age, identifying the components of artificial intelligence and digital curriculum. The main objective of the present study is to define the components of artificial intelligence and then apply them in the form of digital curriculum elements. In other words, the digital curriculum in the workplace is defined by the components and functions of artificial intelligence.This model is designed based on the phase logic of artificial intelligence and can help to improve the design of the digital workplace curriculum. Based on the background studies, no research was found that could organize the digital workplace curriculum in this way, and therefore, the findings of the current research and the final output were completely unique.
2. What is the concept of digital curriculum from the point of view of commentators?
The concept of digital curriculum has 6 Main and 33 sub-categories. These categories include digital curriculum objectives (increasing the capacity of program design by teachers, developing cognitive skills, meaningful learning experiences, participatory learning opportunities, educational dynamics, research-oriented, educational justice, self-learning), digital curriculum features (stable yet flexible, transforming learning into a lifelong process, balancing the learner and learning environment, using technology in the classroom, digital teaching culture, high compliance capacity), digital curriculum tools (educational games, digital laboratories, electronic libraries, simulators, environmental features of the digital curriculum (interactive, flexible, classroom Networking lessons, personalization of the learning environment), digital curriculum resources (Smart Textbooks,personalization of learning resources, web-based resources, open educational resources, textbooks), evaluation methods in the digital curriculum (online tests, video dialogue, video recorded by the learner, online critical texts, digital evaluation tools, quizzes).
3. What are the coordinates of the AI-based digital curriculum model?
phase curriculum model includes phase1 curriculum (learning based on specific pattern, classification and organization of content, linear learning, learning under external supervision, reinforcement learning and mutual understanding of language), phase2 curriculum (combined knowledge in learning, optimal building learning, learning from incomplete data, reasoning-based learning, predicting the learning process and facing learning problems) and phase3 curriculum (facing non-linear problems, deep learning, unsupervised learning, expertise in learning, semantic parallelism, self-directed learning and flexibility in learning).
Conclusion
Digital transformations have significantly changed teaching and learning practices. The present study examines the new needs of employee development and empowerment in the digital age, identifying the components of artificial intelligence and digital curriculum. The main objective of the present study is to define the components of artificial intelligence and then apply them in the form of digital curriculum elements. In other words, the digital curriculum in the workplace is defined by the components and functions of artificial intelligence.This model is designed based on the phase logic of artificial intelligence and can help to improve the design of the digital workplace curriculum. Based on the background studies, no research was found that could organize the digital workplace curriculum in this way, and therefore, the findings of the current research and the final output were completely unique.
Soheila Shirezhian, Seyed Mehdi Mirmehdi,
Volume 12, Issue 1 (5-2025)
Volume 12, Issue 1 (5-2025)
Abstract
Introduction
In recent years, advancements in technology, particularly in artificial intelligence, have significantly transformed how customers interact with businesses. One of the most prominent manifestations of this transformation is the emergence of chatbots as intelligent digital agents in marketing and customer service. Chatbots are AI-powered programs capable of responding to user inquiries through text or voice interactions, playing a crucial role in enhancing the efficiency of customer-organization communication. These tools enable companies to provide 24/7 services, reduce response times, increase customer loyalty, and save human resources. Unlike human agents, chatbots are unaffected by factors such as fatigue or holidays, ensuring constant availability. However, traditional customer service channels like email, websites, or phone calls remain popular among some customers.
In the retail sector, chatbots facilitate effective customer-brand interactions by offering convenience, flexibility, and easy access. They streamline the online shopping process by providing quick responses and guiding users, creating a seamless and satisfying experience while addressing the impersonal nature of e-commerce. Recent advancements in natural language processing have enabled chatbots to perform complex tasks, such as analyzing customer preferences and delivering personalized responses. These capabilities, combined with the widespread use of messaging platforms, have driven the growth of the chatbot industry. Nevertheless, concerns like data security and privacy pose significant barriers to widespread adoption, requiring careful consideration from system designers. This study, grounded in the Technology Acceptance Model, examines factors such as trust, personal innovativeness, ease of use, social influence, and hedonic motivation to understand the reasons behind users’ acceptance or rejection of chatbots.
Methods and Materoal
This study adopts a quantitative approach with an applied objective, utilizing a descriptive-survey design. The target population consists of Iranian users with experience using AI-based chatbots in online customer service platforms, such as websites, apps, or messaging services. Inclusion criteria required participants to have used at least one service-oriented chatbot and to be familiar with digital tools. Exclusion criteria included incomplete questionnaires, lack of actual chatbot experience, or use of chatbots for non-customer-service purposes (e.g., entertainment or language learning). To enhance accuracy and minimize bias, the influence of the chatbot’s application domain (e.g., retail, banking, education, or healthcare) was analyzed using variance analysis and control of contextual variables.
Data were collected through three primary methods: documentary studies, electronic resources, and field research. The data collection tool was a questionnaire based on a 5-point Likert scale (ranging from “strongly disagree” to “strongly agree”), measuring variables such as trust, hedonic motivation, social influence, personal innovativeness, perceived usefulness, ease of use, attitude, and intention to use. The questionnaire was designed based on standardized scales from prior research, and its content validity was confirmed by experts.
Resultss and Discussion
The findings indicate that trust, personal innovativeness, and ease of use significantly influence the perceived usefulness of chatbots. Trust enhances perceived usefulness by providing accurate and prompt responses. Personal innovativeness strengthens this perception by aligning chatbots with users’ needs, while ease of use, by simplifying interactions, positively affects both perceived usefulness and users’ attitudes. Both perceived usefulness and positive attitudes directly increase the intention to use chatbots. However, social influence and hedonic motivation did not show a significant impact on perceived usefulness, possibly due to customers’ preference for traditional channels or the functional focus of chatbots over entertainment.
Conclusion
This study reveals that trust, personal innovativeness, and ease of use are critical drivers of chatbot adoption. Trust, fostered through reliable and swift responses, enhances the perception of chatbots’ usefulness. Personal innovativeness aligns chatbot functionalities with users’ creative needs, further boosting this perception. Ease of use simplifies interactions, fostering positive attitudes and increasing the intention to use chatbots. The lack of significant impact from social influence may stem from customers’ preference for traditional channels like email or phone calls. Similarly, hedonic motivation’s limited effect could be attributed to the service-oriented nature of chatbots, which prioritizes efficiency over enjoyment.
Chatbots, by automating routine tasks, offering predictive analytics, and enhancing customer experiences, serve as innovative tools in digital services. However, challenges such as data security and privacy concerns remain barriers to broader adoption. Designing user-friendly and trustworthy chatbots can enhance their acceptance and improve the digital customer experience. This study recommends further research on non-users and environmental factors that may hinder the impact of social influence and hedonic motivation to better understand adoption barriers.
In recent years, advancements in technology, particularly in artificial intelligence, have significantly transformed how customers interact with businesses. One of the most prominent manifestations of this transformation is the emergence of chatbots as intelligent digital agents in marketing and customer service. Chatbots are AI-powered programs capable of responding to user inquiries through text or voice interactions, playing a crucial role in enhancing the efficiency of customer-organization communication. These tools enable companies to provide 24/7 services, reduce response times, increase customer loyalty, and save human resources. Unlike human agents, chatbots are unaffected by factors such as fatigue or holidays, ensuring constant availability. However, traditional customer service channels like email, websites, or phone calls remain popular among some customers.
In the retail sector, chatbots facilitate effective customer-brand interactions by offering convenience, flexibility, and easy access. They streamline the online shopping process by providing quick responses and guiding users, creating a seamless and satisfying experience while addressing the impersonal nature of e-commerce. Recent advancements in natural language processing have enabled chatbots to perform complex tasks, such as analyzing customer preferences and delivering personalized responses. These capabilities, combined with the widespread use of messaging platforms, have driven the growth of the chatbot industry. Nevertheless, concerns like data security and privacy pose significant barriers to widespread adoption, requiring careful consideration from system designers. This study, grounded in the Technology Acceptance Model, examines factors such as trust, personal innovativeness, ease of use, social influence, and hedonic motivation to understand the reasons behind users’ acceptance or rejection of chatbots.
Methods and Materoal
This study adopts a quantitative approach with an applied objective, utilizing a descriptive-survey design. The target population consists of Iranian users with experience using AI-based chatbots in online customer service platforms, such as websites, apps, or messaging services. Inclusion criteria required participants to have used at least one service-oriented chatbot and to be familiar with digital tools. Exclusion criteria included incomplete questionnaires, lack of actual chatbot experience, or use of chatbots for non-customer-service purposes (e.g., entertainment or language learning). To enhance accuracy and minimize bias, the influence of the chatbot’s application domain (e.g., retail, banking, education, or healthcare) was analyzed using variance analysis and control of contextual variables.
Data were collected through three primary methods: documentary studies, electronic resources, and field research. The data collection tool was a questionnaire based on a 5-point Likert scale (ranging from “strongly disagree” to “strongly agree”), measuring variables such as trust, hedonic motivation, social influence, personal innovativeness, perceived usefulness, ease of use, attitude, and intention to use. The questionnaire was designed based on standardized scales from prior research, and its content validity was confirmed by experts.
Resultss and Discussion
The findings indicate that trust, personal innovativeness, and ease of use significantly influence the perceived usefulness of chatbots. Trust enhances perceived usefulness by providing accurate and prompt responses. Personal innovativeness strengthens this perception by aligning chatbots with users’ needs, while ease of use, by simplifying interactions, positively affects both perceived usefulness and users’ attitudes. Both perceived usefulness and positive attitudes directly increase the intention to use chatbots. However, social influence and hedonic motivation did not show a significant impact on perceived usefulness, possibly due to customers’ preference for traditional channels or the functional focus of chatbots over entertainment.
Conclusion
This study reveals that trust, personal innovativeness, and ease of use are critical drivers of chatbot adoption. Trust, fostered through reliable and swift responses, enhances the perception of chatbots’ usefulness. Personal innovativeness aligns chatbot functionalities with users’ creative needs, further boosting this perception. Ease of use simplifies interactions, fostering positive attitudes and increasing the intention to use chatbots. The lack of significant impact from social influence may stem from customers’ preference for traditional channels like email or phone calls. Similarly, hedonic motivation’s limited effect could be attributed to the service-oriented nature of chatbots, which prioritizes efficiency over enjoyment.
Chatbots, by automating routine tasks, offering predictive analytics, and enhancing customer experiences, serve as innovative tools in digital services. However, challenges such as data security and privacy concerns remain barriers to broader adoption. Designing user-friendly and trustworthy chatbots can enhance their acceptance and improve the digital customer experience. This study recommends further research on non-users and environmental factors that may hinder the impact of social influence and hedonic motivation to better understand adoption barriers.
Dr. Mohammad Moradi, ,
Volume 12, Issue 1 (5-2025)
Volume 12, Issue 1 (5-2025)
Abstract
In order to know whether the quality standards are being met, universities evaluate the educational quality of professors every semester using professor evaluation by students based on evaluation criteria determined by the Ministry of Science. However, it has never been investigated which of the criteria has had the greatest impact on increasing student interaction with professors and course content, and consequently increasing student learning and productivity. Also, methods such as Multiple Attribute Decision Making (MADM) techniques only measure the opinions of experts for each of the evaluation criteria, which may be in contradiction with reality. Therefore, the purpose of this study is to investigate the importance of each of the professor evaluation criteria related to student-professor interaction and course content based on students' performance and their average scores, as well as the results of professor evaluations by students. For this purpose, data mining techniques and regression models have been used. Also, a decision tree classification model has been presented to predict the academic status of students based on the characteristics of a professor.
Methods and Materials
The research method consists of 4 phases. In the first phase, the evaluation criteria for university professors related to student interaction with professors and course content were reviewed based on the items announced by the Ministry of Science. Then, in the second phase, data and information on the evaluation of professors by students and the average efficiency and grades of students were collected. In the third phase, the collected data were analyzed using data mining techniques and regression models, and the importance of each evaluation criteria was examined. In the fourth phase, a decision tree classification model was presented to predict the academic status of students according to the characteristics of the professor. The presented model will help professors and educational administrators determine teaching and classroom management methods to increase student interaction with professors and course content, and as a result, achieve the desired academic status of students.
Resultss and Discussion
Based on the results obtained, the evaluation criterion "having an appropriate lesson plan and comprehensiveness and continuity in presenting the material" with a coefficient of 28.907 had the greatest impact on increasing student interaction with professors and, as a result, increasing student productivity and grades. This emphasizes the need to use organization in teaching and learning, and the teacher should pay special attention to setting the lesson plan as planning and organizing the set of activities in relation to educational goals, lesson content, and students' abilities for the duration of the semester. The evaluation criterion "social manners and behavior with students and mutual respect" with a coefficient of 12.069 is the second factor affecting student efficiency. The evaluation criterion "classroom order and time management" with a coefficient of 11.597 is the third factor affecting student efficiency and scores. "Teacher's mastery of the subject matter" with a coefficient of 8.316 has been identified as the fourth factor affecting student efficiency and scores. The evaluation criterion "appropriateness of teaching strategies and methods to the course objectives" with a coefficient of 7.775 has been identified as the fifth factor affecting students' scores. The evaluation criterion "using appropriate student evaluation methods according to the course objectives" with a coefficient of 7.769 has been the sixth factor affecting students' average scores. "Possibility of communication (face-to-face and offline) with the professor outside the classroom" with a coefficient of 1.571 is the seventh factor affecting students' efficiency. Also, solutions were presented to strengthen the evaluation criterion with high weight and importance, namely the criterion "having an appropriate lesson plan and comprehensiveness and continuity in presenting the material".
Conclusion
The level of importance obtained for each evaluation criterion and the classification model created can help professors and educational administrators determine teaching and classroom management methods to increase student interaction with professors and course content, and as a result, increase their efficiency and average grades.
Methods and Materials
The research method consists of 4 phases. In the first phase, the evaluation criteria for university professors related to student interaction with professors and course content were reviewed based on the items announced by the Ministry of Science. Then, in the second phase, data and information on the evaluation of professors by students and the average efficiency and grades of students were collected. In the third phase, the collected data were analyzed using data mining techniques and regression models, and the importance of each evaluation criteria was examined. In the fourth phase, a decision tree classification model was presented to predict the academic status of students according to the characteristics of the professor. The presented model will help professors and educational administrators determine teaching and classroom management methods to increase student interaction with professors and course content, and as a result, achieve the desired academic status of students.
Resultss and Discussion
Based on the results obtained, the evaluation criterion "having an appropriate lesson plan and comprehensiveness and continuity in presenting the material" with a coefficient of 28.907 had the greatest impact on increasing student interaction with professors and, as a result, increasing student productivity and grades. This emphasizes the need to use organization in teaching and learning, and the teacher should pay special attention to setting the lesson plan as planning and organizing the set of activities in relation to educational goals, lesson content, and students' abilities for the duration of the semester. The evaluation criterion "social manners and behavior with students and mutual respect" with a coefficient of 12.069 is the second factor affecting student efficiency. The evaluation criterion "classroom order and time management" with a coefficient of 11.597 is the third factor affecting student efficiency and scores. "Teacher's mastery of the subject matter" with a coefficient of 8.316 has been identified as the fourth factor affecting student efficiency and scores. The evaluation criterion "appropriateness of teaching strategies and methods to the course objectives" with a coefficient of 7.775 has been identified as the fifth factor affecting students' scores. The evaluation criterion "using appropriate student evaluation methods according to the course objectives" with a coefficient of 7.769 has been the sixth factor affecting students' average scores. "Possibility of communication (face-to-face and offline) with the professor outside the classroom" with a coefficient of 1.571 is the seventh factor affecting students' efficiency. Also, solutions were presented to strengthen the evaluation criterion with high weight and importance, namely the criterion "having an appropriate lesson plan and comprehensiveness and continuity in presenting the material".
Conclusion
The level of importance obtained for each evaluation criterion and the classification model created can help professors and educational administrators determine teaching and classroom management methods to increase student interaction with professors and course content, and as a result, increase their efficiency and average grades.
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