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Showing 3 results for Data Mining
Dr Shahnaz Khademizadeh, Mrs Zeinab Mohammadi,
Volume 9, Issue 2 (9-2022)
Volume 9, Issue 2 (9-2022)
Abstract
Purpose: Study aimed to identify the applications of data mining in the provision of services, collection and management of digital libraries.
Methodology: This is an applied study in terms of purpose and in terms of method is qualitative research that have been done by systematic review method. For this purpose, articles have been obtained by searching databases of Springer, Emerald, ProQuest, Web of Science, Google Scholar, Science Direct, and Semantic Scholar.
Articles published between 2000 and 2021 have been scrutinized. The systematic review model of Kitchenham and Charter (2007) was surveyed. According to the inclusion criteria, 1296 articles have been extracted after initial refinement, and among them, 77 articles related to the subject have been identified by reviewing the titles of articles and entered the final review by reviewing the full text. In conclusion, 29 articles were chosen for final analysis. The Qualitative content- coding method was used for data analysis and qualitative analysis was performed by two coders. The agreement of the evaluators based on the formula of Miles and Haberman for the performed analyzes, 78.5 was obtained.
Findings: Based on the results of qualitative analysis, 74 basic, 13 organizing and 3 comprehensive themes of "digital services,” “digital library management" and "digital collection" have been identified, which in total define the application of data mining in digital libraries represented.
Conclusion: Using data mining techniques in digital libraries, a variety of information can be stored seamlessly in different classes so that the end user of the information could meet their information needs in the shortest possible time. On the other hand, libraries can provide more useful resources by analyzing their users' information interests, and this can be considered a turning point in situations where libraries are facing financial difficulties.
Dr. Mohammad Moradi,
Volume 11, Issue 3 (12-2024)
Volume 11, Issue 3 (12-2024)
Abstract
Social networks and their increasing influence among different users in all parts of the world have made these networks become suitable tools for advertising and e-commerce. Today, businesses have come to understand that social networks are and will continue to be a means of doing business. Instagram is a popular social network based on video and images. This social network is known as one of the powerful marketing tools. The number of views, likes and comments on social networks, including Instagram, plays a significant role in customer decision-making; Because they pay attention to the opinions and reception of other audiences towards that product or post and are influenced. This research analyzes what factors create posts with different levels of popularity. For this purpose, the factors affecting the number of views, likes and comments in an Instagram social network post are extracted and their weight and importance are calculated based on the regression model. Finally, the decision tree model is presented for forecasting and management in order to increase the number of visits, likes and comments.
Methods and Materoal
In this research, the type of research is based on the purpose of applied research. At first, library studies have been used in order to extract factors affecting the amount of visits, likes and comments in Instagram social network marketing posts. The statistical population includes all articles related to the factors affecting visits, likes and comments. The probability sampling method of simple random samples has been used and 30 articles in this field have been reviewed. Then, the data related to the factors identified from the previous stage have been extracted from the pages of big marketers on the Instagram social network. Then, using the extracted data and using the regression model, the weight and importance of each factor affecting the number of visits, likes, and comments of Instagram social network marketers' posts has been calculated. Finally, a decision tree model has been created to predict the status (rate of visits, likes and comments) of a marketing post on the Instagram social network based on the characteristics of that post.
Resultss and Discussion
Directly, factors such as the number of posts, the number of followers, the type of post, the content of the post and the time of the post are potential factors that affect the number of views, likes and comments. According to the obtained results, the "post content with survey" factor with a positive sign and a coefficient of 420,290.616 had the most positive effect on the label, which is the number of visits to a post. The factor "discount post content" with a positive sign and a coefficient of 5417.751 has had the most positive effect on the label, which is the liking of a post. The factor "discount post content" with a positive sign and a coefficient of 2164.016 has had the most positive effect on the label, which is the amount of comments on a post. Also, the type of image post with a regression coefficient of 565.153 and a negative sign in the investigation of factors affecting the number of comments shows that the use of video posts will increase the comments and interaction of customers.
Conclusion
Most of the researches conducted, such as Gkikas et al (2022), Torbarina, Jelenc & Brkljačić (2020), Wahid & Gunarto (2022), etc., only investigated the influence of a few specific factors on the likes and comments of social media posts, and a comprehensive set of factors has not been investigated. Also, these factors were only for checking likes or opinions and not checking both cases. Most importantly, in the studies conducted, only the positive or negative impact of a factor on the number of likes and opinions has been discussed, and their importance has not been determined. In this research, various factors affecting the number of visits, likes and comments of social network posts were investigated. Also, the importance of each factor was determined. In addition, a decision tree model was presented to manage related pages and posts in order to achieve increased likes and comments. Based on the extracted effective factors, calculating the weight and importance of each factor and the created decision tree model, posts can be managed to increase the number of visits, likes and comments
Methods and Materoal
In this research, the type of research is based on the purpose of applied research. At first, library studies have been used in order to extract factors affecting the amount of visits, likes and comments in Instagram social network marketing posts. The statistical population includes all articles related to the factors affecting visits, likes and comments. The probability sampling method of simple random samples has been used and 30 articles in this field have been reviewed. Then, the data related to the factors identified from the previous stage have been extracted from the pages of big marketers on the Instagram social network. Then, using the extracted data and using the regression model, the weight and importance of each factor affecting the number of visits, likes, and comments of Instagram social network marketers' posts has been calculated. Finally, a decision tree model has been created to predict the status (rate of visits, likes and comments) of a marketing post on the Instagram social network based on the characteristics of that post.
Resultss and Discussion
Directly, factors such as the number of posts, the number of followers, the type of post, the content of the post and the time of the post are potential factors that affect the number of views, likes and comments. According to the obtained results, the "post content with survey" factor with a positive sign and a coefficient of 420,290.616 had the most positive effect on the label, which is the number of visits to a post. The factor "discount post content" with a positive sign and a coefficient of 5417.751 has had the most positive effect on the label, which is the liking of a post. The factor "discount post content" with a positive sign and a coefficient of 2164.016 has had the most positive effect on the label, which is the amount of comments on a post. Also, the type of image post with a regression coefficient of 565.153 and a negative sign in the investigation of factors affecting the number of comments shows that the use of video posts will increase the comments and interaction of customers.
Conclusion
Most of the researches conducted, such as Gkikas et al (2022), Torbarina, Jelenc & Brkljačić (2020), Wahid & Gunarto (2022), etc., only investigated the influence of a few specific factors on the likes and comments of social media posts, and a comprehensive set of factors has not been investigated. Also, these factors were only for checking likes or opinions and not checking both cases. Most importantly, in the studies conducted, only the positive or negative impact of a factor on the number of likes and opinions has been discussed, and their importance has not been determined. In this research, various factors affecting the number of visits, likes and comments of social network posts were investigated. Also, the importance of each factor was determined. In addition, a decision tree model was presented to manage related pages and posts in order to achieve increased likes and comments. Based on the extracted effective factors, calculating the weight and importance of each factor and the created decision tree model, posts can be managed to increase the number of visits, likes and comments
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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