پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 30 |
| تعداد شماره‌ها | 289 |
| تعداد مقالات | 2,820 |
| تعداد مشاهده مقاله | 3,301,322 |
| تعداد دریافت فایل اصل مقاله | 4,374,472 |
Feasibility study of establishing knowledge management in schools of Dorud city using data mining techniques | ||
| School Administration | ||
| مقاله 2، دوره 8، شماره 2، مرداد 2020، صفحه 40-21 اصل مقاله (1.16 M) | ||
| نوع مقاله: Mixed Method Research Paper | ||
| شناسه دیجیتال (DOI): https://doi.org/10.34785/J010.2020.428 | ||
| نویسندگان | ||
| علی بهلولی1؛ منصور اسماعیل پور* 2؛ علیرضا اسالمبولچی3 | ||
| 1دانشجوی دکتری مدیریت فناوری اطلاعاات، دانشگاه آزاد اسلامی واحد همدان، ،همدان، ایران | ||
| 2استادیار مهندسی کامپیوتر، دانشگاه آزاد اسلامی واحد همدان، همدان، ایران | ||
| 3استادیار مدیریت، دانشگاه آزاد اسلامی واحد همدان، همدان، ایران | ||
| چکیده | ||
| Some models of confirmation of Knowledge management have used successfully in industrials, but as we know the features of educational organization such as a schools are different from industrials now, the question is that; is Possible to confirm the Knowledge management at schools? At last, solving this time by Deans of schools is an important issue in Education organ. This study is an applicable Survey, and is an analytical-descriptive in method to analysis the data used in the Data mining in this research. The related variants to predisposing factors of confirmation of Knowledge management are: the culture of organization and the process of Knowledge and information Technology the validity of process Control. The questions are presented to some faculty members after editing, then Some of them which are included the problem were reviewed after reform supposing till the deletion of unsatisfied questions after that they were replaced by new others, so the external validity of the questionnaire was - Detected. at the reliability of process Control. The studying of reliability of Test was held by Alfa Kron Batch methods and also by making halers. The reliability coefficient of the questionnaire is 92% which is a great one and by making halves we received to 70%, also is accepted. Finally, the questionnaire was planned with 35 questions in 5 multiples form which is said included three variables (the system of Knowledge processing, The information technology system, and the cultural organization), then distributed between the managers. Population The population of this survey includes the Deans of schools in Dorud’s education which are 116 people then selected 89 of them based on Morgan Table. Using the algorithm of data mining and assessing the algorithms The decision tree, Baye's theorem and artificial Neural network were used in this survey, then had continued by confusion matrix which is one of the most important assessing features of casting -algorithm. Decision tree Producing the rules using the decision tree. After Inserting the preprocessing data in weka Software, then the rules below were produced by using the decision tree algorithm. Rule1: if the union accelerates less or lesser The Knowledge sharing for employees and the managers assent with their proposal more or less, it will be confirmed by the knowledge management. Rule 2: if the union accelerated. Less or lesser the Knowledge sharing for employees and the manager’s assent with their proposals more and much more, it will be confirmed the knowledge management. Rule 3: if the union accelerates more and more the knowledge sharing for employees and the managers would be less and lesser Sensitive to reserve their knowledge workers, then it will not be confirmed by the Knowledge management. Rule 4: if the union accelerates more and more the knowledge Sharing for employees and the managers would be more and more sensitive to reserve their knowledge workers, then it will be confirmed the knowledge management. The results were analyzed by using the efficiency features of the decision tree algorithm then to produce the below matrix by it. The Confusion matrix of decision Tree FP=1 TP=7 TN=10 FN=0 After executing the decision Tree, the concentration of casting would be 0.94. The theory of Rough set function To extract the rules by this method using the Genetic, Johnson and Holts algorithms. Genetic algorithm other produced 4100 rules, Johnson algorithm will be produced 18 rules and Holts algorithm will be produced 191 rules, and finally the most efficient rules will be presented among them. Rule 1# if the managers are more Sensitive to reserve their Knowledge workers and the Union accelerates more or less the knowledge sharing for their employees then, it will be confirmed knowledge management. Rule#2: if the managers are almost sensitive to reserve their knowledge workers and the union accelerates more or less the Knowledge-sharing for their employees then, it will be confirmed knowledge management. Rule 3# if the managers are less sensitive to reserve their knowledge workers and the union accelerates lesser the Knowledge-sharingfor their employees then, it will not be confirmed knowledge management. The Confusion matrix of decision Tree FP=1 TP=8 TN=2 FN=7 After executing the Rough Set Model, the concentrate of this algorithm would be 0.88. According to the extracted rules from Genetic, Johnson, and Holts algorithm in Rough set model and decision Tree to receive the General and Creditable rules from these four rules are Compound and finally made rules below. The final Rule 1: if the Union accelerates almost or more the knowledge-sharing for their employees and the managers are more and much more sensitive to reserve their knowledge workers, then it will be confirmed knowledge management. The final rule 2: if the union accelerates less Knowledge Sharing for their employees and the managers assent more and much more with their proposals, then it will be confirmed knowledge management. The final rules 3: if the managers are less sensitive to reserve their knowledge workers and the union accelerates much less Knowledge-sharing for their employees, then it will not be confirmed knowledge management. The final Rule 4: If the managers are almost sensitive to reserve their knowledge workers and the union accelerates much less Knowledge sharing for their employees Then, it will not be confirmed knowledge management. Final Rule 5: if the managers are more sensitive to reserve their Knowledge workers and the union -accelerates almost the knowledge Sharing for their employees then it will be confirmed knowledge management. Final Rule 6# if the managers are almost sensitive to reserve their Knowledge workers and the union accelerates much more knowledge sharing for their employees, and then it will be confirmed Knowledge management. Generally, according to the final findings in this survey which are held by data mining algorithm, the key features have an important role in conforming the knowledge a management are: Knowledge sharing, reserving the knowledge workers and using the employee's proposals. | ||
| کلیدواژه‌ها | ||
| knowledge management؛ Data Mining؛ Information Technology system؛ knowledge system | ||
| مراجع | ||
|
Ashrafi, A., & Mahdavi Kheiri, M. (2017). Investigating the Factors Affecting Knowledge Management in Elementary Schools (Case Study of Pre-school Teachers in Qazvin). 8th National Conference and Second International Conference on Knowledge Management. Tehran Oil, Gas and Petrochemical Information Institute. [in Persian]. Azad Shahraki, Z. (2009). Investigating the Factors of Knowledge Management in Bushehr Education Organization. MSc Thesis. Islamic Azad University of Bushehr. [in Persian]. Azam, M., & Mohammadi, S. (2018). Investigating the Impact of Innovation on Knowledge Management Implementation from the Perspective of University Staff (Case Study of Kermanshah University of Technology). National Congress of Fundamental Research in Computer Engineering and Information Technology. Shahid Beheshti University. Tehran. Permanent Secretariat Conference. [in Persian]. Chatterjee, S. (2014). Managing constraints and removing obstacles to knowledge management. Donate, M. J., & de Pablo, J. D. S. (2015). The role of knowledge-oriented leadership in knowledge management practices and innovation. Journal of Business Research, 68(2), 360-370. Garcia, E., Romero, C., Ventura, S., & Calder’s, T. (2007). Drawbacks and solutions of applying association rule mining in learning management systems. In Proceedings of the International Workshop on Applying Data Mining in e-Learning (ADML 2007), Crete, Greece (pp. 13-22). Hu, Y., Hou, J., & Chien, C. (2018). A framework for knowledge management of university–industry collaboration and an illustration. Computers & Industrial Engineering. Karimi Pouya, M., & Ghanbari, A. (2018). Investigating the accuracy of learning machines in predicting stock price returns using the Rough set model. Nearest neighbor and decision tree. Journal of Financial Engineering and Securities Management. 1 (1)215-234[in Persian]. Lee, E. J., Kim, H. S., & Kim, H. Y. (2014). Relationships between core factors of knowledge management in hospital nursing organisations and outcomes of nursing performance. Journal of clinical nursing, 23(23-24), 3513-3524. Matlaby, A., Alipour, A., & Nasri, F. (2014). Identifying Factors Influencing Knowledge Management Implementation in Higher Education Institutions and Their Ranking Using Fuzzy TOPSIS Method, Quarterly Journal of Educational Management Research, 5 (1), 133-153. [in Persian]. Mousavi, S. N., ShariatNejad, A., Zare, F., & Saedi, A. (2018). Analyzing the effect of knowledge management strategies and knowledge maps on knowledge productivity in order to achieve innovative behaviors. Scientific Quarterly of School Management,8(1),322-340. [in Persian]. Moslehabadi, Z., Mohammadi, S., & Qomi, M. (2017). Status of Facilitating Factors in Establishing Knowledge Management at Farhangian University. Third International Conference on Psychology of Sociology of Educational Sciences and Social Studies. Shiraz. Kharazmi Institute of Science and Technology. [in Persian]. Nouri, R. (2012). Identifying and Ranking Key Factors for Implementing Knowledge Management Based on Organization Knowledge Management Strategies (Case Study: Iranian Oil Company). Master of Science in Industrial Engineering - Web Management System. Azad University South Tehran Branch. [in Persian]. Oluikpe, P. (2012). Developing a corporate knowledge management strategy. Journal of Knowledge Management, 16(6), 862-878. Oufkir, L., Fredj, M., & Kassou, I. (2016). Knowledge management performance measurement: a generic framework. Paper presented at the International Baltic Conference on Databases and Information Systems. Rahimi, H., & Najafi, A. (2003). Knowledge Management in Educational Organizations, Volume I, Turn 2, Tehran, Immortal. [in Persian]. Razini, R., & Milad, S. (2017). A Comprehensive Classification of Key Factors Influencing Organizational Knowledge Management, Strategic Knowledge Management Journal. [in Persian]. Safaei, N., Taleghaninia, F., & kiamanesh, A. (2017). Identifying and Ranking Key Factors of Success in Knowledge Management in Knowledge-Based Companies (Case Study: Science and Technology Park in Tehran University). Technology Development, 13(50), 21-28. [in Persian]. Saniie Abadeh, M., Mahmoudi, S., & Taherpour, M. (2015), Applied data mining. Volume 1, Second Time, Tehran (Need for Knowledge). [in Persian]. Tabarsa, G., & Ormazdi, N. (2009). Explanation and Evaluation of Contextual Factors for Knowledge Management Implementation (Case Study: National Iranian Oil Products Distribution Company, Tehran). Management Message, 7 (26), 70-39. [in Persian]. Valian, S., Moeini KIA, M., & Zahed Baban, A. (2013). Meta-analysis of Factors Affecting Knowledge Management Implementation in Higher Education, 6th Conference on Knowledge Management. Tehran. Oil, Gas and Petrochemical Information Institute. [in Persian]. | ||
|
آمار تعداد مشاهده مقاله: 619 تعداد دریافت فایل اصل مقاله: 400 |
||