Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Teacher Ranking System to Rank of Teacher as Per Specific Domain


Affiliations
1 Department of Computer Engineering, Vishwakarma Institute of Information Technology, India
     

   Subscribe/Renew Journal


Today, data mining is used in every area. In data mining, huge quantity of data can be store. Now education is very powerful field. In this field, data mining suggests many techniques, tools and research plan for extracting large data generated by learning activities in educational system. The main aim of paper, we create a system which is giving rank to teacher as per subject or domain. It will help to assign teaching load to the teacher and assign subject to the teacher. This paper introduces a system i.e. system decide which teacher teaching which subject on the base of rank. The system assigned the subject is based on various parameters such as how many time teachers teach those subjects, resume, work done on subject, area of interest, student feedback ,etc. When principle or HOD assign proper teaching load to the faculty, the expertise of that faculty should be consider. Teaching load distribution is complicated work as it as many constituents such as maximum teaching load as each faculty, number of working hours etc. So, this system is useful to manage load of department. Therefore, there is need of recommendation system which will helpful to HOD as department level and principle as institute level.

Keywords

Clustering, Naïve Bayes, Decision Trees, Weka.
Subscription Login to verify subscription
User
Notifications
Font Size

  • Randa Kh. Hemaid and Alaa M. El-Halees, “Improving the Teacher Performance using Data Mining”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 4, No. 2, pp. 407-412, 20015.
  • M.O. Asanbe, A.O. Osofisan and W.F. William,“ Teachers’ Performance Evaluation in Higher Educational Institution using Data Mining Technique”, International Journal of Applied Information Systems, Vol. 10, No. 7, pp. 10-15, 2016.
  • Aranuwa Felix Ola and Sellapan Pallaniappan, “A Data Mining Model for Evaluation of Instructors ‘Performance in Higher Institutions of Learning using Machine Learning Algorithms”, International. Journal of Conceptions on Computing and Information Technology, Vol. 1, No. 2, pp. 18-23, pp. 87-91, 2013
  • G. Nirmala and P.B. Mallikarjuna, “Faculty Performance Evaluation Using Data Mining”, International Journal of Advanced Research in Computer Science and Technology, Vol. 2, No. 3, pp. 87-89, 2014.
  • P. Tamil Selvy, D. Madhumathi, B. Preetha and S. Sarika, “Predicting Instructor Performance in Educational Institution using Data Mining Techniques”, International Journal of Engineering Science and Computing, Vol. 7, No. 3, pp. 5641-5645, 2017.
  • S. Archana and K. Elangovan, “Survey of Classification Techniques in Data Mining”, International Journal of Computer Science and Mobile Applications, Vol. 2, No. 2, pp. 23-27, 2014.
  • Upendra Singh and Saqib Hasan, “Survey Paper on the Document Classification and Classifier”, International Journal of Computer Science Trends and Technology, Vol. 3, No. 2, pp. 103-107, 2015.
  • Vandana Korde and C Namrata Mahender, “Text Classification and Classifier: A Survey”, International Journal of Artificial Intelligence and Applications, Vol. 3, No. 2, pp. 233-237, 2012.
  • Shikha Chourasia, “A Data Mining Model for Evaluation of Instructors ‘Performance in Higher Institutions of Learning using Machine Learning Algorithms”, International Journal of Computer, Communication and Information Technology, Vol. 1, No. 2, pp. 747-749, 2013.
  • Lior Rokach and Oded Maimon, “Top-Down Induction of Decision Trees Classifiers-A Survey”, IEEE Transactions on System, Man and Cybernetic: Part C, Vol. 1, No. 11, pp. 476-487, 2002.
  • P. Haripriya and R. Porkodi, “A Survey Paper on Data Mining Techniques and Challenges in Distributed DICOM”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 5, No. 3, pp. 741-747, 2016.
  • Harshna and Navneet Kaur, “Survey Paper on Data Mining Techniques of Intrusion Detection”, International Journal of Science, Engineering and Technology Research, Vol. 2, No. 4, pp. 799-802, 2013.
  • Meenakshi and Geetika, “Survey on Classification Methods using WEKA”, International Journal of Computer Applications, Vol. 86, No. 18, pp. 16-19, 2014.
  • Shahrukh Teli and Prashasti Kanikar “A Survey on Decision Tree Based Approaches in Data Mining”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 5, No. 4, pp. 613-617, 2015.
  • A.O. Ogunde and D.A. Ajibade, “A Data Mining System for Predicting University Students’ Graduation Grades Using ID3 Decision Tree Algorithm”, Journal of Computer Science and Information Technology, Vol. 2, No. 1, pp. 21-46, 2014.
  • Mrinal Pandey and Vivek Kumar Sharma, “A Decision Tree Algorithm Pertaining to the Student Performance Analysis and Prediction”, International Journal of Computer Applications, Vol. 61, No. 13, pp. 23-27, 2013.
  • Hitarthi Bhatt, Shraddha Mehta and Lynette R. Dmello, “Use of ID3 Decision Tree Algorithm for Placement Prediction”, International Journal of Computer Science and Information Technologies, Vol. 6, No. 5, pp. 4785-4789, 2015.
  • Abeer Badr El Din Ahmed and Ibrahim Sayed Elaraby, “Data Mining: A Prediction for Student's Performance using Classification Method”, World Journal of Computer Application and Technology, Vol. 2, No. 2, pp. 43-47, 2014.
  • Mashael A. Al-Barrak and Muna Al-Razgan, “Predicting Students Final GPA using Decision Trees: A Case Study”, International Journal of Information and Education Technology, Vol. 6, No. 7, pp. 33-37, 2016.
  • David Kolo, Solomon A. Adepoju and John Kolo Alhassan, “A Decision Tree Approach for Predicting Students Academic Performance”, International Journal of Education and Management Engineering, Vol. 5, pp. 12-19, 2015.
  • Ajay Kumar Pal and Saurabh Pal, “Evaluation of Teacher’s Performance: A Data Mining Approach”, International Journal of Computer Science and Mobile Computing, Vol. 2, No. 12, pp. 359-369, 2013.
  • Jai Ruby and K. David, “Predicting the Performance of Students in Higher Education Using Data Mining Classification Algorithms - A Case Study”, International Journal of Applied Science and Research, Vol. 2, No. 11, pp. 336-339, 2014.
  • John M. Kirimi and Christopher A. Moturi, “Application of Data Mining Classification in Employee Performance Prediction”, International Journal of Computer Applications, Vol. 146, No.7, pp. 229-232, 2016.
  • Rajni Jindal and Malaya Dutta Borah, “A Survey on Education Data Mining and Research Trends”, International Journal of Database Management Systems, Vol. 5, No. 3, pp. 16-19, 2013.
  • P. Nithya, B. Umamaheswari and A. Umadevi, “A Survey on Educational Data Mining in Field of Education”, International Journal of Advanced Research in Computer Engineering and Technology, Vol. 5, No. 1, pp. 69-79, 2016.

Abstract Views: 266

PDF Views: 3




  • Teacher Ranking System to Rank of Teacher as Per Specific Domain

Abstract Views: 266  |  PDF Views: 3

Authors

Anjali A. Dudhe
Department of Computer Engineering, Vishwakarma Institute of Information Technology, India
Sachin R. Sakhare
Department of Computer Engineering, Vishwakarma Institute of Information Technology, India

Abstract


Today, data mining is used in every area. In data mining, huge quantity of data can be store. Now education is very powerful field. In this field, data mining suggests many techniques, tools and research plan for extracting large data generated by learning activities in educational system. The main aim of paper, we create a system which is giving rank to teacher as per subject or domain. It will help to assign teaching load to the teacher and assign subject to the teacher. This paper introduces a system i.e. system decide which teacher teaching which subject on the base of rank. The system assigned the subject is based on various parameters such as how many time teachers teach those subjects, resume, work done on subject, area of interest, student feedback ,etc. When principle or HOD assign proper teaching load to the faculty, the expertise of that faculty should be consider. Teaching load distribution is complicated work as it as many constituents such as maximum teaching load as each faculty, number of working hours etc. So, this system is useful to manage load of department. Therefore, there is need of recommendation system which will helpful to HOD as department level and principle as institute level.

Keywords


Clustering, Naïve Bayes, Decision Trees, Weka.

References