Classification and Prediction of Slow Learners Using Machine Learning Algorithms
MLA Style:Sangeeta.K, G.V.S.S.Naveen Babu, Madhuri.G "Classification and Prediction of Slow Learners Using Machine Learning Algorithms." International Journal of Computer Trends and Technology 68.2 (2020):54-58.
APA Style: Sangeeta.K, G.V.S.S.Naveen Babu, Madhuri.G (2020). Classification and Prediction of Slow Learners Using Machine Learning Algorithms. International Journal of Computer Trends and Technology, 68(2),54-58.
Abstract
Any educational Institute’s main goal is to increase pass percentage of the students. A student’s performance depends on his learning ability and is influenced by many factors. A slow learner grasps things lately ,requires things to explained with much detailed resources to be successful as compared to a fast learner. As the competitive world demands more out of a student with respect to an all round development, student classification based on learning ability is useful in predicting slow learner. The slow learners will be given appropriate training to improve his/her performance and thereby achieving institute’s goal. This paper uses real time student data of computer science engineering department, Aditya Institute Of Technology and Management, Tekkali in Srikakulam district. The study involves experiments to understand the influence of cognitive attributes on academic performance. The classification of Students into very fast learners, fast learners, average learners, and slow learners using classification algorithms and thereby finding out the best prediction model. The proposed paper accommodates the individual differences of the learners in terms of knowledge level, learning preferences, cognitive abilities etc
Reference
[1] M.Ramaswamy and R.S.Bhaskaran, “A CHAID Based Performance Model in Educational Data Mining”, International Journal of Computer Science Issues(IJCSI), Vol. 7, Issue 1, No. 1,January 2010.
[2] Praneet Kaur,Manpreet Singh and Gurpreet Singh Josan, “Classification and Prediction based data mining algorithms to predict slow learners in educational sector” 3rd International Conference on Recent Trends in Computing(ICRTC) 2015.
[3] Cortez and Silva, “Using data mining to predict secondary school student” 5th Future Business Technology Conference(FUBUTEC 2008).
[4] Ramesh, P.Thenmozhi and Dr.K. Ramar, “Study of influencing factors of academic performance of students: A data mining Approach” International Journal of Scientific & Engineering Research, Volume 3, Issue 7, July-2012
[5] R. E. Sorace, V. S. Reinhardt, and S. A. Vaughn, “High-speed digital-to-RF converter,” U.S. Patent 5 668 842, Sept. 16, 1997.
Keywords
Classification, prediction, Slow learner.