An Assessment for Understanding Student Behaviour by Applying Machine Learning Technique
Vol. 5 No. 2 (2022), Articles
Vol. 5 No. 2 (2022)

An Assessment for Understanding Student Behaviour by Applying Machine Learning Technique

Articles
https://doi.org/10.51153/kjcis.v5i2.122
Published 2022-07-07
Syeda Kainat Ahmed
PMAS Arid Agriculture University, Rawalpindi
Syed Mushhad Gilani
University Institute of Information Technology, PMAS-Arid Agriculture University
Sidra Sultan
PMAS Arid Agriculture University, Rawalpindi
Abdur Rehman Riaz
Muhammad Wasim Abbas Ashraf
University of Agriculture ?Wuhan, China
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Keywords

machine learning
k-clustering
Behaviour Understanding
Student Psychology

How to Cite

Ahmed, S. K. . ., Gilani, S. M., Sultan , S. ., Riaz , A. R. ., & Ashraf, M. W. A. . (2022). An Assessment for Understanding Student Behaviour by Applying Machine Learning Technique. KIET Journal of Computing and Information Sciences, 5(2). https://doi.org/10.51153/kjcis.v5i2.122
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Abstract

In the past, research and tests have been carried out to study the behaviour of students as it is an important topic in psychology. Parents and teachers are concerned about their children how to act in class. Learning of students behaviour in school is essential for teachers for the development and growth of them. A class is composed of students with distinctive characteristics and capacities where a few students are sharp and some are dull. In some cases, it becomes troublesome for the educators to spot who is picking the pace with them and who is falling behind. The proposed approach will extend the students to groom their personalities and overcome their shortcomings and it will offer assistance to the instructor to identify which students require more consideration from them. To do that, we chose and applied K-Clustering Algorithm. In other words, this research attempt to discover homogeneous subgroups inside the information. K means calculation is an iterative calculation that tries to parcel the dataset into K pre-defined unmistakable non-overlapping subgroups (cluster) where each information point has a place as it were one bunch. It tries to form the intra-cluster information focuses as comparable as conceivable whereas moreover keeping the cluster as diverse (distant) as conceivable

https://doi.org/10.51153/kjcis.v5i2.122
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