Assessment of Performance of Students using Conditional Statistical Technique
Hema Malini B H1, Suma V2, Suresh L3, Shankar M M4
1Dr. Hema Malini B. H.*, Associate Professor, Department of CSE, BMS Institute of Technology, Bengaluru, India.
2Dr. Suma V. Professor, Department of ISE, Dayananda Sagar College of Engineering, Bengaluru, India.
3Dr. Suresh L. Principal and Professor, Department of CSE, Cambridge Institute of Technology, Bengaluru, India.
4Mr. Shankar M. M., Data Scientist, CARES, Bangalore, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 20, 2020. | Manuscript published on March 10, 2020. | PP: 1025-1031 | Volume-9 Issue-5, March 2020. | Retrieval Number: C8389019320/2020©BEIESP | DOI: 10.35940/ijitee.C8389.039520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Educational data mining (EDM) is gaining importance in every field. Due to the competency in every branch of engineering, the institutions are concentrating mainly on improving the performance of students. Efforts are also put towards knowing the reasons for low performance and identifying the factors affecting the student’s performance. Researchers are working on preparing predictive models for improving student performance. The present study is considering the educational data of 1186 students. The data is classified as demographic and study related variables. An effort is made to predict the student performance, using a statistical technique – Chi Square test. The attributes affecting and not affecting the performance of students are assessed. The results are plotted using Pie Chart and histograms. The association between demographic and education variables with semester results is tabulated.
Keywords: Student Performance, Demographic, Study Related, Educational Data Mining.
Scope of the Article: Data Mining