International Journal of Advanced Research in Computer and Communication Engineering

A monthly peer-reviewed online and print journal

ISSN Online 2278-1021
ISSN Print 2319-5940

Abstract: This study introduce a  Decision Support System called Correlation and Linear Regression Analysis –Decision Support System (CALRA-DSS) System. The CALRA-DSS will use Data mining technique in the process of discovering knowledge which in turn can be used to predict future results. CALRA-DSS predicts Students chances whether passed or Failed in the LEAE Licensure Examination. The integration of Data Mining Technique using Pearson-Product Moment Correlation that is used to determine the degree to which two variables are related and Regression Analysis that is used to examine the relationship between and one dependent and one independent variable. The data to be tested by this CALRA-DSS were the April 2016 Agricultural Engineering graduates of ICET, SPAMAST- Digos Campus who participated in the LEAE Mock Board Examination last and took the August 2016 LEAE. The academic records of these graduates were taken from the SPAMAST Electronic Students Information System (eSMS) Digos Campus while the Mock Board data Result was taken from the SPAMAST LEAE Reviewer Committee. It is concluded that with the use of this tool, the ICET Department can implement an intervention program timely before Student would intend to take the LEAE.  Based on the Outcome, the results obtained from the Correlation and Regression Analysis and the attributes obtained from eSMS, the identified Academic Predictors has a strong correlation to Mock Board Examination. In general outcome of the study can give a hint to the Students as to which subjects can be considered as predictor variables for their licensure exam scores and hence become their focus of study/review while still studying. 

Keywords: Correlation, Data Mining, Regression, LEAE, Academic Performance, Prediction.


PDF | DOI: 10.17148/IJARCCE.2019.8401