A Data Mining Approach for Student Referral Service of the Guidance Center

Abstract

The academic guidance office of an educational institution holds pertinent data of all the students in the institution such as psychological examination results, students' referral records and the like. Further, the office offered orientation services, testing services, counseling and follow-up services, individual inventory services, career guidance services, research & evaluation services and placement services. In this paper, a data mining approach was used to produce a trend analysis through time series and forecasted data using the Autoregressive Integrated Moving Average (ARIMA) of the student referral details from one of the Higher Education Institutions in the Philippines. Student referral historical data from the second semester of school year 2016- 2017, first semester of school year 2017-2018, second semester of school year 2017-2018 and the first semester of school year 2018- 2019 was used in the study. Results showed that absenteeism, poor attendance and poor academic performance were the highest number of recorded students' referrals over the others in which poor attendance yields a decreasing pattern among the three. On the other hand, based on the forecasted data, only poor academic performance and poor attendance showed a slight increasing patterns among others. These further signify that a proper program should be in place by the school counselors in mitigating the occurrence of referrals especially on the reasons showing an increase of prediction data.