Prediction model for potential school dropout using data mining – a proposed flowchart

• Main Authors: Sariman, Siti Rafidah, Ab. Jalil, Habibah, Marlisah, Erzam
• Format: Conference or Workshop Item
• Language: English
• Published: Faculty of Educational Studies, Universiti Putra Malaysia 2019
• Online Access: http://psasir.upm.edu.my/id/eprint/76674/1/GREDUC2019-11.pdf; http://psasir.upm.edu.my/id/eprint/76674/; http://spel3.upm.edu.my/max/dokumen/GREDUC_GREduc2019_E-proceedings.pdf

In line with the first aspiration in Malaysia Education Blueprint, which is to give access to children for education to let the child realise their full potential, the ministry is focusing on tackling the issue of students drop out in primary and secondary schools. In particular, this study is focusing on building a prediction model to identify a potential school dropout using data mining approach. The pilot sample of this study is taken from a data source from primary and secondary schools located in one of the states. WEKA, an open-source tool for data mining is used to evaluate the attributes predicting potential school dropout. From the analysis, the outcome will be determined using the highest frequency from the classification techniques in the prediction model. The paper aims to present a methodology flow which will be used to identify the main factors of potential school dropouts from many attributes that will be tested using data mining techniques.