Classification of students' academic performance using data mining techniques - Nur Hafieza Ismail
Data Mining (DM) is an approach to discover useful information from large amount of data. In education, DM plays an important role to discover hidden information about students' learning behaviour and their academic performance. At the beginning of a new semester, lecturers face difficulties because there is no approach that can be used to identify the students' performance towards their studies. Appropriate actions should be taken as early as possible to prevent the students from gaining low Grade Point Average (GPA) at the end of the semester. This research aims to classifying Students' Academic Performance (SAP) by applying DM techniques namely Naive Bayes Classifier (NBC), Rule-Based (RB), and Decision Tree (DT). The dataset used for the study is based on the students' particulars in the Bachelor programs in the Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin (UniSZA). The data is of eight-year period beginning from July 2006/2007 to July 201312014. A total of 399 records with eight parameters have been collected from the Division of Academic Management, UniSZA. A Statistical Package for the Social Sciences (SPSS) tool is used to identify the significant parameters that contribute to SAP and Waikato Environment for Knowledge Analysis (WEKA) tool is used to conduct the DM analysis. The data from the selected parameters is then cleaned, trained, and tested to produce the best model to classify SAP. The extracted model is able to forecast future academic performance of the first semester for the first year students based on three GPA categories which are 'Good', 'Average', and 'Poor'. It is discovered that, RB model shows the best result compared to NBC and DT models. RB model produces the accuracy value of71.3% based on three significant parameters which are students' race, gender, and family income. In conclusion, it is very useful for the lecturers and the faculty management to embrace SAP model to be used in identifying students' performance at the very early stage. A set of rules generated by the model can also be used to classify the SAP for the new intakes. Hence, this information provides assistance in terms of the necessary actions that can be 'taken to improve the students' performance so as to guarantee success in the students' academic tenure.