Classification techniques on University College Bestari bachelor degree students' academic performance - Norashikin Musa
In today's strongly cornpetitrve environment, there are two key factors in higher education institutions success which are students' retention and their academic performance. Therefore, the institutions need a student retention plan in order tominimize number of students drop out. However, in coping with student retention problem, many institutions are having difficulties in identifying excellent, good, average and weak students. The institutions are also unable to identify important factors that give high influence to the students' performance. Furthermore, the institutions are unable to handle and use students' data effectively and efficiently due to its large volumes and complexity. This study aims to apply classification algorithms on University College Bestari educational dataset in order to classify students' academic performance based on their personal background, admission data and previous academic results, evaluate the algorithms' performance and identify parameters that give high influence to the academic performance of students by comparing the results produced. To achieve those objectives, classification techniques were implemented in this study using lO-fold cross-validation method to build training and testing dataset. Using WEKA 3.8.2 software, this study implemented decision tree (J48), Naive Bayes and artificial neural network (Multilayer Perceptron). Naive Bayes is chosen because of its ability to work with small amount of data whereas J48 produces a decision tree which can be used to identify most influencing attributes. Multilayer Perceptron is selected because it uses backpropagation algorithm which allows the classifier to adaptively learn from mistakes and thus, yields accurate results. The classifiers were implemented on two datasets: the one which contains unequal class distribution while the other has more balanced class distribution. The first dataset used students' class honours as the target variable whereas the second dataset used students' performance level as the target variable. Since one of the dataset is imbalanced, using accuracy as the only evaluation metric is inadequate. Therefore, four evaluation measures were used to assess the results: accuracy, sensitivity, specificity and Area Under Curve (AUC). Results show that Multilayer Perceptron is the best classifier to work with Honours Degree dataset with 90.60% accuracy. Meanwhile, Naive Bayes is the best classifier for Performance Level dataset with 70.94% accuracy. Naive Bayes also able to correctly identify minority classes. On the other hand, decision tree performed poorly compared to other classifiers especially on minority classes. The study also found that first semester Grade Point Average, general courses scores and high school academic results are important attributes which give high influence to students' achievement. There is correlation between students' scores in Islamic Civilization and Asian Civilization (TITAS) and Basic Entrepreneurship courses with students' final achievement. The usage of classification algorithms in educational data mining could assist institutions and instructors to classify students' academic performance and identify average and weak students and thus can help them to make decisions on the student retention plan.