# Clustering Analysis for Classifying Student Academic Performance in Higher Education
###### Abstract  
There are three income categories for Malaysians: the top 20% (T20), the middle 40% (M40), and the bottom 40% (B40). The government has extended B40/s access to higher education to eliminate socioeconomic disparities and improve their lives. The number of students enrolled in bachelor's degree programmes at universities has risen annually. However, not all students who enrolled graduated. Machine learning approaches have been widely used and improved in education. However, research studies related to unsupervised learning in education are generally lacking. Therefore, this study proposes a clustering-based approach for classifying B40 students based on their performance in higher education institutions (HEIs). This study developed three unsupervised models (k-means, BIRCH, and DBSCAN) based on the data of B40 students. Several data pre-processing tasks and feature selection have been conducted on the raw dataset to ensure the quality of the training data. Each model is optimized using different tuning parameters. The observational results have shown that the optimized k-means on Model B (KMoB) achieved the highest performance among all the models. KMoB produced five clusters of B40 students based on their performance. With KMoB, this study may assist the government in reducing HEI drop-out rates, increasing graduation rates, and eventually boosting students' socioeconomic status. s 2022129467 202  
Due to uncertain economic conditions, higher education institutions (HEIs) had to cut expenses but enhance their quality and efficiency even before the COVID-19 pandemic. Thus, the key aim for ensuring the sustainability of higher learning institutions is to retain the number of students during student enrolment until the end of the course. B40 represents the lowest household group, with an average income below RM4850 per month [3]. One of the Eleventh Malaysia Plan (11 MP) objectives is to advance the B40 household group to the middle-income group. According to the available statistics, the number of students enrolled in bachelor's degree programmes at Malaysia's universities has been increasing annually. Despite a large number of enrolments, not all students can graduate on time. Students dropping out of the university will negatively affect B40 families financially. The family financial burden will increase as students' education loan has to be paid even if they fail to graduate. Furthermore, it will affect a student's chances of securing a high-income job. Student drop-out would also lead to a huge loss in human capital in the nation as fewer professionals and public universities will produce expert skills. Thus, to improve the standard of living among this group, the achievements and performance of B40 students in public HEIs need to be given more attention. Lately, machine learning techniques have been widely applied in forecasting student performance at various levels of study. Most studies use supervised classification approaches to generate predictions. Previous studies focused on developing unsupervised machine learning models by predicting student performance using student data sets from online learning systems. Clustering techniques have been used to predict student performance; however, the data set only includes student performance from online learning, which does not represent B40 students, as B40 students do not have the ease of access to online learning. Research related to unsupervised machine learning to cluster B40 students based on their performance, behaviour, and achievement in HEI is generally lacking. Student behaviour factors were poorly considered in predicting drop-out among B40 households in public universities. This new dimension of student behaviour will look at the number of curriculum activities students participate in during their university studies. Therefore, this study proposes a clustering-based approach for classifying B40 based on their performance, behaviour (i.e., the number of student curriculum activities), and achievement in public HEIs.