# Clustering Analysis for Classifying Student Academic Performance in Higher Education
## 2 Related Work  
Literature findings indicate that unsupervised machine learning methods, specifically the clustering technique for educational data, are widespread and frequently employed by researchers. Researchers took student datasets from online learning platforms and used them to develop clustering models to predict student performance [4; 5; 6; 7]. Student information, student behaviour when using e-learning, and student achievements at the end of the learning are all common attributes. Since previous studies used student datasets from online course applications, this study will take a different approach by using a set of student data from HEIs that offered face-to-face learning courses. The data set includes a large number of students from Malaysian public universities studying in a variety of fields. Researchers often use popular clustering algorithms to build learning models for educational data: k-means, BIRCH, and DBSCAN [8; 9]. These algorithms have advantages and disadvantages but can produce clusters with satisfactory performance. Among the restraints outlined by the reviewer above is the difficulty of finding good cluster results because it is sensitive to the parameter setting of the grouping algorithm. In addition, [PERSON] [10] investigated student behaviour recorded in the e-learning system, which can contribute to the performance in the examination. The data mining techniques used are k-means and decision trees. The cluster analysis showed three groups of students based on their behavioural similarity in e-learning, and three decision tree models were built based on the cluster analysis. The feature that gave the highest information gain was scoring in mid-term exams. Low frequency in accessing lecture material and online learning would lead to lower exam achievement. [PERSON] et al. [11] analysed student performance using unsupervised machine learning, namely hierarchical and k-means algorithms. The techniques employed were proven to be effective at predicting students' course achievement. Hierarchical clustering has yielded major contributing factors that influence student results through relationships in dendrograms, such as extracurricular activity, attendance, and the number of failed classes. Similarly, a study by [PERSON] et al. [12] has conducted a comparative analysis involving four clustering algorithms: k-means, k-medoids, fuzzy c-min, and expectation maximization (EM). The data of 1531 college students were used to predict student performance in the semester's final examination. The study's results found that fuzzy c-means and EM had better clustering quality in terms of purity and normalized mutual information (NMI), but the implementation time was longer. [PERSON] et al. [6] have applied seven clustering algorithms to educational data sets containing several student achievement grades. The study's findings revealed that k-means and PAM techniques were the best in the division category, while DIANA and hierarchy were the best in the hierarchy category. In addition, they also found that student achievement grades were very easy to group and could be applied to other educational data sets. Other studies have also shown similarities in using unsupervised machine learning, especially clustering algorithms, to predict student performance [13; 14] and identify undesirable student behaviours [15]. The clustering technique showed good performance in making predictions and produced interesting patterns when using large student datasets. Table 1 below shows a list of past studies using clustering algorithms in the education domain. Furthermore, [PERSON] et al. [16] have explained that the quality indicators of HEIs are drop-out rate, timely graduation rate, and student marketability. Drop-outs have a devastating effect on students and higher learning institutions as they involve financial implications, graduation rates, and reputation in the eyes of the community [17; 18].