# Clustering Analysis for Classifying Student Academic Performance in Higher Education
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author and Year** & **Objective** & **Data** & **Algorithm** & **Result** \\ \hline [PERSON] et al. & To develop a data-driven clustering model to identify low student engagement during the early stages of the course cycle. & Demographic, student’s interaction in the virtual learning environment (VLE). & fuzzy c-means (FCM), hierarchical, gaussian mixture, k-prototype & The k-prototype clustered the low-engagement students more accurately and generated highly partitioned clusters. \\ \hline [PERSON] et al. (2021) & To propose an unsupervised ensemble clustering framework to use student behavioural data in order to discover behavioural patterns. & Behavioural, library entry, and gateway login behavioural data. & k-means, DBSCAN, BIRCH, CLIQUE, Expectation Maximization (EM) & The framework not only detects anomalous behavioural patterns but also finds mainstream patterns. \\ \hline [PERSON] (2020) [10] & To apply data mining techniques on educational data of a higher education institution in Croatia. & An e-learning system data. & k-means & The cluster analysis resulted in groups of students according to the frequencies of access to the e-contents, confirming author’s previous research. \\ \hline [PERSON] et al. & To identify the factors affecting students’ academic performance. & Demographic, academic performance & x-means, k-means & The study finding includes a set of the most influencing personal and social factors on the students’ performance, such as parents’ occupation, parents’ qualification, and income rate. \\ \hline [PERSON] (2020) [4] & Identify student groups that would benefit from the intervention in the AC-ware tutor online learning system. & Online learning behaviour & mean shift & Student clusters can be identified according to student interaction with AC-ware tutor. \\ \hline [PERSON] et al. (2019) [22] & Clustering techniques are used to analyse the student’s performance and check how they vary from one another. & Demographic, academic performance & mean shift, k-means & Both the algorithms show that parent’s education is directly proportional to student’s academic performance. \\ \hline [PERSON] and [PERSON] (2019) [23] & To evaluate student’s performance based on both classification and clustering techniques. & Demographic, academic performance, behaviour, and extra features. & k-means & The result proves that the hybrid algorithm combining clustering and classification approaches yields results that are far superior in terms of achieving accuracy in the prediction of the academic performance of the students. \\ \hline [PERSON] et al. (2019) [11] & To analyse the student’s performance by using statistical and unsupervised machine learning algorithms. & Demographic, academic performance, behaviour & k-means, hierarchical & K-means and hierarchical cluster studies give good results for predicting student performance (pass or fail). \\ \hline [PERSON] et al. (2019) [24] & To use the clustering process to generate groups of students whose characteristics might help to understand the reasons for drop-out among the students. & Student’s performance and activities in Moodle online learning education. & FCM & The Fuzzy C-Means generated groups based on how engaged the students are, and, in each group, there are two subgroups: students that drop out and do not drop out of the course. \\ \hline [PERSON] et al. (2019) [9] & To focus on performance evaluation of some clustering algorithms using an educational dataset. & Demographic, academic performance, behaviour & EM, CLOPE, DBSCAN, k-means, CLARA, filtered cluster, farthest first & DBSCAN algorithm performs well in all of the performance measures. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of studies and types of machine learning algorithms in the education domain. Failure in exams and a lack of interest in the courses that students were enrolled in are the main causes of drop-outs at public HEIs in Malaysia.