## 2 Literature Review
Fortunately, advances in data mining have enabled a wide range of tools and approaches to fully utilise the data and produce insights [32]. The use of real-world datasets faces a challenge where the number of instances for a particular class could be higher than others. Class imbalance is when a dataset has an uneven distribution of classes with binary classification issues [33]. This disparity causes what is known as class imbalance or imbalanced datasets, which creates a challenge in machine learning [34]. There are limited studies that have been conducted using imbalanced datasets. A study by [PERSON] et al. [35] uses data mining techniques to predict students' academic progress and dropout risk in computer science. The study developed a recommender system that assists students in selecting subjects. This system is built using a real-world dataset obtained from a Spanish institution. The method addresses issues of developing trustworthy recommender systems based on sparse, few, imbalanced, and anonymous data. The study shows that using decision support tools for students has the potential to improve graduation rates and reduce dropout rates. Another study analysed students' previous academic performance and recommended the most appropriate educational programme using relevant features collected via correlation-based feature selection. Multiple machine learning algorithms were deployed to obtain the best results, and the most suitable model and relevant features were utilised in the recommendation process to ensure students reached their professional goals [36]. Past studies have suggested several approaches to constructing fair algorithms by reducing the impact of bias. Due to the link between some features, it has been shown that avoiding sensitive features is insufficient to eradicate bias because this association may lead to biased and unfair data [37]. To address data bias and generate unbiased recommendations, [PERSON] and [PERSON] [37] proposed a method for managing data bias and producing unbiased recommendations by altering the data before model training. This technique converts biased data into unbiased data by changing and ignoring the sensitive attribute in favour of predicting the class label using a ranking function learned from the biased data. Their findings showed that prejudice might be lessened by changing the labels for the positive and negative classes. Predicting students' performance has been challenging for higher education institutions seeking to overcome persistent issues, such as dropping out, changing qualifications, failure, low academic grades, and prolonged graduation time. Some researchers have studied the influence of non-academic attributes, like student demographics and socio-economic status, on student performance [36; 38]. While no one factor guarantees success in Science, Technology, Engineering, and Mathematics (STEM) fields, several factors, including academic preparation, encouraging family and social networks, and inclusive learning environments, are essential for attracting and keeping students in STEM fields [12]. Educational data mining can impact students' privacy, security, and rights [39]. [PERSON] et al. [1] found that students' decisions to drop out were influenced by a mix of personal choices and structural factors, providing valuable insights into potential strategies for reducing attrition among academically eligible students. [PERSON], [PERSON] and [PERSON] [23] contributed insights into course-specific determinants influencing student achievements, demonstrating the relevance of course-specific factors in academic advising. In their study on tackling imbalanced class learning, [PERSON] et al. [31] underscore the necessity of recognising and mitigating potential constraints, including data bias and handling sensitive data information in datasets. [PERSON] and [PERSON] [32] emphasised the need to explore the critical point where data bias becomes problematic, highlighting the significance of addressing data imbalance.