## 5 Discussion
### Performance of Models  
The findings show that when utilising APS, mathematics, and physical sciences as input parameters, some models perform better than others when predicting student performance and qualification enrolment, especially for first-year students. The study reveals that employing these particular features will likely generate more accurate predictions for first-year student performance relative to overall student performance among the models studied, including decision trees, LGBM, KNN, random forests, and XGB.  
The models evaluated for predicting student performance and qualification enrolment demonstrate varying performance levels. The KNN model performs the best for overall student performance, followed closely by the decision tree, LGBM, and random forest models. All models perform exceptionally well when predicting first-year students' performance, achieving identical high scores. This study suggests that APS, mathematics, and physical sciences are valuable features for predicting first-year students' academic performance.  
The decision tree and random forest models consistently demonstrate strong performance when predicting qualification enrolment for all students and the qualification flags using first-year data. At the same time, the KNN and XGB models also exhibit high accuracy and precision. The decision tree, KNN, random forest, and XGB models show consistent and strong performance across multiple prediction tasks, with the LGBM model also demonstrating good performance in some instances.  
When predicting overall student performance, this study's results closely resemble those of [PERSON] et al. [3]. In their research, random forest achieved an accuracy of 86%, with the AdaBoost regressor following closely at 85%. Meanwhile, KNN had an accuracy of 82%, and the decision tree scored 81%. Our study observed similar accuracies: the decision tree at 82%, KNN at 84%, LGBM at 84%, the random forest at 82%, and XGB at 83%. The minor variations in accuracy between our study and [PERSON] et al.'s [3] research may be attributed to factors like dataset size. Nonetheless, these results indicate a remarkable similarity between the two studies.  
A study by [PERSON] et al. [67] used traditional classifiers that demonstrated exceptional accuracy, with SVM and Naive Bayes achieving accuracy scores of 89.6% and 90.3%, respectively. The decision tree achieved 93.1% accuracy, and the random forest classifier 96.9% accuracy. Our study presented slightly lower accuracy values ranging from 82% to 84% but introduced diverse classifiers, such as KNN, LGBM, and XGB, demonstrating their adaptability in various contexts. This study also provided insights into prediction error through RMSE and MAE metrics, showcasing reasonable prediction accuracy.  
A recent study suggested a method to remove bias by excluding data about protected features that are not biased against race without sacrificing predictive accuracy [68]. This study followed the same process of eliminating demographic factors, which helps to reduce the risk of perpetuating discriminatory practices. By removing features like ethnicity or nationality and using high school performance features, the models ensure that predictions are based solely on their abilities and achievements, not demographics. This approach also safeguards against discriminatory or unfair practices from considering personal information, fostering a more equitable commitment to responsible and ethical data mining practices [31].  
The high accuracy means that a recommender system is good at differentiating between students who can succeed in mainstream and those who are more likely to benefit from extended programmes. Using this predictive study, academic advisors and institutions can identify students who may be in danger of falling behind and need more help to meet academic standards. Universities can increase their chances of success and fill in any gaps in their fundamental knowledge by proactively targeting and offering the right interventions for students in extended programmes.