Envisioning Educational Success Through Advanced Analytics and Intelligent Performance Prediction

The accurate prediction of students academic success holds significance in enhancing educational outcomes. The study focuses on overcoming the challenge of assessing whether a student will successfully pass the final exam, taking into account a range of socio-economic and academic factors. Every student's journey is different, and it is influenced by various factors such as their socioeconomic background, academic abilities, and personal experiences. To evaluate their performance, we use various machine learning algorithms such as KNN, Naive Bayes, SVM, AdaBoost, and Naive Bayes— and assess their performance using key metrics such as ROC curve, confusion matrix, precision, recall, and F1 score. The F1 score offers a balanced assessment of precision and recall, offering insights into overall model accuracy. Precision and recall focus specifically on the model's ability to correctly identify positive instances and capture all actual positive instances, respectively. ROC curves demonstrate the balance between the rates of correctly identifying true positives and incorrectly identifying false positives, allowing for a nuanced assessment of classifier performance. The confusion matrix further breaks down true positives, true negatives, false positives, and false negatives, enhancing our understanding of algorithmic effectiveness. Through a comprehensive comparison of these classification methods, our goal is to identify the most accurate approach for predicting student outcomes, thus contributing valuable insights to educational institutions and policymakers aiming to implement effective strategies for student success and academic improvement.