AutoML Algorithms for Predicting Students’ Dropout and Academic Success in Higher Education

Siti Hajar Datu Ali Nafiah — 2026-07-31

Challenges with high dropout rates and low academic achievement continue to be a major issue for higher education institutions. To deal with these challenges, it is essential to identify the underlying causes and develop strategic solutions to support student success. Machine learning techniques are powerful tools for predicting student academic performance and enabling timely, targeted interventions. In this study, the goal is to test Automated Machine Learning (AutoML) frameworks that can predict student dropout and academic success. The dataset included students' demographic, socio-economic, and academic information gathered from institutional records. H2O AutoML, TPOT, PyCaret, and AutoGluon were evaluated, as these tools automate important parts of the machine learning pipeline, making it easier to train and select models. Evaluation metrics including accuracy, precision, recall, and F1-score, as well as training time and CPU usage, were used to assess model performance and computational efficiency. Three main experiments were conducted: the first evaluated model diversity, the second observed the effect of cross-validation fold size, and the third assessed different search space configurations. In the first experiment, TPOT obtained the highest accuracy of 90.04%. In the second experiment, using 10-fold cross-validation, H2O AutoML obtained the best accuracy of 91.38%. In the third experiment, TPOT maintained its accuracy at 90.04% across all configurations. A comparison between the conventional ExtraTreesClassifier and TPOT's AutoML ExtraTreesClassifier showed comparable performance, with the conventional model achieving slightly higher accuracy of 90.20% and recall of 94.44%, while TPOT demonstrated superior precision of 90.24%. These results demonstrate that AutoML frameworks are effective tools for early identification of at-risk students, offering competitive performance with significantly reduced manual effort. Future studies could explore deploying these models within academic advising systems.

Network Traffic Classification using Machine Learning: A Review of Decision Tree, Random Forest, and SVM Approaches on CIC-IDS2017

Gohar Rahman — 2026-07-31

Cyber-attacks are proliferating rapidly, and networks today are increasingly complex. For this reason, the ability to sort and locate intrusions in network traffic is critical. Researchers have employed methods based on machine learning (ML) as the conventional automated and signature focused detection methods often fail to catch newly emerged attack patterns. The purpose of this work is categorized as the survey is to review state-of-the-art machine learning (ML) network traffic classification techniques, focusing on one specific dataset, i.e., the CIC-IDS 2017 dataset and provides an analysis of today’s research. The study drew on the IEEE Xplore, Springer and Elsevier databases for conducting an extensive review of literature published between 2020 and 2024 having identified studies involving supervised machine learning methods like Decision Trees, Random Forest, Support Vector Machine (SVM) that detected desirable and undesirable traffic. Researchers have also reviewed examples of performance measures such is accuracy, precision, recall and F1-score in different studies to determine areas of strength/weakness or patterns. Studies show that Decision Trees are interpretable, Random Forest has good memory and generalization abilities and SVM is accurate but it requires computational optimization in large datasets. Despite these benefits, challenges such as dataset imbalance, feature redundancy, and real-time implementation continue to remain unsolved. This paper has shown how machine learning can improve the network intrusion detection and challenging issues that need to be addressed in further. The obtained insights lead to a basis for an enhanced scalable and robust traffic classification model, contributing to the goals of current research in this domain.

International Journal of Machine Intelligence and Computing

AutoML Algorithms for Predicting Students’ Dropout and Academic Success in Higher Education

Network Traffic Classification using Machine Learning: A Review of Decision Tree, Random Forest, and SVM Approaches on CIC-IDS2017