Wireless Sensor Networks (WSNs) are integral to mission-critical applications, including environmental monitoring, smart infrastructure, and healthcare. However, they are particularly vulnerable to denial-of-service (DoS) attacks, which can deplete the node's energy and disrupt communication. This study examines the effectiveness of various machine learning algorithms in enhancing intrusion detection within WSNs, focusing on balancing detection accuracy and computational efficiency. Utilising the Network Simulator-2 (NS-2) generated WSN-DS dataset, seven algorithms—K-Nearest Neighbours (KNN), Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Stacking Classifier, AdaBoost, and Artificial Neural Network (ANN)—were implemented and evaluated. The experimental results indicate that AdaBoost achieved the highest overall performance, with an accuracy of 99.7%, ROC-AUC of 0.996, and detection speed of 1.6 min, underscoring its suitability for real-time intrusion detection. Stacking and Random Forest also demonstrated high accuracy (99.7% and 99.6%, respectively) but required slightly longer detection times of 7.07 and 7.33 min, respectively. In contrast, KNN exhibited the longest detection time (86.2 min) due to its high computational overhead, whereas Naïve Bayes was the fastest (0.02 min) but had lower precision (0.757) and F1-score (0.771). AdaBoost demonstrated superior detection accuracy, efficiency, and adaptability under constrained WSN conditions, outperforming all other algorithms across multiple performance metrics. These findings offer a practical benchmark for developing lightweight, high-performance intrusion detection systems in resource-limited wireless sensor environments, thereby enhancing the resilience and reliability of next-generation WSN infrastructures.

This study investigates the performance of students enrolled in programming courses offered under the Sandwich Educational System (SES) in Ghanaian universities and colleges. SES is a unique educational approach that combines academic studies with practical work experience. This study examines various teaching models employed within the SES for programming education to identify any significant relationship between teaching methods and student academic performance. The target population for this study comprised students enrolled in computing education-related programs within the SES, with a specific focus on those undertaking programming courses. A single study group of students pursuing the Bachelor of Education programme in Information Technology (B.Ed. IT) under the sandwich mode at University X was selected to ensure efficient research management in this study. Employing a mixed-methods research design, quantitative and qualitative data were collected and analysed using descriptive and inferential statistics. A survey was administered to 218 of the 357 students in the study group during the designated survey period. Additionally, a seven-year longitudinal quasi-experiment involving five different year groups in the B.Ed IT sandwich programme at University X was conducted to examine the relationship between student performance and teaching methods within SES. The findings of this study do not demonstrate a significant difference in academic performance among students taught using different teaching methods in SES. However, it is crucial to acknowledge the study's limitations, which necessitate considering the findings as insightful observations rather than as conclusive results. This study recommends enhancing students' prior exposure to programming and adopting innovative teaching methods to improve their academic performance. Future research should address the limitations of this study by utilising a more rigorous experimental design, such as a randomised controlled trial, and exploring additional factors that may influence student performance within the SES. Such endeavours would enable more robust causal inferences to be drawn.