This study compares immersive VR-based control systems with conventional keyboard-based control to examine the efficacy of VR interfaces for controlling robotic arms in Internet of Things (IoT) education. A 5-DOF robotic arm with MG996R servomotors and controlled by an Arduino microcontroller and Raspberry Pi wireless communication was operated by 31 third-year engineering students in hands-on experiments using both control modalities. To determine student preferences across in-person, online, and hybrid learning contexts, the study applied a mixed-methods approach that combined qualitative evaluation using open-ended questionnaires and quantitative analysis through Likert-scale surveys. First, it should be mentioned that most of the reported papers either use a robotic arm or a VR system in education. However, we are among the first to report a combination of the two. Secondly, in most cases, there are either technical papers or educational quantitative/qualitative research papers on existing technologies reported in the literature. We combine an innovative education context (robotic arm and VR), completed with a quantitative and qualitative study, making it a complete experiment. Lastly, combining qualitative with quantitative research that complement each other is an innovative aspect in itself in this field.

Sensor-based cattle monitoring is a growing field within precision livestock farming, where machine learning algorithms are used to detect health issues, monitor behavior, and improve productivity. However, the diversity of evaluation strategies, performance metrics, and algorithmic choices in the current literature complicates comparison and selection of machine learning models. In this study, we conduct a systematic review and quantitative analysis of machine learning approaches applied to sensor-driven cattle monitoring tasks. We evaluate and compare algorithms using a broad spectrum of performance indicators, including accuracy, F1-score, precision, and recall, alongside their variability across studies. Through detailed heatmap visualizations and statistical summaries (mean, median, standard deviation, coefficient of variation), we identify the most consistently high-performing models and the factors contributing to variability. Results show that Convolutional Neural Networks and Extreme Gradient Boosting achieve the highest F1-scores and most balanced precision-recall performance, especially in imbalanced health detection tasks, while tree ensemble models such as Random Forest and Gradient Boosting demonstrate superior accuracy and consistency for behavior classification on balanced datasets. Our findings emphasize the importance of multi-metric evaluation frameworks and context-aware algorithm selection in the development of scalable and reliable livestock monitoring systems.