The effect of technology has caused many institutions to intend to change their approach to teaching and learning, resulting in the current model of teaching and learning being an active collaborative and self-directed one. The connection between education and technology has received attention as part of educational policy and practice. Debatable topics like how to use technology in the classroom have therefore punctuated complaints about declining educational standards, unemployed learners, duplicate curricula, and archaic institutional structure. In the framework of information and communication technologies and societal growth, the Internet of Things (IoT) is asserting its vital position. Institutions may improve learning outcomes with the use of the Internet of Things by offering enhanced learning experiences, increasing operational effectiveness, and collecting real-time, actionable insight into student performance. The current state of the Internet of Educational Things (IoET) is examined from several educational perspectives in this article; a few of them are highlighted, and both of their established and potential educational benefits are discussed. Additionally, it provides in-depth discussions on current issues and problems for the IoET from a variety of approaches. The investigation performs a bibliometric analysis with VOSviewer to focus on peer-reviewed research articles published in well-known publications databases over the past eight (8) years. The findings of this study should also act as a strong incentive for universities and colleges around the world to use IoT-based teaching and learning technology for improved academic achievement.

Brain tumors (BT) represent a severe and potentially life-threatening cancer. Failing to promptly diagnose these tumors can significantly shorten a person’s life. Therefore, early and accurate detection of brain tumors is essential, allowing for appropriate treatment and improving the chances of a patient’s survival. Due to the different characteristics and data limitations of brain tumors is challenging problems to classify the three different types of brain tumors. A convolutional neural networks (CNNs) learning algorithm integrated with data augmentation techniques was used to improve the model performance. CNNs have been extensively utilized in identifying brain tumors through the analysis of Magnetic Resonance Imaging (MRI) images The primary aim of this research is to propose a novel method that achieves exceptionally high accuracy in classifying the three distinct types of brain tumors. This paper proposed a novel Stack Ensemble Transfer Learning model called “SETL_BMRI”, which can recognize brain tumors in MRI images with elevated accuracy. The SETL_BMRI model incorporates two pre-trained models, AlexNet and VGG19, to improve its ability to generalize. Stacking combined outputs from these models significantly improved the accuracy of brain tumor detection as compared to individual models. The model’s effectiveness is evaluated using a public brain MRI dataset available on Kaggle, containing images of three types of brain tumors (meningioma, glioma, and pituitary). The experimental findings showcase the robustness of the SETL_BMRI model, achieving an overall classification accuracy of 98.70%. Additionally, it delivers an average precision, recall, and F1-score of 98.75%, 98.6%, and 98.75%, respectively. The evaluation metric values of the proposed solution indicate that it effectively contributed to previous research in terms of achieving high detection accuracy.