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Self-Management of learning plays a fundamental role in higher learning by improving student’s intention to use M-learning systems for academic purposes. The purpose of this study was to examine the influence of Self-Management of learning on the intention to use M-learning systems in Ugandan universities. A cross sectional survey methodology was employed to collect data from 370 students selected from two different universities in Ugandan universities on the variables of study. Results of correlation and regression analysis indicated that a positive and significant relationship exists between Self-Management of learning and intention to use M-learning systems. These findings have theoretical implications for intention to use M-learning systems by aligning Self-Management of Learning as a Significant Predictor of intention to use M-learning systems. The findings also have practical interventions designed at enhancing student’s intention to use of M-learning systems which implies that universities should ensure that students are able to control their learning activities. The more the learner controls their own activities, the more successful learning will occur.

Principal leadership issues are becoming increasingly debated and explored in international and comparative contexts due to the changing context of educational provision. In the South African education context and the world over, there is unprecedented interest in how school leadership influences the performance of learners. Teaching and learning environments in schools have become more complex and diverse, where effective principalship is required to achieve high standards of educational success. The quality of leadership in schools is the cornerstone for driving transformational change that is required in schools. In this article, we argue that principals need to adopt leadership styles that would enhance the culture of teaching and learning. It was the goal of our qualitative study to explore the role of school principals in improving learning outcomes. Six schools in the North-West province, South Africa, were purposively selected to participate in this study. Semi-structured interviews were conducted with principals and teachers from the selected schools. We found that principals use several leadership styles to enhance the culture of teaching and learning in their schools. The study concluded that leadership in schools plays a significant role in improving learner performance.

Speaking skills is an essential skill in the learning of English. Students are always evaluated in the speaking skill through oral assessment. Carrying out speaking skills modules has always been a difficult process when students fail to give good support due to them being shy and non-responsive in their learning. This action research investigated the effectiveness of the use of the application of YouTube to foster confidence in speaking among the pupils in primary and secondary schools while inculcating fun learning. The samples consist of five Year Five primary school students in Labuan and five Form Four the secondary students in Kuala Kangsar, Perak. Data collection was based on a pre and post observation. The findings showed an increase level of self-confidence in using English orally. In addition this application promotes fun rather than fear towards the learning process. Students are able to give comments and likes on the YouTube postings thus making learning more interactive. This action research showed that using this application of YouTube is an interactive way to foster speaking skills among the pupils.

The popularity of social networking sites is constantly growing among the iGeneration. This study attempted to investigate the effectiveness of TikTokActiV as a tool for teaching and improving ESL pupils’ writing skill focusing on action verbs. The respondents of this study comprise of 60 primary school pupils from a rural school, a sub-urban school and an urban school in southern part of Malaysia. The pupils’ writing skill development were measured using pre and post-test containing 20 questions. Action research design was used to understand the effectiveness of using ‘TikTokActiV’. The pre-test and post-test conclude that by incorporating TikTokActiV as a teaching tool in English language classrooms, pupils will have significant improvements in ESL writing skills.

Learning Language Strategy has come out with several of strategies that can be chosen or used among the pupils. Every pupil has also had another potential in using the learning language strategy in many ways. This study was conducted to investigate the learning language strategy by using vocabulary strategy among 19 school pupils at a rural school in Sabah. The survey was carried out among the pupils of Year 4 and Year 6 to find out which strategy they prefer most in language learning strategy. There were choices of strategies that can be chose by the pupils which were listening, vocabulary, speaking, reading, writing and translation. Among those strategies that have been tested, they chose vocabulary strategy as their most preference based on the analysis frequency of the survey that has been distributed. The survey contained the vocabulary test and the vocabulary learning strategy questionnaire. In addition, they least preferred to listening strategies followed by writing strategies. This can be perhaps for the teachers to make a brand-new step to arise the motivation among pupils to expand their vocabulary in many attractive and interesting ways which then affects to many more benefits in speaking, reading and writing.

Teachers play a major role for effective class conduction. It is the responsibility of the teacher to make his /her class interesting so that students are keen to attend and participate in the class. Some of the teachers keep complaining about the class which they teach. The students of each class bring its own distinct set of characteristics and teacher faces challenge on daily basis. Accepting these challenges and meeting them head on is the first step. The second step is finding a creative solution that will help your students develop the skills they need to succeed. Innovative methods for transforming common classroom management struggles into opportunities for positive change and for changing negative behaviors into positive interactions are explained. Teachers who are courteous, enthusiastic, in control, patient, and organized provide examples for their students through their actions. Classroom Management is very important for achieving the aims and objectives of the teaching-learning and understanding student’s level of knowledge and create a positive thinking. This paper examined the concept of classroom management, styles and implemented how learning strategies used by teachers to promote effective classroom management.

Education is the ability to meet life’s situations Dr. John G. Hibben As quoted by Nelson Mandela, Education is the most powerful weapon which you can use to change the world. As an educationist the investigator’s present research aimed to find out the effect of teaching the children especially with dyslexia treating with a criteria of Multi Sensory Strategy which improves the level of listening with more interest and answering the academic assessment more accurately without error. Multi sensory Strategy can be used in regular classroom. This method can provide children to be free to learn without anxiety and move more enthuastically with their peer group. The objective of the present study was to find out the effectiveness of Multi sensory strategies to overcome learning disability in mathematics at primary level. This study employs quantitative experimental method with single group pre test, post test design. A sample of 30, Ninth standard students were chosen through purposive sampling in Devakottai, Sivagangai District. This data analysis shows that using Multi sensory strategies improves students learning outcomes. This study suggest that using Multi sensory strategies is highly effective in improving learning outcomes in dyslexic students.

This study introduces an IoT and deep learning-based framework designed to enhance biomedical waste management through real-time monitoring and efficient waste classification. By utilising IoT sensors in conjunction with advanced machine learning models and autoencoders, the system automates waste tracking, timely collection, and the categorisation of hazardous materials. IoT sensors enable continuous data collection on waste generation, supporting pattern analysis to optimise waste management processes. This approach addresses critical challenges in mismanagement, including the prevention of spills, control of odours, and the handling of hazardous waste types. By leveraging real-time data, healthcare facilities can improve the segregation and disposal of BMW, minimising risks to human health and the environment. The integration of these technologies contributes to a cleaner, more sustainable waste management ecosystem, ensuring regulatory compliance and enhancing operational efficiency. Ultimately, the proposed system demonstrates how IoT and AI-driven solutions can revolutionise waste management in healthcare settings, mitigating environmental impacts and improving overall public health outcomes.

This study presents the development of a Feed-Forward Neural Network (FFNN)-based model for security of Internet of Things (IoT) network protocols. The proposed method applied in the execution of the study involves data collection, preprocessing, feature selection, and model training using the CIC-IoT 2022 dataset, which includes normal and attack traffic from various IoT devices. In the study, Synthetic Minority Oversampling Technique (SMOTE) was used for data balancing, Principal Component Analysis (PCA) technique was used for feature selection and hyperparameter optimization were employed to enhance the performance of the model during training. The system was simulated in the NS-3 environment to replicate real-world IoT network conditions, and its effectiveness was evaluated using metrics such as accuracy, precision, recall, and F1-score. The results demonstrated that the FFNN-based model achieves an average validation accuracy of 89.7%, precision of 88.9%, recall of 86.9%, and an F1-score of 87.9%. The system results showcased robustness in detecting various attacks, including DoS, brute force and RTSP attacks in mixed traffic scenarios, meanwhile this study serves as a strong foundation for leveraging deep learning techniques to enhance IoT network security protocols.

Traffic congestion at urban roundabouts represents a critical challenge in developing cities such as N’Djamena, the capital of Chad, where rapid urbanization, heterogeneous traffic composition, informal driving behaviors, and limited road infrastructure significantly degrade mobility and traffic efficiency [1], [2]. In such environments, conventional fixed-time and rule-based traffic signal control systems lack the adaptability required to respond effectively to highly dynamic and uncertain traffic conditions. This paper proposes a reinforcement learning–based intelligent traffic signal control framework specifically designed for signalized roundabouts in developing-city contexts, with a particular focus on the traffic characteristics of N’Djamena. The proposed system models traffic signal control as a sequential decision-making problem, in which an autonomous reinforcement learning agent continuously observes real-time traffic states—such as queue lengths, vehicle waiting times, and traffic density—and learns optimal red-light and green-light timing policies through direct interaction with the traffic environment. Unlike traditional approaches, the proposed method does not rely on predefined signal plans or prior traffic flow models, enabling it to adapt effectively to fluctuating and unbalanced traffic demand typical of N’Djamena’s urban road network. The effectiveness of the proposed approach is evaluated through traffic simulation experiments configured to reflect realistic traffic conditions observed in N’Djamena, including heterogeneous vehicle types and variable demand patterns. Performance comparisons with conventional fixed-time signal control demonstrate substantial reductions in average vehicle waiting time, queue length, and overall congestion. These results confirm the potential of reinforcement learning–based traffic signal control as a scalable and cost-effective solution for adaptive traffic management in resource-constrained urban environments and highlight its applicability to intelligent transportation systems in developing cities.