Online-Journals.org (International Association of Online Engineering)
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Enhancing Spatial Thinking Awareness of World-Scale Geography with Excel Dynamic Map Charts and Virtual Globes
Get PDFSpatial thinking is a unique thinking skill that geographers use to reason. Every individual is believed to have this thinking skill, but not all are aware of it. This condition causes differences in the development of each person. A person’s spatial thinking can be improved by training. Geospatial technology is a representation tool that many people believe can be used to train spatial thinking skills. However, not many people encounter obstacles when using this technology. The complexity of the command to run is an obstacle that is often found. In line with the development of geospatial technology, many applications integrate this technology as part of visualization tools. This teaching and learning were conducted with Action Research Classroom (three cycles) in the form of Project-Based Learning with Science, Engineering, Technology, and Mathematic (STEM) approach. This article discusses the findings of the research on the use of Excel dynamic map chart and virtual globe to improve spatial thinking in research subjects with the case study of Indonesian Geography and World Regional Geography. With the Excel application project, for the context of upper-level education, the findings show an increase in spatial thinking skills and mastery of the use of mapping platforms without the need for prior experience of coding, software, or cartography, although it needs to be corroborated by other studies
Effectiveness of an E-learning System for Emergency Signs and CPR Emergency Preparedness in Marathon Events: A Comparative Study
Get PDFThis study investigates the implementation, effectiveness, and impact of a unique e-learning system designed specifically for emergency signs and cardiopulmonary resuscitation (CPR) emergency preparedness in marathon events. Our approach introduces the first e-learning system specifically designed for marathon events. It delivers engaging content, including infographic stories, expert lectures, and interactive modules, to provide registered runners with comprehensive knowledge of first aid and emergency signs for CPR. To evaluate the e-learning application, we conducted a comparative experiment during the CMU (Chiang Mai University) marathon with 9,761 participants. We used pre- and post-tests, as well as a survey questionnaire. The results showed significant improvements in participants’ CPR knowledge across all educational backgrounds. The integration of e-learning into the registration process contributed to a safer marathon environment, as participants felt more confident in handling emergencies. Approximately 85% of participants expressed a willingness to recommend the e-learning system. This increased confidence among participants in handling emergencies benefits both runners and marathon organizers by enhancing safety measures and emergency response during events. In conclusion, our findings strongly support the integration of e-learning into the registration process for marathon events. Recommendations based on our research include providing comprehensive guidelines for other marathon events, instilling stakeholder confidence, and emphasizing the suitability of e-learning for medium- to largescale events. However, caution is advised for smaller events due to potential complexities and costs. Additionally, we suggest limiting the validity of e-certificates to ensure that participants have up-to-date CPR knowledge
Mobile-Based Digital Assessment Transforming the Learning Support Efficiency
Get PDFThe digital age has brought great changes in many areas of life, including education. The main change is the use of mobile device systems such as smartphones or tablets that are practical to change the way assessments affect learning efficiency. This paper examines how mobile-based digital assessment systems can improve the effectiveness and efficiency of the assessment process in learning. This paper aims to provide educators with practical guidance for integrating technology into their assessment systems by utilizing available mobile smartphones or tablets and conducting an analysis of the benefits and challenges faced. This study also explores how to maximize the use of smartphones or tablets as a support tool in conducting assessments and can enrich the learning experience of students through faster and personalized feedback. Assist educators in managing assessments in a more structured and measurable way. In addition, recommendations for the effectiveness of smartphone use in supporting the assessment system are also included so that educators can achieve optimal results in the learning process. This study contributes to the educational literature by providing new perspectives on the application of technology and the use of mobile devices to assessment by presenting empirical evidence that supports the effectiveness of assessment application in improving the quality of education
Future Prospects of Large Language Models: Enabling Natural Language Processing in Educational Robotics
Get PDFLarge language models (LLMs) have recently shown considerable promise in educational robotics by offering generic knowledge necessary in situations when prior programming is not possible. In general, mobile education robots cannot perform tasks like navigation or localization unless they have a working knowledge of maps. In this letter, we tackle the issue of making LLMs more applicable in the field of mobile education robots by helping them to understand Space Graph, a text-based map description. This study, which focuses on LLMs, is divided into several sections. It explores basic natural language processing (NLP) techniques and highlights how they can help create smooth education discussions. Examining the development of LLMs inside NLP systems, the paper explores the benefits and implementation issues of important models utilized in the education sector. Applications useful in educational discussions are described in depth, ranging from patient-focused tools like diagnosis and treatment recommendations to systems that support education providers. We provide thorough instructions and real-world examples for quick engineering, making LLM-based educational robotics solutions more accessible to novices. We demonstrate how LLM-guided upgrades can be easily included in education robotics applications using tutorial-level examples and structured prompt creation. This survey provides a thorough review and helpful advice for leveraging language models in automation development, acting as a road map for researchers navigating the rapidly changing field of LLM-driven educational robotics
Exploring Blended Learning Models Enhanced by Mobile Interactive Technology in Higher Education
Get PDFWith the continuous advancement of information technology, mobile interactive technology has been increasingly applied in higher education, providing robust support for blended learning models. These models integrate the advantages of traditional classroom teaching and online learning, enhancing the flexibility and interactivity of the educational process. In recent years, research on mobile interactive technology and blended learning has expanded. However, most studies have primarily focused on the application of technological tools and platform construction, with limited exploration of specific interaction models and learning experiences. Furthermore, research on student learning objective identification and group stratification have been constrained, failing to effectively meet the needs of personalized learning. In response, this study explores the integration of interaction models and learning experiences within a mobile environment for university students in blended learning settings. Additionally, methods for identifying learning objectives and stratifying student groups based on mobile technology were investigated, aiming to provide theoretical and practical guidance for the implementation of blended learning in higher education
Enhancing Learning Motivation through a Blended Learning Model: Integrating Mobile Devices and Virtual Reality
Get PDFWith the rapid development of information technology (IT), the application of mobile devices and virtual reality (VR) in education has become increasingly widespread, giving rise to a blended learning model that integrates these two technologies. This model, through the immersive experience provided by virtual environments and the convenience of mobile devices, effectively expands the temporal and spatial dimensions of traditional learning, offering new potential for enhancing students’ learning motivation. However, existing research has predominantly focused on either mobile learning or VR in isolation, with relatively limited exploration of how the combination of these technologies within a blended learning model impacts learning motivation. Moreover, most of the current methods are constrained to simple questionnaire surveys, lacking in-depth analysis of students’ interactive behaviours and the underlying mechanisms that contribute to motivation formation. In this study, a comprehensive evaluation model was constructed based on an examination of students’ interactions in the blended learning model and its effects on learning motivation. An empirical study was then conducted to provide innovative theoretical and methodological support for educational practice
Non-Invasive Detection of Iron Deficiency Anemia in Young Adults Through Finger-Tip Video Image Analysis
Get PDFIn this study, we propose the use of a convolutional neural network (CNN) (2+1) D-based model for the non-invasive detection of iron deficiency anemia using smartphones, addressing the need for accessible and efficient diagnostic methods. We have collected fingertip images from 909 young people, creating a robust dataset to train and validate the model through machine learning (ML), providing us with both spatial and temporal information. Our approach stands out for its simplicity and potential to be implemented in diverse contexts, which facilitates early and accurate diagnosis of anemia. The results show remarkable performance indices in the validation set: an accuracy of 0.9840, a precision of 0.9830, a sensitivity (recall) of 0.9840, an F1-Score of 0.9835, and an AUC ROC of 0.9878, demonstrating the model’s high ability to effectively classify anemic states. These findings validate the efficacy of the model to significantly improve the detection of iron deficiency anemia, promoting the use of mobile technologies and machine learning for more accessible and efficient diagnosis globally. This breakthrough marks a significant step towards improving access and efficiency in health diagnostics through smartphone technology and machine learning, opening new avenues for the effective management of anemia and other health conditions
A Hybrid Technique of Diagnosing Kidney Disorder Using Deep Neural Network
Get PDFThe growing demand for renal alternative medical diagnostics is driven by their non-invasive, early, real-time, and painless characteristics. Early diagnosis of kidney disease is crucial, given its severity as a health issue. This paper introduces a novel method that integrates residual networks with long short-term memory (LSTM) through deep feature analysis. The LSTM network is employed to extract global features from the iris, such as Wolfflin nodules and lacunae. The Res-Net classification system is then used to distinguish between kidney and non-kidney diseases. Various image-processing techniques are applied to segment, enhance, and normalize the iris image while extracting its distinctive features. Experimental results show that our model achieves 95% accuracy in classifying kidney and non-kidney conditions based on iris analysis. Future work aims to predict additional diseases using a deep neural network applied to iris images
Intelligent System Based on Round Robin and Genetic Algorithm for Managing Nurse Schedules in Health Centres in Peru
Get PDFThe inefficient assignment of nurse shifts poses a significant problem for medical centres due to stringent constraints and preferences, such as staffing requirements and hospital needs. In this study, we address these challenges by applying an improved hybrid algorithm that combines the Round Robin (RR) and Genetic Algorithms (GA) to develop an intelligent system for managing nurse schedules in hospitals in Peru. The development of the system consists of seven phases: dataset definition, variable definition, constraint definition, applying RR algorithm, applying GA, evaluation of algorithms, and intelligent system architecture. Nurse schedule records from a medical centre in Peru were used as the dataset. A total of 53 individuals, including head nurses and nurses, participated in the proposed system. The use of the proposed system resulted in a 99.71% reduction in “execution time” and a 30.08% improvement in “fairness in shift distribution” for nurses compared to previous methods. The key findings demonstrate significant improvements in both efficiency and fairness, highlighting the potential for future applications in health centres. The unique contributions of this research lie in the enhanced hybrid algorithm and its successful implementation in a real-world medical centre in Peru
Medical Support System for Spontaneous Breathing Trial Prediction Using Nonuniform Discrete Fourier Transform
Get PDFSpontaneous breathing trials (SBTs) represent a pivotal phase in the weaning process of mechanically ventilated patients. The objective of these trials is to assess patients’ readiness to resume independent breathing, thereby facilitating timely weaning and reducing the duration of mechanical ventilation (MV). Nevertheless, accurately predicting the success or failure of SBT remains a significant challenge in clinical practice. This study proposes a healthcare system that employs machine learning techniques to predict the outcome of SBT. The model is trained on respiratory flow and electrocardiogram (ECG) signals, employing the non-uniform discrete Fourier transform (NUDFT) for frequency domain analysis. The SBT prediction model has the potential to significantly enhance clinical decision-making by enabling the early identification of patients at risk for SBT failure, achieving an accuracy of 84.4 ± 3.2%