Online-Journals.org (International Association of Online Engineering)
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Mobile Marketing Training towards Increasing Business: A Case Study of Agri-Food Micro-Enterprises
Get PDFMobile marketing (MM) refers to marketing through mobile platforms and has become a phenomenon in the business world. Many sectors utilize online applications for various business activities, including promotion and marketing. However, small and medium-sized enterprises (SMEs), especially those in the agricultural sector, face numerous challenges in using and determining training that is appropriate to their level of ability. Therefore, the objective of this study is to discuss the use of digital marketing training management (DMTM) by agri-food micro-enterprises in Peninsular Malaysia. A total of seventeen participants took part in this case study, and data were collected through surveys and semi-structured interviews between March 2022 and December 2022. The results led to the development of a DMTM model for agri-food micro-enterprises
Using Digital Video Recordings in Class Activities for Enhancing Mathematics Pre-Service Teachers’ Reflective Thinking
Get PDFDigital video recordings (DVRs) have become an effective tool in sustainable teacher education. In the current study, our aim was to investigate the impact of utilizing mobile applications for DVRs in classroom activities, designed to address misconceptions of mathematical concepts, on pre-service teachers’ capacity of reflective thinking. Two groups of approximately 10 PSTs participated in an education program. The participants learned as third-year students in the Department of Mathematics Education. Interviews and digital video-recorded observations were used as data collection tools. Deductive and inductive content analysis would be used as data analysis tools. The study showed that the DVR environment constituted a sustainable educational environment that supported PSTs’ development in writing reflections on their mathematics teaching. These results underscore the significance of using digital platforms for PSTs’ education, particularly for promoting metacognitive thinking practices such as reflective thinking
Optimization of Online Learning Resource Adaptation in Higher Education through Neural Network Approaches
Get PDFWith the advent of the digital era, the quantity and variety of online higher education learning resources have expanded rapidly. The efficient adaptation of suitable resources to meet the needs of learners with specific requirements has become crucial for improving learning outcomes. Although current online learning resource recommendation systems have made some progress in matching resources, they still face challenges related to the inadequate integration of resource features and a superficial understanding of learners’ needs. These challenges hinder the achievement of personalized and precise matching, affecting learners’ study efficiency and the effective utilization of educational resources. This study first analyzes the importance of adapting online higher education learning resources and the limitations of existing research. Subsequently, a novel neural network optimization strategy is proposed. The research comprises two main parts. Firstly, the self-attention-convolutional neural network (SA-CNN) model is employed for the deep integration of the content features of online learning resources. This aims to enhance the comprehensiveness of resource descriptions. Secondly, a deep-metric attention model is introduced to accurately model and adapt to learners’ needs. This approach not only optimizes the feature representation of learning resources but also enhances the sensitivity and accuracy of the recommendation system towards learners’ requirements. This study is of significant importance for improving the performance of higher education online learning resource recommendation systems. It also provides new insights into the construction of personalized learning paths and ensuring the balanced allocation of educational resources
The Impact of Educational Informatics on School Management Decision-Making in the Context of Big Data
Get PDFIn the domain of private education, particularly vocational education, school management encounters a complex and variable environment with diverse demands. As the advancement of educational informatics progresses, schools have accumulated a substantial amount of data, including student learning behavior, teaching data, and classroom utilization statistics. However, the efficient utilization of these data for scientific management decisions to enhance the quality of education and resource efficiency presents an urgent challenge. The study of the impacts of educational informatics on school management decision-making is of significant importance. It aids administrators in comprehensively understanding and analyzing the allocation and usage of educational resources, thereby enhancing management efficiency and teaching quality and further promoting the development of educational informatics. Despite some progress in data analysis and decision-making methods within educational informatics, deficiencies remain. Traditional methods often overlook comprehensive considerations of spatiotemporal data, failing to accurately reflect the dynamic changes in educational resources. Existing decision-making methods, predominantly based on single-agent models, lack studies on multi-agent collaborative decision-making, resulting in suboptimal decision outcomes. This study comprises three main components: first, the data modeling of educational informatics based on a spatiotemporal data model; second, an intelligent decision-making framework for school management under a reinforcement learning mechanism; and third, the implementation of an intelligent school management decision-making method based on multi-agent reinforcement learning. Through these investigations, the scientific nature and efficiency of school management are enhanced, providing new ideas and methods for management decision-making in other educational domains
Harnessing Educational Big Data Analytics for Decision-Making in Enhancing School Teaching Quality
Get PDFThe application of educational big data analytics holds significant importance in enhancing decision-making processes for school teaching quality. This study explores the effective utilization of educational big data analytics technologies to support the improvement of teaching quality in schools. Initially, the challenges and needs faced by current school teaching quality decision-making were analyzed, highlighting the critical role of educational big data analytics in this context. Subsequently, the limitations and gaps in existing study were identified through a review of related studies, underscoring the study value of this study. Based on this foundation, this study progresses through an examination of the decision-making factors that influence school teaching quality, problem description and model assumptions, construction of decision models, and model solutions using genetic algorithms. By analyzing key factors and constraints in the decision-making process for school teaching quality and integrating optimization algorithms, a viable decision support model was proposed and empirically analyzed. This study aims to provide a scientific basis for school administrators and decision-makers, thereby promoting continuous improvement in school teaching quality
Structured Operations Training and Evaluation: A Case Study
Get PDFOnboarding for a company with remote, hybrid, and in-office workers can be challenging, especially if the workers are spread across the United States. This case study examines a US-based medical records company. This company met with stakeholders and developed a structured onboarding process which was implemented in December 2021. The case study follows employees who started between January and December 2022, analyzing their improvement in 30-, 60-, and 90-day assessments as well as their retention. While the data do not support a statistically significant increase, this process anecdotally improved the onboarding experience for new employees
Case Study: Engaging Learners Through Curiosity, Community, and a Sense of Exploration
Get PDFThis case study explores the collaboration between Smithsonian Affiliations and Desklight to develop a digital learning hub for Smithsonian’s Affiliates Program. Anchored in humancentered design, the design process relied heavily on ongoing insights from current Affiliates to identify the most useful information to share in this online resource. These insights guided the development of unique Learning Excursions within the learning hub that offered curated worksheets, real-world impact scenarios, and helped prospective Affiliates envision what it would look like to join forces with Smithsonian. The success of this learning hub is attributed to stakeholder involvement, personalized learning, real-world examples, flexible accessibility, community building opportunities, and a design thinking approach. This work serves as a blueprint for organizations aiming to enhance programs, foster meaningful engagement, and drive transformative outcomes by using learning as a benefit
Collaborative Learning of Macroeconomics Through the Lens of Universal Design for Learning (UDL)
Get PDFThe paper shows that implementation of Universal Design for Learning (UDL) for collaborative learning through teams in a Macroeconomics course can help to transform students’ educational experience to become lifelong learners. The UDL uses a portfolio of assessments in the course and team presentation happens to be one of them. In this paper students form teams through self-selection exercise and work in teams throughout the semester to produce and present research outcomes of analyzing macroeconomic policy in a globally connected world. The presentation requires students to apply their learning of country specific macroeconomic issues on global policy decisions. This enables transforming classroom learning into an immersive experience for future career choices of students developing transferable skills in the process
Classification Model Using Transfer Learning for the Detection of Pneumonia in Chest X-Ray Images
Get PDFIn the current global context, there has been a significant increase in respiratory system diseases, particularly pneumonia. This disease has a higher incidence of mortality in children under five years old and adults over 60 years old because it leads to complications if not treated in time. This research leverages convolutional neural networks (CNNs) to classify images, specifically to detect the presence of pneumonia. The data processing methodology utilized in this study is CRISP-DM. The dataset consists of 5,856 images of anteroposterior chest X-rays downloaded from the open repository “Kaggle,” divided into 5,216 images for training, 16 for validation, and 624 for testing. Preprocessing involved image augmentation through modifications to the original images, scaling, and batch division in tensor format. A comparative analysis was conducted among the transfer models: DenseNet, VGG19, and ResNet50 version 2. Each transfer model was the header of a CNN with four subsequent layers. The models underwent training, validation, and testing phases. The test’s results showed that DenseNet achieved an accuracy of 0.87, VGG19 achieved 0.86, and ResNet50 achieved 0.91. These results affirm the effectiveness of ResNet50 in image classification, considering that the model’s output is binary, where 0 represents that the patient does not have pneumonia and 1 indicates that the patient has pneumonia
Gait Analysis—A Tool for Medical Inferences
Get PDFGait analysis is a valuable tool for making medical inferences and improving the diagnosis and treatment of mobility issues. This project aims to leverage gait analysis in addressing two important challenges: detecting knock knees and monitoring patients with Parkinson’s disease for falls. The project proposes the integration of gait analysis with yoga therapy to provide a unique and effective approach for correcting knock knees. A web user interface is developed to enable individuals to access the system, receive accurate feedback on their gait, and access yoga postures tailored to target knock knees. Additionally, a fall detection system is designed to monitor patients with Parkinson’s disease and notify caregivers or guardians in case of a fall. The implementation involves utilizing deep learning models, such as OpenPose model, a widely adopted deep learning framework for pose estimation and MediaPipe, another recognized framework used for building multimodal applied machine learning pipelines, to analyze gait patterns and detect knock knees and falls. The project aims to empower individuals in improving their gait, correcting knock knees, and enhancing their physical health, ultimately improving their quality of life and well-being