Online-Journals.org (International Association of Online Engineering)
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The Application of Mobile Technology in Educational Administration to Foster Continuous Learning and Professional Development
Get PDFThis study explores the application of mobile technology in educational administration to promote continuous learning and professional development. By conducting a quantitative analysis of the interaction level and activity consistency within mobile networks and proposing strategies based on mobile network knowledge sharing mechanisms, this research aims to enhance our understanding of how mobile technology operates in educational management. It also aims to offer theoretical support and practical guidance for educational management practices. Currently, the integration of mobile technology in educational management has become a prominent research topic. Existing studies, however, lack sufficient quantitative analysis regarding interaction levels and activity consistency within mobile networks. Moreover, research on mobile network knowledge-sharing mechanisms that foster continuous learning and professional development is relatively limited. Therefore, this study addresses these research gaps and offers new perspectives and methodologies for a deeper understanding of the application of mobile technology in educational management
Quality Optimizing Teaching Decisions in Flipped Classrooms Through Data-Driven Strategies
Get PDFIn the context of digitalized educational research, the rapid advancement of internet technologies and big data has catalyzed the transformation of traditional teaching models. The flipped classroom, recognized for its flexibility and efficiency, has garnered significant attention. However, the challenge of scientifically optimizing teaching decisions in flipped classrooms to maximize educational outcomes remains critical. Previous studies have achieved some progress in optimizing teaching decisions within flipped classrooms, yet they often suffer from a lack of methodological diversity, inadequate consideration of multi-level constraints, and struggle to adapt to dynamic teaching environments. Addressing these deficiencies, this research introduces a hybrid evolutionary algorithm combining differential evolution and greedy backtracking. Defined and classified constraints within flipped classroom teaching decisions, the construction of constraint networks, and the creation of multi-level decision spaces are transformed and solved through this hybrid algorithm, offering a systematic optimization strategy. Case analysis confirms the effectiveness and practicality of the proposed method, aiming to bolster decision-making in flipped classrooms and advance the development of digitalized teaching
Examining How Work Environment Effects the Work Engagement of Instructional Designers and the Moderating Role of Psychological Capital
Get PDFThe primary purpose of this study was to determine if and to what extent there is a difference in the overall work engagement of instructional designers who are either working predominantly at home or predominantly in the office in the United States and the secondary purpose of this study was to examine the moderating effect of psychological capital on the predictive relationship between work environment and work engagement. Based on a sample size of 345, the results illustrate that the work engagement scores for those who work predominantly in the office (mean rank = 221.89) were statistically significantly higher than those who work predominantly at home (mean rank = 122.67), U = 23431.50, z = 9.25, p < .001, but did not indicate that the interaction effect between types of work environment and psychological capital on work engagement was statistically significant (B = -0.04, se(HC3) = 0.07, p = 0.58. The results extend research on work engagement by providing evidence that there is a statistically significant difference in mean ranks of work engagement scores between those who worked predominantly at home and those who worked predominantly in the office
Nurturing Feedback and its Impact on Self-Efficacy, Empowerment, and Professional Growth in Educational and Corporate Environments
Get PDFThis study examines the impact of feedback quality in design education on students' self-efficacy. It argues that nurturing feedback can positively influence students' self-efficacy, leading to increased effort, perseverance, and eagerness to embrace challenges. A case study conducted in a senior design studio course at the American University of Sharjah (UAE) explores the effects of feedback. It proposes strategies to transform it into a nurturing tool for students' self-efficacy. The insights gained provide valuable guidance for all instructors involved in educational environments where feedback plays a pivotal role in students' growth and development. Furthermore, the findings have broader implications for the corporate design sector, emphasizing the importance of nurturing feedback to empower employees and foster their professional growth. Empowered employees are more likely to embrace challenges, take initiative, and contribute to organizational success, making nurturing feedback a vital factor in promoting a positive and productive work environment
Fostering Inclusive Recruitment Interviews with Intelligent Digital Humans: A Diversity and Inclusion Training Initiative
Get PDFThis article is about an intelligent digital human model enhanced by artificial intelligence, designed to meet the requirements from a multinational company in need of training for their human resources personnel on bias-free recruitment interviews. We have been creating a new generation of avatars with social intelligence, who are capable not only of presenting a wide variety of topics in a dynamic and engaging manner, but also of interacting with the audience and communicating emotions and moods. We have been customizing avatars for role plays, building them as real interlocutors who facilitate training in how to handle difficult conversations by including aspects such as non-verbal communication, different communication styles, and diversity and inclusion. Practicing conversations with avatars accelerates learning from experience without the risks associated with learning in the field. At the end of each interview, timely feedback is provided so learners can determine how to improve their performance. These digital humans are able to perform like realistic human beings, challenging the interviewer both at a verbal and para-verbal level, as well on the cognitive and the emotional levels – making it easy for the interviewer to get trapped into biases and false assumptions. The key message is this: diversity and inclusion best practices are first of all about mindset
What Drives Student Engagement? A Community Engagement Framework for Online Education
Get PDFCultivating a sense of community can be difficult in online education. We build on the work of Redmond et al. [9] and their proposal of an Online Engagement Framework for Higher Education to investigate online course designs and pedagogies that are likely to foster increased perceptions of social capital, confidence, and resilience in the learning process among students. Our research builds on the student-engagement themes proposed by Redmond et al.: cognitive, behavioral, collaborative, and emotional engagement; these forms of engagement can be facilitated by online course design and pedagogy. These types of engagement, in turn, help foster students’ social engagement, which, as a manifestation of social capital, is linked to better learning outcomes, increased confidence, and resilience in the online learning process. Following a comprehensive literature review that draws on concepts from the community of inquiry and community of practice frameworks, we propose a new model—a Community Engagement Framework for Online Education. With this model, we aim to identify the elements of online course design and pedagogy that correlate with increased student social engagement and, therefore, increased students’ social capital. Our model is more theoretically complex and analytically sound than previous proposals, rendering applicability through testing with real-world data. Future studies can use this model to survey online students and cross-validate it using path analysis and structural equation modeling. Future research can also survey online instructors to identify practical uses of our proposed engagement constructs
Gamification in E-learning: Bridging Educational Gaps in Developing Countries
Get PDFIn the field of education, developing countries face numerous challenges, including limited resources, inadequate infrastructure, and a shortage of qualified educators. This research paper explores the transformative potential of gamification in e-learning as a strategic approach to mitigate these challenges and bridge educational gaps in developing countries. The study investigates the impact of integrating gamified elements into the e-learning environment, aiming to enhance student engagement, motivation, and knowledge retention. The research employs a mixed-methods approach, combining quantitative analysis of student performance metrics with qualitative insights from educators and learners. The investigation evaluates the effectiveness of gamification in fostering a positive learning experience and addressing specific educational gaps prevalent in developing nations. Additionally, the study examines the adaptability of gamified e-learning platforms to diverse cultural and linguistic contexts, ensuring inclusivity and relevance. The findings of this research contribute valuable insights to the discourse on leveraging technology for educational advancement in resource-constrained settings. By shedding light on the potential benefits and challenges of gamification in e-learning within the context of developing countries, this paper aims to inform policymakers, educators, and researchers on innovative strategies to enhance the quality and accessibility of education. Ultimately, the research seeks to provide practical recommendations for the integration of gamified e-learning approaches, fostering sustainable educational development in the global context
Generative AI as Virtual Healthcare Assistant for Enhancing Patient Care Quality
Get PDFThis study investigates the potential of Chat Generative Pre-Trained Transformer (ChatGPT) as a virtual healthcare assistant to enhance the quality of patient care. Inadequate patient care within healthcare systems is a key issue that has resulted in lower satisfaction and medical errors. Virtual healthcare assistants, exemplified by ChatGPT, have emerged as a promising solution to mitigate these challenges. A comprehensive literature review compares the benefits and drawbacks of using virtual healthcare assistants with those of human healthcare providers to assess their effectiveness in enhancing patient care. The article discusses the ChatGPT development process, including the data sources used, training and validation, and the integration of this technology into healthcare systems. The results of testing ChatGPT in patient care, including patient feedback, are provided. The study interprets these findings and indicates that ChatGPT can significantly enhance patient care. The implications of implementing virtual healthcare assistants in the healthcare sector are also explored, along with potential future research areas for enhancing ChatGPT. This study provides important new insights into how virtual healthcare assistants might enhance patient care and offers recommendations for healthcare organizations and legislators on leveraging ChatGPT. It shows that the astonishing development in patient care, known as ChatGPT, has the potential to revolutionize the healthcare industry
Optimizing Blood Glucose Regulation in Type 1 Diabetes Patients via Genetic Algorithm-Based Fuzzy Logic Controller Considering Substantial Meal Protocol
Get PDFEffective management of blood glucose levels in individuals with type 1 diabetes, especially after meals, is crucial for diabetes care. Artificial pancreas systems (APS) perform automated insulin delivery in subjects with type 1 diabetes mellitus (T1DM). In this study, an optimized fuzzy logic controller was designed to achieve a euglycemic range after a substantial meal intake. All in silico simulations were performed using the MATLAB/Simulink environment, leveraging control variability grid analysis (CVGA), and the performance of the controller was evaluated. The proposed controller is based on a fuzzy-logic control law designed in three stages. First, a nonlinear framework of the glucose-insulin regulatory system was identified based on the heavy meal protocol of three patients given as follows: for subject ID 117-1, a total of 295 gCHO; for subject ID 126-1, 236 gCHO; and subject ID 128-1, 394 gCHO over a day. Then, an iterative tree structure was employed to establish a stabilizing control rule for insulin delivery, integrating inputs from two Mamdani Fuzzy Inference System (FIS) objects. Finally, a genetic algorithm refines the control system by fine-tuning the uncertainty of the fuzzy membership functions. Two scenarios were considered for three patients to assess the performance of the proposed controller. The results indicated its effectiveness under various conditions, achieving a time in the range of 61.25%, 71% and 61.10% respectively for the three subjects. The obtained results are analyzed and compared with IMC and multi-objective output feedback controllers. The findings of the study reveal that the proposed controller shows promising advancements in tailored strategies for type 1 diabetes patients, outperforming the other controllers in terms of blood glucose regulation
Statistical Analysis of Features for Detecting Leukemia
Get PDFIn this age of digital microscopy, image processing, statistical analysis, categorization, and systems for decision-making have become essential tools for medical diagnostics research. By visualizing and analyzing images, clinicians can identify anomalies in intracellular structure. Leukemia is a cancerous condition marked by an unregulated increase in aberrant white blood cells (WBCs). Recognizing acute leukemia tumor cells in blood smear images (BSI) is a challenging assignment. Image segmentation is regarded as the most significant step in the automated identification of this disease. The innovative concavity-based segmentation algorithm is employed in this study to segment WBC in sub-images from the ALLIDB2 database. The concave endpoints and elliptical features are used in the segmentation step of convex-shaped cell images. The procedure involves the extraction of contour evidence, which detects the visible section of each object, and contour estimation, which corresponds to the final object’s contours. Following the identification of the cells and their internal structure by concavity-based segmentation, the cells are categorized based on their morphological and statistical features. The method was evaluated using a public dataset meant to test classification and segmentation approaches. The statistical tool SPSS is used to independently check the significance of derived features. For classification, significant features are passed into machine learning techniques such as support vector machines (SVM), k-nearest neighbor (KNN), neural networks (NN), decision trees (DT), and Nave Bayes (NB). With an AUC of 98.9% and a total accuracy of 95%, the neural network model performed better. We advocate using the neural network model to identify acute leukemia cells based on its accuracy