Online-Journals.org (International Association of Online Engineering)
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Artificial Intelligence Tools Applied to Education: A Systematic Literature Review
Get PDFToday, the world is in a process of continuous change, and the digital era has positively influenced education by revolutionizing the traditional approach through the adoption of artificial intelligence (AI) tools. The aim of this paper is to analyze how AI is transforming teaching and learning processes. To this end, a systematic literature review was conducted, selecting 33 articles that addressed the research topic from various perspectives. To summarize the information, the PRISMA methodology was used, which involved an exhaustive search in academic databases, the application of inclusion and exclusion criteria to select relevant studies, and a detailed analysis of the chosen articles, developed between 2019 and 2024, which contained relevant characteristics for the study. In parallel, specific research questions were established to guide the review, addressing pedagogical, practical, ethical, and social aspects related to the integration of AI in education. The results highlighted the potential benefits of AI in learning personalization, teaching efficiency, and access to advanced educational resources. In addition, challenges in AI implementation were identified, such as the generation of incorrect information and biases in training data. It is concluded that AI can improve personalization of learning, teaching efficiency, and access to advanced resources, but it is crucial to address ethical challenges such as data privacy, equity in access to technology, transparency of algorithms, and impact on students’ autonomy and critical thinking
Flipped Classroom Mobile Learning Model and Its Application to Enhance Digital Intelligence Quotient among Secondary Students
Get PDFDigital intelligence quotient (DQ) is an essential skill for becoming a digital citizen. In Thailand, the development of DQ is a key policy initiative for youth development. Using mobile applications in a flipped classroom (FC) setting enhances learning outcomes and fosters the development of critical skills. This paper proposes a mobile application integrated with a FC model to improve secondary education students’ DQ. The study utilized an experimental research design with a one-group pretest-posttest approach to compare students’ DQ, analyzed using a paired sample t-test, and assessed student satisfaction. The sample consisted of 60 lower secondary school students under Thailand’s Nakhon Pathom Secondary Education Service Area Office. The research findings are as follows: 1) The developed learning model was rated as highly appropriate; 2) The content and technical quality of the FC mobile application was rated very good; 3) Students’ DQ significantly improved after using the application, with a statistical significance level of 0.05. Among the eight sub-skills, screen time management showed the highest improvement; and 4) Student satisfaction with the learning model was at the highest level. Based on these findings, the developed model can enhance students’ DQ and is adaptable for other audiences requiring digital navigation skills, making the results relevant to broader educational objectives beyond traditional coursework
Enhancing Astronomy Skills: The Role of Mobile Augmented Reality in Vietnamese Middle Schools
Get PDFThis study aims to assess the impact of mobile augmented reality (AR) applications on the astronomy competencies of middle school students in Vietnam, addressing the gap in practical educational mobile applications within science, technology, engineering, and mathematics (STEM) fields. Employing a quasi-experimental design, the study involved 438 sixth graders from four middle schools in Central Vietnam over eight weeks. The experimental group utilized AR applications, while the control group received conventional textbook instruction. Astronomy competencies were evaluated through pre- and post-tests, employing self-developed questionnaires based on standardized assessments. The results indicated that students in the AR group outperformed their counterparts in the control group across key competencies, including celestial motion, spatial orientation, and conceptual understanding. Notably, analysis of spatial learning revealed that female students in the AR group performed better than their male peers. Interviews with participating students suggested that AR applications enhanced interactions and facilitated a better understanding of abstract concepts, although some technical issues were reported. These findings highlight the potential of mobile AR to improve short-term learning outcomes and suggest its role in narrowing gender gaps in STEM education. Future research should investigate the long-term effects of AR on learners and explore its application across diverse learning environments while considering infrastructural and socio-cultural constraints
Optimization and Performance Analysis of Real-time Speech Translation Systems Based on Mobile Technology
Get PDFAs globalization deepens and mobile technology rapidly advances, the demand for crosslinguistic communication has been steadily increasing, making real-time speech translation systems a research focus. However, given the limited computational capacity and storage space of mobile devices, optimizing system performance while maintaining translation quality has become a critical challenge. Current optimization approaches for real-time speech translation systems primarily focus on improvements to model architectures and hardware acceleration, often neglecting a systematic study of model compression. This is particularly evident when handling real-time data, where achieving both high efficiency and translation accuracy remains difficult. To address these challenges, a model compression method based on the connectionist temporal classification (CTC) criterion was proposed, along with an in-depth study of parameter compression tailored for mobile applications. The research focuses on two key areas: first, model compression techniques based on the CTC criterion were explored to enhance the efficiency of real-time speech translation; second, parameter compression methods were investigated to significantly reduce resource consumption in mobile applications while preserving translation quality. The aim of this study is to improve the performance and user experience of real-time speech translation systems on mobile devices
AdaptiveMesh: Adaptive Federate Learning for Resource-Constrained Wireless Environments
Get PDFFederated learning (FL) presents a decentralized approach to model training, particularly beneficial in scenarios prioritizing data privacy, such as healthcare. This paper introduces AdaptiveMesh, an FL adaptive algorithm designed to optimize training efficiency in heterogeneous wireless environments. Through dynamic adjustment of training parameters based on client performance metrics, including central processing unit (CPU) utilization and accuracy trends, AdaptiveMesh aims to enhance model convergence and resource utilization. Experimental evaluations on heterogeneous client devices demonstrate the algorithm’s effectiveness in improving model accuracy, stability, and training efficiency. Results indicate a significant impact on CPU adaptation in preventing client overloading and mitigating overheating risks. Furthermore, the results of the one-way analysis of variance (ANOVA) and regression analysis highlight significant differences in CPU usage, accuracy, and epochs between devices with varying levels of hardware capabilities. These findings underscore the algorithm’s potential for practical deployment in real-world edge computing environments, addressing challenges posed by heterogeneous device capabilities and resource constraints
Novel Classification Approach for Thyroid Detection: Feature Enhanced AdaBoost Optimization with Max Voting
Get PDFThe need for enhanced methods in disease prediction is a significant challenge in the medical field. Current predictive models often face challenges such as limited accuracy, insufficient adaptability to diverse datasets, and inefficiencies in feature selection and model training. These limitations can hinder early diagnosis and effective management of thyroid conditions, which are vital for patient outcomes. The study introduces an innovative method for enhancing thyroid disease prediction using a machine learning study employs algorithms such as support vector machine (SVM), Naive Bayes (NB), K-nearest neighbor (KNN), logistic regression (LR), and stochastic gradient descent (SGD) in conjunction with filter, wrapper, and embedded feature selection methods across three distinct models. The study uses two thyroid datasets, one from Dew Medicare Ternity Hospital, Nagpur, and the other from the UCI thyroid repository, revealing the potential of the novel ‘FeatureBoostThyro’ approach for improving thyroid risk prediction across diverse datasets. The proposed method achieved accuracies of 98.10%, 97.47%, and 95.58% for the three models using the UCI dataset, and 97.42%, 98.71%, and 97.83% for the DMTH dataset. The novelty of this approach lies in its integrated pipeline that ensures the selection of the best features, systematic model training, and rigorous evaluation. This results in a robust, accurate, and reliable model that outperforms traditional approaches, making it a significant advancement in the field of disease prediction. The enhanced performance metrics, especially accuracy, highlight the potential of this method in clinical settings for early and accurate thyroid disease detection
Comparative Evaluation of PD Detection Using Deep Learning on IMFCCs Extracted from VMD
Get PDFThis paper presents a new method for extracting vocal features for the diagnosis of Parkinson’s disease (PD) via voice analysis applying variational mode decomposition (VMD). The classical method of extracting mel-frequency cepstral coefficients (MFCC) is compared to a new approach that generates coefficients named intrinsic mel-frequency cepstral coefficients (IMFCC). For this study, two audio databases were used: the SAKAR database containing 38 recordings and a PC-GITA database comprising 50 recordings. The signal preprocessing steps include frame segmentation, pre-emphasis, and filtering. The voice signal is then decomposed into intrinsic modes employing VMD. From these modes, the log-energy of specific components is calculated to extract the IMFCC. In this study, two types of classifiers were used: convolutional neural networks (CNN) and long short-term memory (LSTM). The results show that IMFCC provides a new perspective for representing vocal signals, capturing distinct features compared to classical MFCC. Notably, the IMFCC2 attained the highest accuracy of 100% adopting the CNN classifier. This approach could improve the performance of systems for identifying PD via voice analysis, offering a robust and complementary alternative to existing feature extraction methods
Artificial Neural Networks with K-Fold Cross-Validation and Feature Selection for Early Heart Disease Prediction
Get PDFThe most common reason behind death all over the world is heart diseases. These conditions are to hit hardest in low- and middle-income nations, where 80% of premature heart attacks could be prevented. In this regard, early diagnosis also plays an important role in increasing patient health and survival rate from heart disease. The purpose of this study was to improve the forecasting power by means of feature selection techniques and then apply K-Fold cross validation in combination with high-performance ensemble machine learning (ML) methods (J48, Artificial Neural Networks (ANNs), Logistic Regression, Naive Bayes, K-Nearest Neighbors) by utilizing a dataset of 401,958 patients. Our experimental results demonstrate that ANNs achieve the highest accuracy at 91.48%. They also record the lowest Mean Absolute Error (MAE) of 0.13, highlighting their precision in predictions. Additionally, ANNs exhibit a low root Mean Squared Error (RMSE) of 0.26, further indicating their reliability in modeling
The Academic Intensity Use of Chatbot-Based Artificial Intelligence and Its Relation to Academic Well-Being: A Correlational Study at the University of Jordan
Get PDFIn terms of artificial intelligence (AI) applications, chatbot-based AI such as ChatGPT have the potential to improve students’ academic well-being and success. Despite the wide spread of chatbot-based AI development, no studies have explored their correlation with psychological constructs in the realm of education. This study aimed to measure the academic intensity use of chatbot-based AI and academic well-being among undergraduates and investigate the correlation between these constructs. The data was gathered using a self-administered web-based questionnaire, which includes the Academic Well-Being Scale and the developed academic intensity use of chatbot-based AI scale (AIUCA). The study sample consists of 340 undergraduates from the School of Educational Science. The findings revealed a moderate level of usage of chatbot-based AI and a moderate level of academic well-being among undergraduates. It also demonstrated a significant positive correlation (r = 0.68) between the intensity use of chatbot-based AI and academic well-being (p < 0.01). These results recommend decision-makers in higher education encourage students to integrate chatbot-based AI with their learning processes and activities to improve their academic well-being
Impact of E-learning Tools (Moodle, Microsoft Teams, Zoom) on Student Engagement and Achievement at Jordan Universities
Get PDFThis study examines the impact of digital learning platforms—Moodle, Microsoft Teams, and Zoom—on student engagement and academic achievement at the University of Jordan. The study explores students’ familiarity with these e-learning tools and their effect on self-directed learning and performance. Over three months, data was collected from 450 students through an online questionnaire comprising closed questions. The analysis employed multiple regression models to consider variables such as gender, age, prior computer literacy, attitude towards emerging technology, learning preferences, and the implementation of e-learning within the university. Additionally, a qualitative content analysis identified the advantages and disadvantages of e-learning from the students’ perspectives. The findings indicate that strategic implementation of e-learning platforms significantly influences student perceptions more than individual contextual factors. Students appreciated the connectivity and accessibility provided by these platforms. Those with prior computer knowledge and those studying emerging technologies showed a particularly positive attitude towards e-learning. The study concludes that engagement with e-learning tools markedly enhances self-study habits and academic performance, underscoring the importance of integrating digital platforms into educational strategies