Online-Journals.org (International Association of Online Engineering)
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Integrating LLMs into Mathematics Laboratories Alongside CAS
Get PDFThis paper proposes a structured approach for integrating large language models (LLMs) and computer algebra systems (CAS) in mathematics laboratories to enhance learning in higher education. By combining LLMs with CAS, this method provides a balanced environment where students engage actively in solving complex mathematical problems. LLMs assist in conceptual understanding through natural language interaction, while CAS ensures precise computational accuracy. Aligned with inquiry-based learning (IBL) principles, the approach guides students through stages that encourage exploration, collaboration, and reflection. This integration maximizes both pedagogical and functional opportunities, providing a balanced approach that enhances problem-solving skills and prepares students for the evolving use of LLMs in mathematics education
Enhancing Informatics Teacher Training with Interactive Mobile Case Technologies
Get PDFThe study explores the use of case-based methods to enhance the professional competencies of future computer science teachers, focusing on communication, creativity, and problem solving skills. Conducted at L.N. Gumilyov Eurasian National University, the research involved instructors and students in collaborative activities to evaluate these skills. The findings indicate that while communicative and creative approaches were commonly applied, interpersonal conflicts often emerged during problem solving exercises. Most participants emphasized the critical role of effective communication in succeeding within modern educational settings. The study underscores the potential of case-based technologies to develop essential teaching skills and adapt to the demands of contemporary education. The conclusions suggest strategies for improving teacher training, advancing adaptive pedagogy, and leveraging technology to transform education. These insights contribute to the broader academic discussion on incorporating innovative methods into teacher education, with implications for institutional policies and community advancement
Adaptive Learning Systems Based on Deep Learning for the Diagnosis and Support of Learning Disabilities
Get PDFIn the current field of educational technology, adaptive learning systems have become a key tool in supporting personalized learning. Particularly for students facing learning disabilities, how to provide effective diagnosis and support has become an important topic. Learning disabilities often hide within the behavioral characteristics of learners, requiring detailed analysis for identification and intervention. This study aims to leverage deep learning technology, especially the XGBoost algorithm, to improve the accuracy of diagnosing learning disabilities. It also seeks to implement knowledge transfer across different subjects through transfer learning algorithms, thereby overcoming learning disabilities. Research background indicates that despite the increasing application of adaptive learning systems, existing methods for diagnosing and supporting learning disabilities still have limitations. These systems often fail to accurately parse complex behaviors and cognitive patterns of students, resulting in insufficiently personalized assistance. Moreover, students with learning disabilities often encounter more difficulties in interdisciplinary learning, and existing learning systems have not effectively supported their cross-domain knowledge transfer. The diagnostic method based on XGBoost and the adaptation approach through transfer learning proposed in this paper have been experimentally verified to effectively improve the prediction accuracy of learning disabilities and promote knowledge transfer between different subjects for students, helping them overcome learning disabilities in specific disciplines. This research not only has significant implications for enhancing the intelligence level of adaptive learning systems but also positively impacts achieving educational equity and enhancing the overall learning experience of learners
Adoption of Mobile Learning: Education’s Use of App-Based Learning Management Systems & Ethical Implementation
Get PDFThis study examines the adoption of mobile learning (M-learning) through app-based learning management systems (LMS) in higher education, aiming to identify key factors influencing students’ usage behavior and continuing adoption intentions. Using an integrated theoretical framework combining the unified theory of acceptance and use of technology (UTAUT) and the expectation-confirmation model (ECM), the research employs a quantitative, cross-sectional design. Data were collected from 399 university students with M-learning experience, using a structured online questionnaire distributed via institutional portals and academic social media channels. Partial least squares structural equation modeling (PLS-SEM) was used to analyze the data and test direct and mediating effects among key variables. The findings reveal that performance expectancy and satisfaction are the strongest predictors of M-learning adoption, while interface design quality and facilitating conditions play significant but secondary roles. The study confirms satisfaction as a critical mediator between initial expectations and long-term usage intentions, highlighting its central role in sustaining engagement with M-learning platforms. These results contribute to educational technology literature by validating an integrated adoption model specifically for app-based LMS and providing empirical evidence on the hierarchical importance of different adoption factors. Practical implications suggest that educational institutions should prioritize user experience design and performance-related functionalities when implementing M-learning solutions, while also ensuring reliable technical support
Redesign of a Virtual Jet Pump Laboratory for Higher Process Engineering Education
Get PDFThe digital transformation increasingly requires the development of competencies for learning and working in the context of Industry 4.0. To meet this need, a practical action research project was conducted to design and implement a virtual instructional laboratory for fluid mechanics in a higher process engineering education course. This paper provides a detailed overview of the key steps and outcomes of the project, including the design of a virtual jet pump experiment using a game engine, the implementation of a real-world scenario (RWS) that was developed with a special constructive alignment (CA) checklist for instructional design of laboratory learning, and the corresponding empirical investigation. The findings demonstrate that the integrated approach employed in this project can enhance learning outcomes related to competencies required in the Industry 4.0 work environment, despite the challenges encountered by students in mastering the RWS. This paper concludes by exploring some potential future directions for further work in the field
Enhance the Security of Data Based on Server-Side Encryption in a Cloud Environment
Get PDFCloud computing has gained popularity due to its advantages for users and providers. This study examined and analyzed the effectiveness of server-side encryption (SSE) aimed at enhancing the security of cloud data storage and transfer. The researcher used Amazon Web Services key management service keys (AWS KMS) to perform the simulation over the AWS platform. The encryption method was integrated with identity and access management (IAM) to help with accessibility control. The findings demonstrated that server-side encryption (SSE), when merged with KMS and IAM roles, helps with restricted access and secures data from unauthorized users. The results show that unauthorized users only view ciphertext rather than plain text, highlighting the security feature of server-side encryption
Metabolomics Pathway Prediction Using Enhanced-Graph Convolutional Networks with Graph Attention Networks
Get PDFMetabolomics, the comprehensive study of small molecules in biological systems, has a central role to play in the diagnosis of diseases, biomarker detection, and the design of new drugs. Although there have been major breakthroughs in analytical toolsets such as mass spectrometry (MS) coupled with chromatography, it is hard to predict metabolomics pathways because biochemical interactions are inherently complex. To meet this end, the current research suggests a deep learning-based approach using graph neural networks (GNN), which have shown high efficiency for graph-structured biological data. We specifically propose an enhanced graph convolutional network integrated with graph attention networks (EGCNGAT) to enhance pathway prediction performance. The hybrid framework employs graph convolutional networks (GCN) to represent molecular structural data and graph attention networks (GAT) to provide context-sensitive feature importance, thus improving the model’s capacity for learning complex pathway patterns. Comparative experiments against current deep learning approaches show that the introduced EGCN-GAT model obtains an accuracy of 98.90 percent, which is a 0.26 percent increase compared to the baseline MLGL-MP model. In addition, it demonstrates a 0.94 percent gain in precision as well as a slight gain in recall. The findings validate the performance of the proposed method and highlight its utility for developing pathway-level predictions in metabolomics studies.
Exploring AI-Powered Mobile Technologies in Educational Leadership: Perceptions, Challenges, and Opportunities
Get PDFThe implementation of AI technologies in the nuances of human life has proliferated, with increasing evidence of these technologies’ usefulness to activities in both academia and administration. However, to reap their full benefits, it is pertinent for teachers to understand the distinctions between current tools and to be prepared and motivated to apply them. To contribute towards achieving such understanding, this study conducts an evaluation of the level of perceived usefulness of AI technologies in academic and administrative activities. Data collection was conducted with 70 Jordanian school teachers using mixed methods. Based on the results, teachers reported low-moderate levels of perceived usefulness (M = 1.86, SD = 0.478 to M = 2.15, SD = 0.374) for AI tools. Moreover, the results of an independent samples t-test did not support statistically significant differences based on respondents’ gender (t = -.870, p = .387 > 0.05) or age (t = -1.754, p = 0.087 > 0.05). These findings highlight the potential of AI to improve leaders’ skills. This study is among the few to tackle school leaders’ perceptions of AI technologies on a global scale and in the Arab nations. The study provides evidence of the significance of integrating AI technologies into school systems and highlighting their gender and age differences in AI leader education
Learning Management by Online Classroom Collaboration with a Community Enterprise: Taking Lessons from Thailand
Get PDFThis study proposes and evaluates a tri-dimensional model for online learning in response to disruptions caused by a global health crisis. It examines learners’ attitudes, student and community stakeholder satisfaction, and differences in academic performance between STEM and non-STEM students. A total of 288 undergraduate students enrolled in business and technology programs participated in the study. Utilizing a mixed-methods approach, the study explores three core dimensions: (1) learners’ attitudes toward online learning, assessed using the uses and gratifications theory (UGT), focusing on cognitive, affective, and social needs, and analyzed using structural equation modeling (SEM); (2) student satisfaction and community feedback on online marketing deliverables, measured using descriptive statistics; and (3) comparative academic performance across disciplines, evaluated using inferential statistical tests. The findings reveal that all UGT variables significantly influence learners’ attitudes. Students expressed high satisfaction with the model’s real-world relevance, and community stakeholders reported similar satisfaction with the student-generated outputs. Furthermore, STEM students achieved significantly higher academic performance than their non-STEM peers
Trust, Information Quality, and User Perceptions in Mobile Social Networks: Implications for Mobile Learning and Health Crisis Communication
Get PDFThe coronavirus COVID-19 outbreak has become a global crisis, where most nations are suffering from severe health and economic consequences. As known, the probability of infection is high, so it is important to understand the public perception of trust in sources of information, especially mobile social networks (MSNs). As a result, this information will assist in better informing public health messaging. In this study, we surveyed the users of MSNs to understand their perceptions regarding the utilization of MSNs as communication platforms during the COVID-19 outbreak. An online survey was utilized to collect the responses of 854 MSN users in mid-November 2024 using a questionnaire that we developed. The sample of participants was fairly similar to the general MSNs population in terms of gender, age, usage time, and education level. We found that 60% of the respondents mentioned that there is a lot of misleading, inaccurate, and anonymous information. The majority of the respondents stated that utilizing MSNs can be a helpful tool to promote and explain strict infection prevention policies proposed by the government to control the outbreak. Based on our findings, health professionals should lead the COVID-19 response to enhance trust in information sources. Consequently, it is crucial to emphasize and enhance the understanding and views of the use of MSNs during and following the crisis period