Online-Journals.org (International Association of Online Engineering)
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A Study of Farmers’ Digital Literacy on Their Entrepreneurial Income Based on Hierarchical Multiple Linear Regression
Get PDFThis study focuses on the role of farmers’ digital literacy on their entrepreneurial income. Through household interviews and questionnaire distribution, data from 130 farmers in Jianggu Town, Sihui City, Zhaoqing are obtained. On this basis, farmers’ digital literacy is categorized into five dimensions, and the effect of each dimension on farmers’ entrepreneurial income is empirically tested by means of hierarchical regression analysis. The results show that “the dependence on smartphone and internet,” “the level of digital access,” “digital information literacy,” and “digital creation literacy” can all significantly affect the income level of entrepreneurial farmers. Based on this understanding, this paper puts forward three policy recommendations: to formulate a practical digital literacy cultivation plan; to establish a unified digital aggregation platform; and to popularize digitalization and informatization through multiple channels
Interactive Tangible User Interface for Early Childhood Education: A Usability Study on Teaching Geometric Shapes in Kindergarten
Get PDFTangible user interfaces (TUIs) offer significant educational benefits, but their use in kindergarten, particularly for teaching geometric shapes, remains underexplored, with research largely focused on elementary education or higher levels. Moreover, usability evaluations assessing the ease of use of such tools by teachers as end-users are rarely conducted. This study aims to address these gaps by designing and evaluating TUI interactive surfaces as an aid for teaching geometric shapes in kindergarten. The proposed TUI design was evaluated for usability using the System Usability Scale with kindergarten teachers, achieving a score of 97.43, categorized as ‘Good’ (Grade A). This score indicates superior and acceptable performance, placing it in the highest quartile (4th quartile). Furthermore, functionality testing of the TUI system demonstrated a 100% success rate in its application as a geometric shape learning media. These findings suggest that the proposed TUI design is a viable and effective tool for teaching geometric shapes in kindergarten, offering a practical and user-friendly solution for educators. The study’s limited sample size, subject focus, and lack of teacher diversity affect generalizability, highlighting the need for future research to expand TUI applications, assess long-term impacts, and include diverse teacher demographics
University Students' Trust in AI: Examining Reliance and Strategies for Critical Engagement
Get PDFThe rising use of artificial intelligence (AI) for academic tasks, such as exam preparation and assignment assistance, has increased concerns regarding students’ uncritical reliance on AI technologies in university settings. This mixed-method study investigates the factors driving this reliance, drawing on qualitative data from 18 Indonesian students and quantitative data from 328 students across 18 universities. It proposes strategies for promoting critical engagement with AI. Findings show that while students generally trust AI for its efficiency, evidenced by an average score of 3.89 on the trust in technology scale (TTS), their confidence declines when tasks demand nuanced human judgment. This over-reliance poses risks to critical thinking and may encourage cognitive complacency; however, many students counter this by cross-referencing AI-generated information with credible sources. To mitigate blind trust and promote responsible AI use, this study recommends enhancing AI literacy through workshops, promoting effective prompt crafting and output verification strategies, and implementing institutional policies to guide ethical AI use. By integrating reminder prompts and AI literacy modules into curricula, institutions can encourage critical engagement with AI, ensuring it enhances rather than undermines educational outcomes. These practical guidelines aim to enrich students’ academic experiences while addressing the ethical and cognitive challenges associated with uncritical AI reliance
Fostering Lifelong Competency Development: A Digital Authentic Assessment Model for Vocational Internship Programs
Get PDFThis study aims to develop a digital authentic assessment (DAA) model based on competencybased learning (CBL) in the field work practice program at vocational high schools. The background of this study is the need to improve the quality of assessment in vocational education, which often still uses less effective, traditional methods. The urgency of this study lies in the need for assessments that are more relevant to the world of work so that Vocational High school graduates can be better prepared to face challenges in the industry. The methods used in this study include the development of the ADDIE model, validation by experts, and implementation of the assessment model in several vocational high schools, followed by evaluation through surveys and interviews. The evaluation showed that the model successfully increased student engagement and competency achievement, with high average scores in ease of use, material relevance, and assessment quality. The implications of this study suggest that the DAA model can contribute to innovation in vocational education assessment in Indonesia and improve the readiness of vocational high school graduates in the job market, although challenges related to technology access need to be addressed to ensure the sustainability of implementation
Integrating Mobile AI in Art Education: A Study on Children's Engagement and Self-Efficacy
Get PDFExisting literature has extensively explored the application of artificial intelligence (AI) in core subjects such as mathematics and language, but its use in children’s painting education remains limited. Addressing this gap is crucial, particularly in examining how AI can enhance children’s self-efficacy, motivation, and engagement in art. This study developed a scaffolded teaching method using a mobile AI synchronous generation drawings (MAI-SGD) tool and evaluated its effectiveness in primary school art education. A quasi-experimental design was adopted, involving 60 third-grade students divided into an experimental group (using MAI-SGD) and a control group (using traditional paper painting). Data were collected through motivation, self-efficacy, and art engagement scales. Results indicated that students using MAI-SGD demonstrated higher artistic engagement (p < 0.05) and significantly outperformed the control group in drawing motivation and creative self-efficacy. These findings suggest that MAI-SGD enhances creative interest and benefits technology-sensitive learners. The study offers empirical support for AI in children’s art education and provides insights for its reform, emphasizing individualized teaching. Future research should explore MAI-SGD’s applicability across cultural contexts and educational stages to advance its theoretical and practical contributions to art education
Evaluation of Learning Outcomes in Higher Education through the Integration of Interactive Mobile Technology and Big Data Analytics
Get PDFWith the rapid advancement of mobile Internet technology, interactive learning platforms, and big data analytics, they have become essential tools in modern higher education, particularly in the evaluation of learning outcomes. Traditional evaluation methods primarily rely on offline testing and questionnaire surveys, which often lack real-time adaptability, specificity, and multi-dimensional analysis, making it difficult to comprehensively reflect learners’ academic progress. Recent developments in big data technology and artificial intelligence have led researchers to explore data-driven approaches for evaluating learning outcomes. However, existing methodologies remain limited in feature selection, feature fusion, and model optimization, hindering their ability to fully capture learners’ behavioral features and academic performance. To address these limitations, a learning outcome evaluation model based on multi-semantic feature interaction and big data analytics was proposed. The model was designed with a feature embedding module, a multi-semantic feature interaction module, a two-dimensional squeeze-and-excitation module, and a feature fusion module to enhance evaluation accuracy and comprehensiveness. The feature embedding module extracts latent features by embedding multidimensional learner behavior data. The multi-semantic feature interaction module captures complex learning patterns by facilitating interactions among various features. The two-dimensional squeeze-and-excitation module optimizes feature representation, improving evaluation sensitivity. The feature fusion module integrates diverse features to enhance evaluation accuracy. Through these innovations, this study not only introduces a novel perspective for evaluating learning outcomes in higher education but also provides valuable insights for further research in educational technology
Impact of Personalized Learning Paths Supported by Mobile Technology on Student Academic Achievement
Get PDFWith the rapid advancement of mobile technology, mobile learning has become an integral component of modern education, particularly in the design and implementation of personalized learning paths. These paths enable tailored learning content and strategies based on students’ interests, abilities, and progress, thereby enhancing knowledge acquisition and improving academic achievement. Recent studies on personalized learning paths have primarily focused on content recommendation and learning outcome assessment, whereas limited attention has been given to student interaction relationships. In mobile learning environments, interactions among students and their mutual influence play a crucial role in optimizing personalized learning paths. However, existing research methodologies predominantly rely on theoretical analysis and static data processing, lacking dynamic modeling of complex student interaction patterns. This study aims to address this gap by analyzing student interactions within personalized learning paths and proposing a relationship discovery model based on an extended mobile interaction graph to help further optimize personalized learning paths. The findings are expected to contribute to the refinement of personalized learning path design and provide educators with real-time data support to enhance student academic achievement. By constructing a more precise interaction relationship discovery model, this study offers a novel theoretical framework and practical guidance for advancing personalized education
How IBM Supercharges Its L&D Efforts by Building Communities of Learners
Get PDFLearning and Development organizations (L&Ds) in the workplace face a challenge: Individual learners have at their fingertips a wide range of always-on, remotely accessible learning tools. When individuals come together to learn as a community, they improve their study habits and their results. So how do you meet learners where they are, and at the same time, encourage them to engage in a learning community to accelerate the outcome? IBM has expertise in creating community-oriented initiatives that help technology professionals improve the way they learn—and, ultimately, enhance the likelihood of success on IBM certification exams. Learners who engaged with the IBM community over an extended timeline have proven most likely to successfully complete learning assignments, earn industry-recognized credentials, and apply their new skills in their careers. This paper will share examples of some of the innovative, community-oriented initiatives that IBM has designed and implemented, with an emphasis on the “Study with Me” challenge—a community-building and support program created to complement IBM’s extant popular group study sessions
A Novel Low-Complex Optimized Resource Allocation Algorithm Using GWO Optimization Technique in Energy Scavenging for WBAN
Get PDFIn wireless body area networks (WBANs) powered by energy scavenging, effectively managing renewable energy is critical to ensuring delay-sensitive services. This paper proposes a novel low-complex optimized resource allocation algorithm using the Grey Wolf Optimization (GWO) technique to allocate resources, specifically energy and communication channels, and maximize user utility while guaranteeing the worst-case delay. To achieve this, firstly, a formulation of a user utility optimization problem that accounts for the stochastic nature of energy scavenging and consumption without requiring prior knowledge of these processes is performed. Utilizing GWO optimization techniques, optimization problems broke down into four sub-problems: battery management, collection rate control, transmission power allocation, and drop rate control. Additionally, the proposed algorithm’s performance is analyzed by examining the upper bounds of queues and required battery capacity. Simulation results confirm our theoretical analysis and the effectiveness of the proposed algorithm
Enhanced Intrusion Detection in IoT Smart Homes: Leveraging Binary and Multi-Class Classification Models
Get PDFThis study uses the MQTT-IoT-IDS2020 dataset, which contains normal traffic and attack traffic such as scan_A, scan_sU, Sparta, and mqtt_bruteforce attacks. This dataset is statistically extracted based on the unidirectional-based features packet header flow feature and has 19 features. This study used 10 best algorithms, namely ADABOST, eXtreme gradient boosting classifier (XGBC), stochastic gradient descent classifier (SGDC), random forest (RF), Naïve Bayes (NB), multi-layer perceptron classifier (MLPC), decision tree (DT), logistic regression (LR), linear discriminant analysis (LDA), and K-Nearest Neighbor (KNN) using binary class and multi-class. Using this classification algorithm, researchers measure the value of accuracy, precision, recall, F1 score, classification time, and receiver operating characteristic (ROC) curve to obtain the best classification algorithm. Measurement of accuracy value is done by dividing the dataset into 80:20 for training data and testing data, then validating the measurement of accuracy value with k-fold