Online-Journals.org (International Association of Online Engineering)
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Enhancing Language Learning Experience with Augmented Reality Games: A Systematic Review of Empirical Studies from 2019–2023
Get PDFThis systematic review study synthesizes and presents research findings relevant to the instructional application of gamified augmented reality (AR) tools in language learning from 25 peer-reviewed research from 2019 to 2023 with preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. The review purpose was to identify: (1) the commonly adopted AR games in language classrooms; and (2) students’ perceptions of learning languages with AR games. The main study findings suggest the focus of the past research efforts was placed mostly on self-designed AR games while there is a relative lack of research interest in premade or existing games for language learning. In terms of students’ perceptions, the findings indicate that students generally have positive perceptions toward AR games in the language learning experience. Moreover, games that feature a combination of 3D models and animations, including peer collaboration and competition, seem to be the favorite for digital native students. The findings also reported concerns from the students such as the need for more assistance from the instructors. Suggestions for modification of instructional design and future research in the field of AR-integrated language learning are also provided
Strengths and Challenges in Teaching and Learning in Education with the Use of Information and Communication Technologies
Get PDFThe purpose of this paper is to explore the existing scientific literature on the assets and difficulties in teaching and learning processes through the implementation of information and communication technologies (ICT) in education. The study employed a qualitative methodology with a hermeneutic approach across three stages: (1) identification of the research problem; (2) collection of 50 relevant publications from various databases that contributed to the topic; and (3) a reflective analysis of the importance of implementing ICT in teaching and learning. The findings pointed out five emerging themes: 1. The digital divide in Latin America, 2. The challenges of ICT integration, 3. Digital competencies and skills, 4. The impact of technology on education, and 5. Public policies. Findings show a high degree of digital inequality between different sectors of society and a substantial disparity at the household and educational center levels in terms of both access to and the use of ICTs. Most teachers either know little about new technologies or do not have suitable training to use these technologies in their classrooms. This situation prevents students from acquiring technological tools and building up digital skills that would enhance their learning process, access to higher education, and employment opportunities. It finally confirms an exceptionally intricate and persistently unequal digital divide in Latin America that affects rural and low-income populations by restricting them from accessing educational and labor market opportunities, underscoring the need for effective public policies to promote equitable access to ICTs and teacher training
Mobile Learning in Physics Education: Evaluating the Impact of Interactive Videos on Practical Work
Get PDFThe emergence of multimedia has fostered the development of interactive videos, introducing an innovative approach to enhancing learner engagement. The rapid proliferation of mobile devices provides new learning opportunities, enhancing both information accessibility and immersion in various educational media, including interactive videos. In the context of practical work, these videos can serve as an effective teaching tool on mobile devices, facilitating the understanding and execution of experimental protocols. The aim of the present study was to evaluate the effect of using interactive videos on students’ performance in physics practical work. It was carried out with 370 first-year students enrolled in the Bachelor of Education, Primary Specialization (BEPS) program at a Moroccan university. Participants were randomly divided into two equal groups: an experimental group using interactive videos on mobile devices and a control group using conventional methods. The results revealed that the experimental group completed the experimental set-ups in significantly less time and demonstrated better understanding and application of the protocols. The present findings underscore the efficacy of interactive videos in facilitating practical physics work, thereby emphasizing their potential to enhance student learning in this domain
A Hybrid Approach for Moving Object Detection and Tracking in Event-Based Cameras
Get PDFEvent cameras, also called dynamic vision sensor-based cameras, capture visual information differently than frame-based cameras. These asynchronous event streams record brightness changes with great temporal resolution and low latency, making them perfect for difficult applications. Data preparation, noise removal, and object tracking are issues when using event cameras in computer vision. This work offers a hybrid clustering-tracking method to accurately locate and track moving objects in event camera data. This study introduces a hybrid technique for accurate moving object detection and tracking in event camera data. Our method uses the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) density-based algorithm to eliminate noise and cluster and track in two steps. The clustering process uses standard methods led by centroids from prior frames for accuracy. The tracking method predicts cluster positions in later frames using speed and direction information when clusters overlap. Our hybrid technique achieves 95.35% accuracy against ground truth labels, promising major improvements in event-based camera data-based computer vision and robotics applications
Learning Needs Including Preferences and Digital Technologies: A Study of Mature Students in Higher Education in England
Get PDFInterviews with eight students individually returning to higher education later in life evidence a variety of personal drivers, including financial betterment, career advancement, personal amelioration, subject appeal, formal accreditation and network enlargement. Fashioned by earlier practices, professional goals and societal influences, these factors suggest a need for approaches marked by flexibility and digital integration, including platform-based tools, artificial intelligence (AI)-supported systems, cloud functionality and IoT-enabled resources. Prominence falls on wider societal contributions of governance innovation, ecological responsibility, healthcare improvement and commercial educational reform. With mature learners presenting marked expectations and contexts, attention re-focuses on technology-supported environments, climate-conscious content and practice-led teaching. The authors recommend further exploration in areas related to engagement, curriculum relevance and achievement enhancement
Sustainable Futures: Exploring the Power of Mobile Technologies in Eco-Friendly Product Promotion
Get PDFMobile technologies have expressively altered the way eco-friendly products are promoted, offering a dynamic and engaging platform for both businesses and consumers. These technologies propose green businesses and artisans with innovative ways to showcase environmentally aware products, such as organic food, sustainable fashion, and biodegradable goods, to a global audience that highlights sustainability. Through mobile apps, e-commerce platforms, and social media, organisations can involve directly with consumers, bypassing the traditional intermediaries, reducing costs, and fostering deeper connections. The structures, such as real-time product updates, communicating storytelling, user-generated content, and influencer partnerships, enhance consumer trust and build brand awareness. Moreover, the integration of mobile data analytics and personalised notifications allows businesses to effectively target niche markets, ensuring that eco-friendly goods reach the true consumers. On behalf of small-scale producers and Indigenous artisans, mobile technologies link the gap between rural craftsmanship and the demand in urban areas, opening doors to global markets. Though, challenges like digital literacy, infrastructure limitations, and increasing market competition must be addressed to ensure inclusive and equitable access for all stakeholders. This paper explores the transformative influence of interactive mobile technologies in promoting sustainable products, empowering green entrepreneurs, and advancing the global shift towards responsible consumption and eco-friendly lifestyles
Data-Driven Evaluation of MOOC-Based Blended College English Teaching via Enhanced Neural Networks
Get PDFThe rapid proliferation of massive open online courses (MOOCs) presents both opportunities and challenges for traditional higher education. As MOOCs offer scalable, high-quality educational resources, they have the potential to significantly enhance instructional outcomes in university settings. In this context, the online-offline hybrid teaching model has emerged as a promising pedagogical approach, particularly in the domain of college English instruction. However, the effective integration of MOOCs into blended learning frameworks remains a complex and evolving challenge. This study presents a data-driven analysis of MOOC-based hybrid teaching for college English. It first identifies key limitations in current implementations, including issues related to interactivity, learner engagement, and instructional design. To address these challenges, a strategic framework is proposed to optimize the blended teaching process. Furthermore, this work introduces an enhanced back propagation (BP) neural network model to evaluate the effectiveness of hybrid English instruction. The improved model incorporates an additional momentum term (AMT), adaptive learning rate (ALR), and a conjugate gradient (CG) optimization algorithm to overcome the limitations of traditional BP networks. Experimental results demonstrate that the proposed model achieves superior performance in terms of accuracy and F1 score compared to conventional methods such as support vector machines (SVM) and deep belief networks (DBN). These findings validate the effectiveness of the proposed framework and highlight the potential of intelligent evaluation models in advancing MOOC-based blended learning environments
XGBoost-Based Demand Forecasting in Supply Chain Management Using Machine Learning Algorithm
Get PDFEffective supply chain management (SCM) plays a significant role in enterprises seeking to mitigate risks, optimize overall productivity, and decrease costs while maintaining product quality and customer satisfaction. Addressing quality defects within the supply chain proactively assists in minimalizing returns, recalls, and rework, which results in increased profitability and considerable financial benefit. For the accurate delivery of products and collecting feedback for analysis, enterprises rely on SCM to satisfy customer needs. Machine learning (ML) approaches have witnessed a revolutionary milestone in SCM, facilitating more effective management of the supply chain. However, operational costs, model interpretability, and data quality remain to be a major challenges. Therefore, this paper presents XGBoost-based demand forecasting in SCM using an ML algorithm. At the initial stage, Z-score normalization servers as a data preprocessing to normalize the input data features into uniform scales. For the feature selection process, the genetic algorithm (GA) is exploited to identify the most descriptive variables. Then, the XGBoost model is employed for the demand forecasting process to accurately forecast future market trends. Finally, particle swarm optimization (PSO) involves hyperparameter tuning which allows the model to achieve optimal forecasting results. Experimental outcomes highlight that the proposed technique accomplishes superior forecasting performance compared to other existing approaches
The Driving Effect of Interactive Mobile Technology on Digital Economy Based on PLS-SEM
Get PDFThis paper studies the driving effect of mobile technologies for human-computer interaction on the digital economy, taking interactive mobile technologies as exogenous variables and the user scale, innovation capability, and industrial development of the digital economy as endogenous variables. Using panel data of interactive mobile technologies and the digital economy from 120 monthly periods in China between 2014 and 2023 as the data source, an empirical analysis is conducted by constructing a partial least squares structural equation modeling (PLS-SEM). The research findings show that interactive mobile technologies have a significant positive impact on the user scale and innovation capability of the digital economy, while interactive mobile technologies have no significant direct driving effect on industrial development, which needs to be indirectly influenced through innovation capability and user scale. Therefore, the research conclusion of this paper holds that to promote the industrial development of the digital economy, it is necessary to expand the user scale through interactive mobile technologies and enhance the innovation capability through patent transformation, scientific and technological investment, etc
Real-Time Legal Advisory System for Mobile Computing Devices via Deep Learning
Get PDFWith the widespread adoption of mobile internet technologies and a growing public awareness of legal rights, demand for real-time and accurate legal advice in mobile environments has increased significantly. Traditional legal services are constrained by uneven resource distribution and delayed response times. Existing legal advisory systems, which often rely on static knowledge bases or simple rule-matching techniques, have demonstrated notable limitations in understanding personalized user needs, processing multimodal inputs, and adapting to mobile devices. Collaborative filtering methods require large-scale annotated datasets and are generally inadequate in capturing the domain-specific semantics of legal texts. Recurrent neural networks (RNNs) and other models impose computational burdens that hinder real-time responsiveness on mobile platforms. Moreover, they lack mechanisms for high-order feature integration and key information extraction in user-legal knowledge interactions. To address these challenges, a real-time legal advisory model based on an attention-compressed interactive network was proposed. By extracting interaction features between user consultation texts and legal knowledge units and integrating an attention mechanism to filter critical semantic information, a lightweight compressed interaction network was designed to enable high-order feature fusion while remaining suitable for devices with limited computational capacity. A score prediction module was incorporated to quantify the relevance of advisory responses, forming an end-to-end recommendation system. This approach overcomes dual bottlenecks in semantic modeling and device adaptability that hinder traditional models, providing a technical solution for generating efficient legal advice in mobile settings. The findings offer practical implications for deploying intelligent legal services on edge devices and contribute to the broader development of accessible legal infrastructure