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Training employees in new competencies is crucial for ensuring organizational sustainability and success. Consequently, organizations allocate significant resources to training initiatives. As training constitutes a substantial investment, it becomes imperative to identify and categorize factors that enhance the transferability of training. This necessity becomes particularly pronounced in the context of online training, which is being increasingly adopted by organizations for various reasons, including cost efficiency and flexibility. While the adoption of online training is on the rise, research on factors that enhance the effectiveness of such training remains limited. This study aims to address this gap by introducing a taxonomy of factors that influence the transfer of online training. These factors are categorized into four broad themes: computer (digital) literacy and attitudes, learner characteristics, training design and planning, and the learning environment along with organizational factors. Additionally, the study maps these factors to the intended outcomes of training (knowledge, skills, and attitude) and the participants engaged in the training sessions, providing a comprehensive overview of ongoing research and identifying areas for future investigation. Drawing insights from an evaluation of existing literature, the research not only points toward potential research directions but also offers practical implications for designing more effective online training sessions. [ABSTRACT FROM AUTHOR]

Students need to have a good understanding of occupational health and safety to avoid accidents during practical activities and in the workplace. To help with this, digital technology can be used to create learning materials and media that aid in achieving learning objectives. This study aimed to develop a mobile application that could be used as a learning resource during practical activities in workshops. The research was carried out at the Department of Construction and Housing Engineering in a Vocational High School in Surakarta, using the Alessi & Trollip research method. The development process consisted of three stages: Planning, Designing, and Development. The study employed various techniques, such as questionnaires, interviews, observation, and documentation, to collect research data. The study used interactive models to analyze qualitative data and descriptive statistics to analyze quantitative data. As a result, the learning resource created has a material suitability level of 90% and media suitability of 100%. User testing has shown positive results, with individual test scores averaging 90% and class test scores averaging 90.35%, which is rated as "very good." This study aims to provide an appropriate learning resource that addresses the identified problems. [ABSTRACT FROM AUTHOR]

The post-COVID-19 period presents an opportunity to build on the teaching and learning strategies that higher education institutions adopted during the pandemic. While there is substantial literature on general higher education needs, there is a noticeable gap concerning teaching and learning needs in special needs and inclusive education within an Open and Distance e-Learning (ODeL) environment. This paper addresses the research question: "How are teaching and learning innovations in inclusive education offered in a higher education ODeL environment?" Rooted in constructivist philosophy, the study used a qualitative research approach to capture the real-life experiences of ten tutors from the Zimbabwe Open University, Faculty of Education, selected through criterion sampling based on their expertise, experience, and involvement in supporting students with diverse needs in ODeL. Open-ended interviews allowed participants to fully express their insights, with data coded using open coding and presented through descriptive narratives. The study identified several innovative teaching and learning strategies, including Google Classroom, Microsoft Teams, WhatsApp Audio and Video, Mobile Learning (M-Learning), Video Learning Environment, and Moodle-based learning platforms (LMS) such as MyVista, which have significantly enhanced the accessibility of educational materials. While these innovations represent progress in inclusivity, challenges such as equitable access to resources, especially for students in rural and under-resourced areas, remain. The study recommends that universities facilitate access to affordable e-learning tools and enhance digital platform accessibility by incorporating features like text-to-audio, video captions, and interactive content. [ABSTRACT FROM AUTHOR]

The rapid advancement of technology and the push toward digitalizing the educational system have led higher education institutions to explore mobile solutions as alternatives to traditional study methods. These mobile applications aim to enhance the university experience by offering instant access to educational resources and simplifying the management of academic processes. This paper seeks to explore the range of existing mobile learning applications, focusing on their functionalities and user experience. Additionally, the study examines how these educational apps impact students' academic lives, specifically looking at factors such as academic performance, productivity, engagement, and social interaction. The research adopts a methodological approach centered on collecting statistical data regarding the adoption of mobile solutions in the academic sector. Results indicate a growing use of mobile applications at the university level, primarily due to their portability and the quick access they provide to educational materials. The study also reveals a positive impact on both students and faculty, enhancing communication and interaction from both an educational and social perspective. [ABSTRACT FROM AUTHOR]

Deep learning has been widely explored in 5G applications, including computer vision, the Internet of Things (IoT), and intermedia classification. However, applying the deep learning approach in limited-resource mobile devices is one of the most challenging issues. At the same time, users' experience in terms of Quality of Service (QoS) (e.g., service latency, outcome accuracy, and achievable data rate) performs poorly while interacting with machine learning applications. Mobile edge computing (MEC) has been introduced as a cooperative approach to bring computation resources in proximity to end-user devices to overcome these limitations. This article aims to design a novel image reiterative extraction algorithm based on convolution neural network (CNN) learning and computational task offloading to support machine learning-based mobile applications in resource-limited and uncertain environments. Accordingly, we leverage the framework of image retrieval extraction and introduce three approaches. First, privacy preservation is strict and aims to protect personal data. Second, network traffic reduction. Third, minimizing feature matching time. Our simulation results associated with real-time experiments on a small-scale MEC server have shown the effectiveness of the proposed deep learning-based approach over existing schemes. The source code is available here: https://github.com/jamalalasadi/CNN_Image_retrieval. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Arabic Language Sciences & Literature / Maǧallaẗ ʻUlūm Al-Luġaẗ Al-ʻArabiyyaẗ Wa-Ādābi-hā is the property of Arab Journal of Sciences & Research Publishing (AJSRP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Road conditions, often degraded by insufficient maintenance or adverse weather, significantly contribute to accidents, exacerbated by the limited human reaction time to sudden hazards like potholes. Early detection of distant potholes is crucial for timely corrective actions, such as reducing speed or avoiding obstacles, to mitigate vehicle damage and accidents. This paper introduces a novel approach that utilizes perspective transformation to enhance pothole detection at different distances, focusing particularly on distant potholes. Perspective transformation improves the visibility and clarity of potholes by virtually bringing them closer and enlarging their features, which is particularly beneficial given the fixed-size input requirement of object detection networks, typically significantly smaller than the raw image resolutions captured by cameras. Our method automatically identifies the region of interest (ROI)—the road area—and calculates the corner points to generate a perspective transformation matrix. This matrix is applied to all images and corresponding bounding box labels, enhancing the representation of potholes in the dataset. This approach significantly boosts detection performance when used with YOLOv5-small, achieving a 43% improvement in the average precision (AP) metric at intersection-over-union thresholds of 0.5 to 0.95 for single class evaluation, and notable improvements of 34%, 63%, and 194% for near, medium, and far potholes, respectively, after categorizing them based on their distance. To the best of our knowledge, this work is the first to employ perspective transformation specifically for enhancing the detection of distant potholes. [ABSTRACT FROM AUTHOR]

This qualitative study intentionally gathered insights from 12 educators of varying seniority across three schools in the Nkangala Education District, Mpumalanga Province, South Africa, regarding their experiences with mobile learning (facilitated through Microsoft Teams and WhatsApp Messenger) as a professional development and knowledge retention tool during the Covid-19 pandemic. The findings, derived from semi-structured interviews and document analysis, indicated that young educators positively embraced the implementation of Microsoft Teams and WhatsApp Messenger. Senior educators deemed WhatsApp Messenger less intimidating than Microsoft Teams, thereby endorsing it as the preferred medium for professional growth and knowledge retention. This led to a lack of incentive among senior educators to participate in the professional learning events facilitated by Microsoft Teams. Thus, they depended on the supplemental learning information disseminated via WhatsApp Messenger as the principal method for professional development and knowledge retention activities. The research identified multiple obstacles to utilising mobile learning for professional development and knowledge retention, specifically: insufficient ICT skills among senior educators, ineffective content facilitation by subject advisors, and inadequate communication and scheduling of professional development programs. The study advocated for a systematic coordination of: 1) a series of ICT skills training for underqualified educators; 2) peer teaching among novice, mid-career, and senior educators to address internal skills deficiencies; 3) effective communication among the district, circuits, and schools; and 4) skills audits for subject advisors to enhance competency in areas of deficiency. By presenting these recommendations, the report alerts schools, circuits, and districts to the likelihood that the Covid-19-induced adoption of hybrid professional development will become the standard practice. [ABSTRACT FROM AUTHOR]

Access to quality education remains a significant global challenge today. In line with the goals outlined by the World Health Organization (WHO) in its 2030 agenda, ensuring access to education is a fundamental objective. Consequently, it is imperative to undertake an investigation into the influence of technological innovation on educational practices. This study examines the impact of incorporating the 5G network into educational settings to improve learning experiences. The analysis covered 134 articles, 62 of which were deemed relevant, classifying the research as ongoing projects or pilot studies for future exploration. The main digital tools identified were artificial intelligence, the Internet of Things, virtual reality, and machine learning. The use of the 5G network appears to have a more significant impact on higher education and universities. Research in this field is mainly concentrated in Europe, America, and Asia. In addition, it is clear that the adoption of 5G technology is influencing pedagogical methods, emphasising immersive learning, e-learning platforms, and flipped classrooms. This study argues for further research into the integration of technology in education, advocating a careful examination of the implementation of 5G infrastructure and its potential to improve access to high-quality education. [ABSTRACT FROM AUTHOR]

Background: In India, the leading cause of mortality for those aged 15-39 in 2016 was reported to be suicide. Despite these alarming statistics, efforts to prevent suicide are nevertheless thwarted by a number of obstacles to getting help. Timely identification is essential to ensuring that people at risk of suicide receive the right care and assistance. The World Health Organization's mental health action plan advocates employing self-help treatments through electronic and mobile technologies in addition to other steps to promote accessibility to and availability of evidence-based mental health services with encouraging results. With encouraging results, technology has been utilized to give self-help and guided psychological therapies, and it may be employed as a low-intensity and low-cost tactic to reach a bigger population in need of care. Aims and Objectives: The objectives of this study were to study the comparison of mobile application versus conventional teaching techniques for implementation of suicide risk assessment among medical students. Materials and Methods: Study comprises MBBS students. Sample size was calculated to be 138, with 67 in Group A (conventional teaching) and 67 in Group B (mobile teaching). Week 1 and 2 - students were randomly allocated into Group A and B. Both the groups were taught about suicide risk assessment. Results: We found that 87.61% found the mobile application easier to comprehend than the conventional teaching methods. Conclusion: Mobile application learning for implementation of suicide risk assessment in MBBS students is non-inferior to conventional teaching methods. [ABSTRACT FROM AUTHOR]

Thanks to the advancement of mobile technologies, Augmented Reality (AR) has become broadly accessible through mobile devices such as smartphones and tablets. Mobile Augmented Reality can benefit science education in a variety of ways. However, except from some sporadic experimental cases, it is rather rarely employed by teachers and has not yet been fully introduced in education. Moreover, little research exists about the adoption behavior of mobile AR by pre-service teachers. Against this background, the current study proposes and validates an integrated adoption model to explain and predict the factors that significantly influence student teachers' intentions to use mobile AR in teaching primary science. The study also introduces two new constructs, Perceived Immersion and Perceived Educational Value in the context of mobile AR. Eighty-nine undergraduate pre-service primary school teachers participated in a mobile augmented reality workshop creating mobile augmented reality experiences for teaching physics to primary school pupils. Following that, student teachers answered an online survey. The quantitative survey data was analysed using structural equation modelling. The study confirmed the proposed model explaining and predicting approximately 72% of the variance of student teachers' Behavioral Intention to Use mobile AR to teach primary science. Perceived Immersion and Perceived Educational Value significantly influence Behavioral Intention to Use after being mediated by Perceived Usefulness. The study offers insight into the factors influencing pre-service primary teachers' intentions to utilise mobile augmented reality (AR) in their future lessons, which is relevant given the growing interest in utilising these technologies in education. Implications are discussed. [ABSTRACT FROM AUTHOR]

In this research, a GAmified Mobile Leaning Framework (GAMOLEAF) developed as a new intelligent application designed for mobile devices to ensure learning, assessing, and advancing learners' knowledge in programming complex data structures in Java programming language. GAMOLEAF adopted motivational strategies to solve motivational problems during the COVID-19 pandemic by employing a gamification module, that integrates levels, scores, badges, leaderboard, and feedback. Furthermore, in order to assist learners to find useful and relevant lessons and best solutions for each data structure, GAMOLEAF incorporated personalized recommendations through two intelligent modules: a Lessons Recommendation Module (LRecM) and a problem-solving Solutions Recommender Module (PSSORecM). LrecM aims to provide learners with personalized lessons depending on the ratings collected explicitly from them. Whereas, PSSORecM bases on learners' behaviors and directs them to consult other solutions. Both modules were based on the collaborative filtering method and used Matrix Factorization (MF) applying Singular Value Decomposition (SVD) and Negative Matrix Factorization (NMF) algorithms, respectively. To explore how the integration of personalized recommendations and gamification impact on students motivation and learning achievements in higher education to learning programming complex data structures course using mobile technologies, especially in difficult times like COVID-19, an experiment was carried out to compare the learning achievement and motivation of 90 students divided into three groups (control group, first experimental group, and second experimental group) using three versions of GAMOLEAF respectively: GAMOLEAF-V1 without gamification and without recommendation, GAMOLEAF-V2 integrating gamification only and GAMOLEAF-V3 integrating both gamification and recommendation. The One-way ANOVA (analysis of variance) test and Post hoc Tukey test were employed to analyze the performances of the three groups before and after the learning activity. The results suggested that the students who learned with GAMOLEAF-V3 using gamification and recommendation achieved significantly better learning achievement than those who learned with GAMOLEAF-V2 and GAMOLEAF-V1. From the experimental results, it was found that the gamification applied in GAMOLEAF-V2 and GAMOLEAF-V3 had significantly better effectiveness in improving only students' motivation without improving their learning achievement. Moreover, the analysis result of the learning achievement indicated that the students in the second experimental group showed significantly higher learning achievement using GAMOLEAF-V3 in comparison with those in both the control group and the first experimental group who used GAMOLEAF-V1 and GAMOLEAF-V2 respectively. Such findings indicate that the personalized recommendations offered by the Lessons Recommendation Module (LRecM) and the problem-solving Solutions Recommender Module (PSSORecM) in GAMOLEAF-V3 may be one of the reasons why the learning achievement of students was increased. [ABSTRACT FROM AUTHOR]

Self-regulatory learning skills are recognized as an essential factor motivating students to learn. The lack of self-regulatory learning skills could reduce students' learning performance. Therefore, engaging students in making learning plans, executing the plans, and reflecting on their learning remains a challenge for teachers. To address this problem, this study proposes a gamified mobile self-regulated learning approach which provides multiple gamified rewards to motivate students' self-regulatory learning skills. Gamified elements were integrated into students' self-regulated learning stages to guide them in efficiently exploring how to achieve their goals and making deep learning reflections. To investigate the effect of this proposed approach on students' learning outcomes, two classes of students were recruited to conduct the experiment. The learning approach was used by the experimental group (N = 22) while the conventional mobile self-regulated learning approach was used by the control group (N = 22). Both groups of students' performance on the mathematics learning achievement test, SRL learning sheets, and goal-achieving intentions were collected and analyzed. The results showed that the gamification mechanism improved the students' learning achievement as well as their goal-setting and reflection performance. However, the students who used the proposed approach reached similar monitoring performance as those students who learned without gamification. This study confirmed the feasibility of introducing a gamification mechanism into self-regulated learning, and revealed how to apply multiple game elements in instructional activities to enhance students' learning intentions. [ABSTRACT FROM AUTHOR]

Mobile learning has ushered in a transformative era in education, compelling educational institutions to reimagine their pedagogical approaches. This shift is particularly evident in teacher training programs, where mobile learning is increasingly integrated into traditional education. The success of these integrated systems hinges on educators' willingness to adapt to these evolving paradigms. Nevertheless, a notable gap exists in the research landscape concerning the challenges of adopting mobile learning in teacher training and its consequential impact on teachers' professional capabilities. This study aims to bridge this gap by identifying the factors influencing teachers' satisfaction and performance within mobile learning training courses and establishing the intricate relationships between these variables. This study was conducted in a quantitative research framework and collected data from 563 schoolteachers through an online survey. These respondents were actively engaged in mobile-based training courses at the Provincial Institute for Teacher Education (PITE) Sindh during their mandatory training programs in the academic year 2022-23. Structural equation modeling was employed to analyze the proposed hypotheses rigorously. The study's findings unveil a robust and significant nexus between several critical factors and educators' experiences when utilizing mobile learning for training. Specifically, content quality, information quality, system quality, prior experiences, and mobile self-efficacy contributed strongly to task-technology fit, ultimately enhancing teachers' engagement, and yielding improved outcomes. Moreover, the study elucidates a clear correlation between factors encompassing understanding, Instructors' prompt feedback, teachers' expectations, and instructor quality within the context of training course design. These factors collectively positively influence teachers' satisfaction and performance, enhancing content knowledge, pedagogical skills, and professional dispositions. This holistic approach to mobile learning positively influences teachers' satisfaction and, ultimately, enhances teachers' overall performance. This study provides valuable insights to guide educators, institutions, and policymakers in effectively embracing and implementing mobile learning to benefit teachers and, ultimately, the broader field of education. [ABSTRACT FROM AUTHOR]

Age, gender, body mass index (BMI), and mean heart rate during sleep were found to be risk factors for obstructive sleep apnea (OSA), and a variety of methods have been applied to predict the occurrence of OSA. This study aimed to develop and evaluate OSA prediction models using simple and accessible parameters, combined with multiple machine learning algorithms, and integrate them into a cloud-based mobile sleep medicine management platform for clinical use. The study data were obtained from the clinical records of 610 patients who underwent polysomnography (PSG) at the Sleep Medicine Center of the Second Affiliated Hospital of Fujian Medical University between January 2021 and December 2022. The participants were randomly divided into a training-test group (80%) and an independent validation group (20%). The logistic regression, artificial neural network, naïve Bayes, support vector machine, random forest, and decision tree algorithms were used with age, gender, BMI, and mean heart rate during sleep as predictors to build a risk prediction model for moderate-to-severe OSA. To evaluate the performance of the models, we calculated the area under the receiver operating curve (AUROC), accuracy, recall, specificity, precision, and F1-score for the independent validation set. In addition, the calibration curve, decision curve, and clinical impact curve were generated to determine clinical usefulness. Age, gender, BMI, and mean heart rate during sleep were significantly associated with OSA. The artificial neural network model had the best efficacy compared with the other prediction algorithms. The AUROC, accuracy, recall, specificity, precision, F1-score, and Brier score were 80.4% (95% CI 76.7–84.1%), 69.9% (95% CI 69.8–69.9%), 86.5% (95% CI 81.6–91.3%), 61.5% (95% CI 56.6–66.4%), 53.2% (95% CI 47.7–58.7%), 65.9% (95% CI 60.2–71.5%), and 0.165, respectively, for the artificial neural network model. The AUROCs for the LR, NB, SVM, RF, and DT models were 80.2%, 79.7%, 79.2%, 78.4%, and 70.4%, respectively. The six models based on four simple and easily accessible parameters effectively predicted moderate-to-severe OSA in patients with PSG screening, with the artificial neural network model having the best performance. These models can provide a reliable tool for early OSA diagnosis, and their integration into a cloud-based mobile sleep medicine management platform could improve clinical decision making. [ABSTRACT FROM AUTHOR]

With the increasing reliance on digital platforms, mobile game-based phone applications provide avenues to enhance learning of complex chemical concepts. This research developed a mobile game-based phone application designed to simulate the qualitative inorganic analysis of cations, determine its effects on learning, and examine students’ insights on using the mobile application. The developed app showed a Content Validity Index of 1, suggesting acceptable validity and relevance. Data analysis revealed an increase in the pre-test to post-test scores in terms of students’ metacognitive self-regulation skills, self-efficacy, and conceptual understanding after the intervention. This study offers essential implications for the potential of mobile game-based applications as valuable educational resources in chemistry. [ABSTRACT FROM AUTHOR]

The purpose of this study was to develop and evaluate the effectiveness of a self-regulated vocabulary learning mobile application (SRVL-MAPP) to improve English as a foreign language (EFL) students’ English vocabulary learning achievement and motivation. To evaluate the effectiveness of SRVL-MAPP, an experiment was conducted with 60 fourth-grade students from a primary school in Henan Province, China. These students were divided into two classes and randomly assigned to either the experimental group, which used SRVL-MAPP, or the control group, which used a mobile vocabulary learning application (NSRVL-MAPP) without the SRL mechanism, with 30 students in each group. The experiment lasted for three weeks. The results revealed that students in the experimental group outperformed those in the control group in both vocabularies learning achievement and motivation. In addition, the results also indicated that SRVL-MAPP significantly improved the English vocabulary learning achievement and motivation of field-dependent learners, while no such effect was observed for field-independent learners. [ABSTRACT FROM AUTHOR]

Background: Given the increasing complexity of illnesses and rapid pace of technological advancements in professional training, it is vital to offer nurses ample opportunities to hone their clinical expertise and skills, particularly in ensuring the delivery of premier medical care. This study aimed to determine the factors and predictors influencing nurses' satisfaction with adopting mobile learning approaches in intensive care unit healthcare settings. Additionally, it sought to investigate the applicability of the technology acceptance model in explaining their inclinations and validating the measurement scales employed in the research. Methods: The study employed a cross-sectional survey research design, utilizing a technology acceptance questionnaire and a learning satisfaction questionnaire. The survey was conducted in six intensive care unit departments. A total of 212 participants completed the survey as the primary instrument. Rigorous assessments were conducted to establish the content validity and ensure instrument reliability. Results: The findings demonstrated that perceived usefulness was the most influential factor affecting nurses' intentions to embrace mobile learning approaches, with perceived ease of use emerging as the principal determinant of perceived usefulness. Conclusions: Incorporating mobile learning methodologies is paramount to increasing the calibration of professional nursing education programs. By effectively integrating digital information technology and tools, nursing educators can overcome teaching challenges, deliver innovative clinical nursing education content through mobile learning approaches, and foster optimal development in the field. [ABSTRACT FROM AUTHOR]

M-Learning is a learning process that uses technology or mobile devices such as smartphones, tablets or wearable devices to support the learning process. This is still being done because there are many different theoretical models proposed. However, there is no model that can be generally accepted as an established theoretical model in the application of M-learning in vocational and high school education environments in Sidoarjo. This research is expected to make a significant contribution to the development of a better theoretical understanding of the determining factors that influence M-learning adoption using the Unified Theory of Acceptance and Use of The Technology (UTAUT). To collect data, researchers distributed questionnaires to respondents using Google Form. The data used were 444 M-learning users. Theoretical model research was carried out using Structural Equation Modeling (SEM) analysis, then SPSS and Amos as analysis support. There are seven factors that determine the results of acceptance of M-Learning adoption in this research, namely Facilitating Condition, Performance Expectancy, Effort Expectancy, Perceived Convenience, Social Influence, School Management Support. The six factors that show a positive and significant relationship are Facilitating Condition, Performance Expectancy, Effort Expectancy, Perceived Convenience, Social Influence, School Management Support. Perceived Convenience has the first strongest positive and significant value, and Performance Expectancy has the second strongest value. Each factor has a moderate influence on Intention to Use. This factor is the most influential in implementing M-Learning in vocational and high schools in the Sidoarjo area. [ABSTRACT FROM AUTHOR]

Over the last decade, technology has become a critical component of everyday life, in and out of the classroom. As such, digital learning serves as a vehicle for accessing the general education curriculum, and is imperative to higher education, workforce development, and economic security. The purpose of this systematic literature review is to investigate the current status of research on instructional and assistive technologies used to support students with high incidence disabilities in accessing the general education curriculum over the last decade (2010–2020). Among the results of the analysis of 67 studies, a majority involved White students with specific learning disabilities, with studies primarily focused on social/emotional/behavioral, reading, or mathematics outcomes. Independent variables spanned seven categories, largely involving computer-assisted instruction such as modules or virtual manipulatives, although applications via mobile devices were increasingly researched toward the middle to end of the decade. Additional results and implications for research and practice are shared. [ABSTRACT FROM AUTHOR]

Data supplied by mobile phones have become the basis for identifying meaningful places frequently visited by individuals. In this study, we introduce SAMPLID, a new Supervised Approach for Meaningful Place Identification, based on providing a knowledge base focused on the specific problem we aim to solve (e.g., home/work identification). This approach allows to tackle place identification from a supervised perspective, offering an alternative to unsupervised clustering techniques. These clustering techniques rely on data characteristics that may not always be directly related to classification objectives. Our results, using mobility data provided by call detail records (CDRs) from Milan, demonstrate superior performance compared to applying clustering techniques. For all types of CDRs, the best results are obtained with the 20 × 20 subgrid, indicating that the model performs better when supplied with information from neighboring cells with a close spatial relationship, establishing neighborhood relationships that allow the model to clearly learn to identify transitions between cells of different types. Considering that it is common for a place or cell to be labeled in multiple categories at once, this supervised approach opens the door to addressing the identification of meaningful places from a multi-label perspective, which is difficult to achieve using classical unsupervised methods. [ABSTRACT FROM AUTHOR]

This study developed a realistic, interactive English blockade-running game that integrates virtual environments and mobile devices to conduct a collaborative and competitive contest to promote English learning among technical college students. In addition, it also explored students' learning effectiveness, perception of peer collaboration, and the use of grouping strategies for collaborative learning. This study adopted a quasi-experimental design and separated 120 participants into four groups (Baseline, Smartphone, Collaborative Learning, and Smartphone + Collaborative Learning). The procedure included the pretest, English blockade-running contest, peer collaboration perception questionnaire, focus interview, and the posttest at the end of the semester. Our findings in terms of learning effectiveness, the Collaborative Learning Group and the Smartphone + Collaborative Learning Group exhibited significant improvements in their scores. In terms of peer collaboration, we also found that the Smartphone + Collaborative Learning Group reported increased perceptions of collaboration in Take Action and Group Organization dimensions when compared with the Collaborative Learning Group. Regarding grouping strategies for collaborative learning, the Smartphone + Collaborative Learning Group used smartphones to ensure continuous communication between team members during both individual and collaborative blockades. Team members performed their respective duties, jointly participated in the contest, and worked together to overcome the challenges of the different blockades. The implications and research limitations are also discussed in this paper. [ABSTRACT FROM AUTHOR]

As a corpus-assisted method for language pedagogy, DDL (data-driven learning) may have the potential to enhance language exposure and promote active learner engagement. Concordancing, or KWIC (Key Words in Context), has been the traditional method used in DDL to retrieve numerous language examples, while the method has limitations with overreliance on individual words to search. This paper aims to propose and promote concordancing alternatively based on grammar patterns, a multi-word concept in corpus linguistics. The conceptualised method is named as PIC (Patterns in Context), an extended form of KWIC. An empirical study was conducted to investigate whether the PIC method has any advantages over the traditional KWIC method, using two custom-built Android apps. The research involved 18 pre-university intermediate learners (and six pilot study participants), who used the apps in a self-directed way for two weeks. Then the assessment of the two apps and methods was conducted based on data from automatic logs and responses from questionnaires and interviews. The results suggest that, compared to KWIC, PIC could be slightly advantageous in efficiently helping learners find the target language use, while this approach seems not strong in user engagement and perceived effectiveness. The implications for DDL are discussed, and further investigation is also planned. [ABSTRACT FROM AUTHOR]

With the rapid development of wireless technology and Mobile communication technology worldwide, Mobile Learning represents a new learning trend in the field of higher education, and has gradually become the development trend and research hotspot of independent learning and lifelong learning. Based on modern wireless network mobile communication terminal technology, this paper proposes a Granott model-driven mobile learning system. Firstly, the Granott model is studied and analyzed, and the ubiquitous computing with physical integration and spontaneous interoperation is used to control the Android mobile terminal, which greatly improves the interactivity of M-Learning. Then JSON is selected to optimize the data transmission format in Ajax, and j Query is used to parse JSON data. Ajax + JSON + j Query can shield the complexity of DOM parsing XML files, greatly shorten the data transmission time and improve the efficiency of Granott model. Finally, the Fast kernel density estimation (Fast KDE) algorithm is introduced to compress the data transmitted by Ajax + JSON + j Query, so as to obtain a reasonable number of training samples. Through experiments and relevant data analysis, the proposed mobile learning system based on modern wireless network mobile communication terminal technology, optimized by JSON and Fast KDE algorithm, driven by Granott model, can efficiently complete the learning task, and improve the ubiquity, interactivity, timeliness of mobile learning. [ABSTRACT FROM AUTHOR]

With the rapid development of innovative applications, lots of computation-intensive and delay-sensitive tasks are emerging. Task offloading, which is regarded as a key technology in the emerging mobile edge computing paradigm, aims at offloading the tasks from mobile devices (MDs) to edge servers or the remote cloud to reduce system delay and energy consumption of MDs. However, most existing task offloading studies either didn't consider the dependencies among tasks or simply designed heuristic schemes to solve dependent task offloading problems. Different from these studies, we propose a deep reinforcement learning (DRL) based task offloading scheme to jointly offload tasks with dependencies. Specifically, we model the dependencies among tasks by directed acyclic graphs and formulate the task offloading problem as minimizing the average cost of energy and time (CET) of users. To solve this NP-hard problem, we propose a deep Q-network learning-based framework that creatively utilizes deep neural networks to extract system features. Simulation results show that our proposed scheme outperforms the existing DRL scheme and heuristic scheme in reducing the average CET of all users and can obtain near-optimal solutions. [ABSTRACT FROM AUTHOR]

Background: This paper examines the use of augmented reality (AR) as a concept‐association tool in schools, with the aim of enhancing primary school students' learning outcomes and engagement. Conflicting findings exist in previous studies regarding the cognitive load of AR‐enriched learning, with some reporting reduced load and others indicating increased demand and poorer performance. Understanding these implications is essential for effectively leveraging AR in education. The study offers a fresh perspective on the potential of AR technology in improving educational experiences. Objectives: The primary goal of this research was to evaluate the effectiveness of an AR‐assisted concept‐association strategy for improving essential knowledge acquisition and skills outcomes, increasing cognitive load, and increasing self‐efficacy and learning motivation among primary school students. Methods: A quasi‐experimental design with a control group was employed to investigate the effectiveness of the intervention. Results and Conclusions: The results demonstrate that the implementation of the AR‐assisted concept‐association strategy effectively enhanced essential knowledge acquisition and skills outcomes, increased cognitive load, and increased self‐efficacy and learning motivation among primary school students. These findings highlight the potential of AR technology to improve the learning experience and engagement of primary school students. The study contributes to the existing literature on the effectiveness of AR technology in education, suggesting future research directions. Ultimately, it offers a practical solution for improving the learning experience by presenting a new approach for teaching using AR technology. Lay Description: What is currently known about this topic: Augmented reality (AR) technology has been increasingly explored in educational settings.Previous research has shown that AR can enhance learning outcomes and engagement among students.AR applications have the potential to serve as concept‐association tools to improve the learning experience. What this paper adds: This paper investigates the effectiveness of using an AR application as a mobile concept‐association tool in school settings.The study demonstrates that the AR‐assisted concept‐association strategy effectively enhances knowledge acquisition, skills outcomes, cognitive load, self‐efficacy, and learning motivation among primary school students.The findings highlight the potential of AR technology in improving learning experiences and engagement for primary school students.The study offers practical implications for integrating AR technology into educational settings, and suggests avenues for future research in this area. Implications for practice and/or policy: Incorporating AR applications as concept‐association tools can enhance learning outcomes and engagement in primary school settings.AR technology can be used to increase cognitive load, and to increase self‐efficacy and motivation among students.Educators and policymakers should consider integrating AR technology into teaching practices to improve the overall learning experience.Future research and exploration should be conducted to understand the full potential and limitations of AR technology in education. [ABSTRACT FROM AUTHOR]

Background: Despite the significant emphasis on self‐determination in special education and the crucial role of mobile learning, there is a notable absence of path modelling studies that explore the effect of self‐determination on the usage of mobile learning by students with special needs. Objectives: This study sought to investigate the intentions of mobile learning usage among students with special needs by proposing a model rooted in the technology acceptance model, complemented by extraneous constructs from self‐determination theory (competence, autonomy and relatedness). Methods: The data of the study was obtained online from 1298 special needs university students with eight different types of disabilities. Data were analysed with descriptive statistics, confirmatory factor analysis, structural equation modelling and bootstrapping. Results and Conclusions: The proposed model explained 78.4% of ease of use, 85.2% of usefulness, and 76.5% of intention. 11 of the 12 hypotheses tested within the scope of the model were supported. All hypotheses examining the impacts of self‐determination theory constructs on ease of use, usefulness, and intention were validated (8 hypotheses), with the exception of the autonomy → intention relationship. Substantial empirical evidence has been acquired to support the role of the self‐determination theory in exploring the intention toward mobile learning among university students with special needs. Concurrently, a robust theoretical framework has been introduced to the field of special education to elucidate the acceptance and utilization of technology by university students with special needs. Lay Description: What is already known about this topic: Numerous studies have demonstrated the significance of mobile tools and mobile learning in the realm of special education.Self‐determination holds immense importance in fostering independence and social engagement by empowering individuals with special needs to gain confidence, assume control and accountability for their own choices and actions.Self‐determination theory underscores the importance of providing individuals with support for three fundamental psychological needs—autonomy, competence, and relatedness—to foster healthy development. What this paper adds: To the best of the authors' knowledge, this study stands as a unique contribution within its field due to its distinctive characteristics. The model, supported by exceptionally successful outcomes and its ability to elucidate technology acceptance constructs at remarkably high levels, offers a robust theoretical framework for enabling students with special needs to engage effectively with mobile learning in both special and inclusive educational settings.Investigating how self‐determination theory enhances the explanatory power of the technology acceptance model in special education.Illuminating the significance of basic psychological needs in the adoption of mobile learning, employing advanced statistical methods with a substantial participant base, and delivering valuable insights to advance the utilization of mobile learning in special education. Implications for practice and/or policy: Special education settings should encourage students to take initiative, make choices, and exercise autonomy, and curriculum design should include opportunities for students to make choices.Making students part of the design processes and emphasizing usability through the prioritization of customizable, user‐friendly designs should be an inherent aspect of educational technology.Educators and support staff must have training and professional development opportunities to effectively use mobile learning tools and create inclusive learning environments. [ABSTRACT FROM AUTHOR]

Seamless learning refers to a learning model in which students can accomplish their learning tasks with the help of technology whenever they are curious in different real-life learning contexts. In the era of artificial intelligence, seamless learning technologies are expected to collect big data from mobile and computer devices, enable analytics to have an in-depth understanding of learning behaviors and patterns, and be flexible in employing suitable educational models to facilitate learning and teaching. This paper aims to build a seamless learning platform for language learners and language instructors by coining the extension of seamless learning based on the theories of mobile learning, ubiquitous learning, and task-based language learning. In order to facilitate formal and informal learning for foreign language learners across individual and social learning spaces, we present Taskcave- a seamless learning platform. Taskcave assists language instructors in assigning vocabulary learning tasks to the students and monitoring their learning activities using an analytic dashboard. Taskcave allows the students to log their learning activities digitally, interact with their classmates, engage in collaborative learning, and receive feedbacks from the teacher. [ABSTRACT FROM AUTHOR]

This study conducted a new adaptation of the short version of the Smartphone Addiction Scale (SAS-SV) for the Mexican population. The scale exhibited excellent internal consistency (α = .88), with one factor explaining 54.8% of the variance. 286 students aged 15 to 35 years of age from central and southeast Mexico revealed a prevalence of 30% excessive users (18% men, 12% women). They used smartphones primarily for communication, social media, entertainment, productivity, and browsing the Internet. No gender differences were found; however, a significant association with age emerged (rho = .221, p < .001). Withdrawal (72.7% women) and tolerance (80.8% men) were common symptoms of addiction, while ignorance of consequences was less frequent (33.3% women, 30.8% men). The prevalence of potential excessive use of smartphones was close to the average identified in the reviewed studies. The results revealed a substantial risk of addiction to smartphones, particularly among men. Finally, the successful adaptation of the scale offers a valuable instrument for future study and clinical evaluation. These findings can guide targeted interventions and educational programs tailored to different groups. Additionally, recognition of common symptoms of addiction allows individuals to identify and address potential problems at an early stage. However, more study is needed to validate the findings and explore additional contributing factors in the Mexican context. [ABSTRACT FROM AUTHOR]

The Five-sphere integrated plan research-based teaching mode of academic paper writing course based on PAD class in normal universities is an important way to improve normal students'professional literacy and innovative ability and to build a first-class course with advanced,innovative and challenging nature. "The Unity of teaching and research" and "studentcentered" are the core concepts of research-based teaching in the course of academic paper writing.The teaching content of "One clue,five stages and fourteen special topics" reflects the purpose of the research-based teaching of academic paper writing course that content is king. The teaching mode of "Trinity and five steps" of "SPOC+ PAD class" provides a theoretical model for the research-based teaching of academic paper writing class.The cultivation of learning environment based on the combination of "mobile learning" and "classroom learning" on a personal and social level plays an important role in the implementation of research-based teaching.The assessment system of "online and offline", "individual and group", "computer assessment, teacher assessment and student assessment" effectively ensures the implementation of the research-based teaching of academic paper writing course.This kind of research-based teaching mode finally promotes the realization of curriculum objectives effectively. [ABSTRACT FROM AUTHOR]

The advent of the digital era has catalyzed transformative shifts in educational paradigms, seamlessly integrating sustainable education with innovative pedagogical approaches that cater to a broader spectrum of learners and educators. As the academic landscape evolves into an information-dense environment, traditional models of one-on-one feedback often fall short, resulting in delays and a lack of diverse intellectual engagement. This shift underscores the growing importance of peer feedback and asynchronous post-lesson feedback mechanisms, which have emerged as vital, process-oriented educational activities. Such mechanisms not only address the immediacy and diversity of feedback but also foster a sustainable, collaborative, and engaging learning environment that bolsters student autonomy and interaction. This study conducts a mapping review and synthesis of the literature on peer interaction, assessment, and participatory innovations in online and mobile learning within higher education, covering the period from 1993 to 2022. By employing topic modeling techniques to analyze a corpus of 485 articles, the literature was initially segmented into 19 distinct issues. These were subsequently consolidated into three major thematic models, thereby highlighting significant themes, research focal points, and prospective trends. This analytical process not only enriches the understanding of peer dynamics in digital learning settings but also integrates sustainable educational practices by promoting resource efficiency and inclusivity. The findings serve as a robust reference for future researchers aiming to explore the intersections of technology, peer interaction, and sustainability in educational settings. [ABSTRACT FROM AUTHOR]

In an effort to improve the efficiency and recommendation accuracy of mobile learning resources, the study proposes a hybrid mobile learning strategy based on Collaborative Filtering (CF), context and interest. Analyse from the perspective of situational awareness, construct a personalised recommendation model for text learning resources based on GimbalTM, and obtain a recommendation form. The experimental results show that the RMSE and MAE of Context-Collaborative filtering (C-CF) are lower than those of traditional CF. The Precision and Recall values of C-CF are higher than those of CF at 10 s, the recommendation growth rates of traditional CF and C-CF are 2.09% and 1.67%, respectively. The Gimbal software enables a certain degree of learner location detection and can trigger contextual rules based on time and location contexts to provide users with personalised text-based learning resources. The research results indicate that in specific applications, over time, under the recommendation system, students' grades steadily increase, which is also beneficial for improving their learning efficiency. [ABSTRACT FROM AUTHOR]

This document provides a summary of a collection of research articles focused on the intersection of education and technology. The articles cover a range of topics, including the integration of IoT in higher education, a tool to improve programming instruction, the impact of COVID-19 on education, and the use of virtual reality and augmented reality in online learning. The research presented in these articles aims to enhance educational experiences, address the needs of diverse learners, and provide recommendations for future research and implementation. [Extracted from the article]

This study examines whether the use of AI-Pengtalk, an AI-based Conversational English programme, provided by a broadcasting company (EBS) that specializes in public education can significantly improve conversational English skills and bridge the English language proficiency gap associated with parental socioeconomic status. Over the course of four weeks from April 27 to May 22 in 2020, 108 fourth-grade classes in 54 elementary schools voluntarily participated in this experiment. Two classes in each school were designated as a treatment group and a control group. For the treatment group, a tablet installed with a pilot version of AI-Pengtalk was provided and students were encouraged to make use of the programme. Two sets of surveys and English tests were placed pre and post hoc. After 4 weeks, test scores, log files, and survey responses of participants were analysed. A series of DID analyses demonstrate that the use of AI-Pengtalk improves the treatment group's self-evaluation of their English abilities, confidence in using English, preference on English itself, and amount of time spent on studying English during the pilot experimental period compared to the control group. When other variables were controlled, the use of AI-Pengtalk also helped the treatment group achieve higher test scores. This study implicates that the use of smart English education like AI-Pengtalk may especially be able to better compensate for academic setbacks caused by low parental SES or, in the case of English learning, the reluctance to converse in English with other students. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has been providing an opportunity to demonstrate the importance and capability for e-learning to deliver quality learning for many universities. In higher education, e-learning systems, such as mobile learning (m-learning) applications, have many valuable advantages for students. This study addresses the students' intention to continue using m-learning applications in higher education after the COVID-19 pandemic, focusing on students' intention to continue using m-learning applications. Specifically, a research model is proposed to explore the antecedents of continuance intention and the relationships among variables identified in the literature on e-learning acceptance in higher education. To evaluate the proposed model, data were collected from 282 students of Taif University, Saudi Arabia, who responded to an online questionnaire. The data were then analyzed using structural equation modeling. The proposed model was able to explain 83% of the behavioral intention of m-learning applications. The results reveal that perceived usefulness and satisfaction affect attitude, and attitude subsequently affects continuance intention. In addition, perceived effectiveness and satisfaction indirectly affect continuance intention. Students' gender did not moderate the relationship between their attitudes and will to continue using m-learning applications. The study results provide a valuable guide for application designers, universities, and policymakers to ensure the students' intention to continue using m-learning applications in higher education. [ABSTRACT FROM AUTHOR]

Early studies show that learning with mobile devices, also known as mobile learning, improves students' learning in authentic contextual learning-i.e., learning connected to the real world. However, no empirical evidence has yet to firmly prove the effects of mobile technology on specific student skillsets such as learning scalability which means learning can be applied in various scenarios and learning sustainability which means learning can be sustained in real-world environments. Therefore, this study aims to explore the effect of learning using a mobile app called mobile Smart-Physics on learning cognitive levels, learning scalability (e.g., number of learning locations and number of experimental data), and learning sustainability (e.g., number of completed assignments). Eleventh-grade vocational high school students volunteered for this quasi-experiment and were divided into an experimental group (EG), which used Smart-Physics, and a control group (CG), which used a mobile Ubiquitous-Physics (U-Physics) app. The findings show that the EG significantly outperformed the CG concerning learning cognitive levels, learning scalability and learning sustainability. Smart-Physics features enabled the students to tackle technical and pedagogical difficulties during physical investigations in real-world environments and, in some cases, improved their task accomplishment and sustained their motivation to learn. Location awareness promoted the students' authentic experiential learning, which sharpened their ability to apply learning in real-world environments and upload more experimental data. Feedback helped the students consolidate their physics theories and practical experiences, thereby generating more learning records with meaningful multimedia content like experimental graphs, tables, and notes in various learning locations. Therefore, we encourage practitioners to use smart learning environment features in their learning tools and activity designs. [ABSTRACT FROM AUTHOR]

In the non-deterministic, large-scale navigation environment under the Mars exploration mission, there is a large space for action and many environmental states. Traditional reinforcement learning algorithms that can only obtain rewards at target points and obstacles will encounter the problems of reward sparsity and dimension explosion, making the training speed too slow or even impossible. This work proposes a deep layered learning algorithm based on the goal-driven layered deep Q-network (GDH-DQN), which is more suitable for mobile robots to explore, navigate, and avoid obstacles without a map. The algorithm model is designed in two layers. The lower layer provides behavioral strategies to achieve short-term goals, and the upper layer provides selection strategies for multiple short-term goals. Use known position nodes as short-term goals to guide the mobile robot forward and achieve long-term obstacle avoidance goals. Hierarchical execution not only simplifies tasks but also effectively solves the problems of reward sparsity and dimensionality explosion. In addition, each layer of the algorithm integrates a Hindsight Experience Replay mechanism to improve performance, make full use of the goal-driven function of the node, and effectively avoid the possibility of misleading the agent by complex processes and reward function design blind spots. The agent adjusts the number of model layers according to the number of short-term goals, further improving the efficiency and adaptability of the algorithm. Experimental results show that, compared with the hierarchical DQN method, the navigation success rate of the GDH-DQN algorithm is significantly improved, and it is more suitable for unknown scenarios such as Mars exploration. [ABSTRACT FROM AUTHOR]

This article describes an inexpensive partitioned coupling strategy for computational fluid–structure interaction (FSI) admitting negative‐Jacobian elements. The emphasis is very much on a reduced smoothed integration (RSI) scheme of the cell‐based smoothed finite element method (CSFEM) using four‐node quadrilateral (Q4) elements for a cost‐effective solution to the Navier–Stokes (NS) equations. In the discrete fluid field, each Q4 element is considered as one single smoothing cell so as to diminish the smoothed integration loops substantially. However, the RSI scheme does not respect the stability condition of smoothed Galerkin weak‐form integral in the CSFEM. To tackle this issue, a simple hourglass control is introduced to the under‐integrated formulation of the NS solver. Importantly, the stabilized RSI scheme has an inbuilt advantage of its enormous tolerance towards negative‐Jacobian elements. The developed technique is easy‐to‐implement and has been tested in various FSI examples adopting both fine and distorted meshes. [ABSTRACT FROM AUTHOR]

Utilizing smartphone-based mobile learning in the learning process will provide the latest innovative alternatives to the learning process. Today's students tend to experience distractions in their learning process. Not infrequently the learning system that applies in a school makes students bored and not enthusiastic about learning. This could have been because the school did not make any changes in the existing education system. Where the effect of the incident will cause students to feel bored and bored in the learning process. In this era of the mobile revolution, it will create various advances in the field of technology that can be used to support the learning process. The purpose of this research is to find out the potential utilization of smartphones as a form of mobile learning revolution in the learning process. Through research on the Mobile Learning Revolution: Utilizing the Potential of Smartphones in the Learning Process, researchers hope that by utilizing the potential of smartphones as a manifestation of the mobile learning revolution in the learning process, it can facilitate the learning process itself. The method used in this research is a quantitative method. The researcher conducted a survey using the Google form which consisted of 15 statements related to the research title. Researchers found that the use of the potential of smartphones in the era of the mobile learning revolution had a major impact on the learning process. The existence of a smartphone in the learning process greatly facilitates the implementation of the teaching and learning process in schools. The limitations of this study are that researchers only conduct research in schools and researchers do not conduct research directly at schools but share survey links on Google forms containing statements about the Mobile Learning Revolution: Utilizing the Potential of Smartphones in the Learning Process. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to explore customer perceived value (CPV) dimensions in the context of free mobile educational applications (EduApps) which are paramount in learning-based digital start-ups and are essential for the implementation of circular economy (CE). The purpose of the present study is to identify dimensions of CPV specifically for EduApps and propose a conceptual model that would assist the digital start-up decisions which in turn can be a catalyst in navigating to a CE. Design/methodology/approach: The study uses the Netnography approach by analyzing online user-generated content. A total of 13,147 reviews posted on the Google play store after using top free education apps were coded using ATLAS.ti 9 software. Findings: Major dimensions of context-specific CPV are identified as technical value, content value, pedagogical value, gamification value and learning value. Subdimensions and items are extracted for each of these dimensions. Practical implications: The larger subscriber base drives sponsorships, advertisements and donations which underpin the business model of free EduApps. This can be obtained through an attractive value proposition. Identifying context-specific value dimensions would aid entrepreneurs in optimal value mix development decisions. The proposed framework can be utilized by both researchers (for scale creation, comparative studies and quantitative studies) and practitioners (for entrepreneurial decisions on better value propositions). Originality/value: CPV successfully describes consumer decision-making, but less attention is paid to linking the theory to the setting of mobile learning apps, where the bulk of research is focused on techniques like TAM, UTAUT, etc. In addition, studies identifying CPV from mobile apps with a specific focus on EduApps are sparse. Extant literature in this context is either based on a foundation of in-store business value dimensions or dominated by technical aspects when focused on the context of mobile apps. The current study bridges this gap. [ABSTRACT FROM AUTHOR]

The paper aims to comprehensively assess the effectiveness of generative learning strategies (GLS) using mobile learning technologies (MLT) in higher education, based on a quasi-experiment and quantitative and qualitative analysis. Methods included concept mapping, round table discussions, monitoring, computer testing, and statistical analysis. GLS with MLT demonstrated enhanced performance in Practical Ukrainian and English Language Courses. MLT-based GLS optimized teacher workload by reducing assignment checking time. Respondents rated MLT-based GLS for Ukrainian/English at 7.8/10. Effective methods included self-review, text correction, and concept mapping. The study validates MLT-based GLS in higher education, improving student performance and easing teacher tasks. Further research is planned for literature studies. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive overview related to higher-order thinking skills (HOTs) as well as recommendations for future study opportunities for educators and researchers. This study uses the systematic literature review (SLR) method to present critically the knowledge, ideas, and findings contained in academic-oriented literature. A total of 276 synthesized papers have been selected from the Scopus database. Annual publication frequency, number of documents, research methods, scope of themes, productive journals and proceedings in research, and learning approaches used in improving HOTs are reviewed. The results show that HOTs in science education have been receiving attention from researchers in the last eight years. Based on the 63 documents reviewed, include HOTs in the title and abstract. Proceedings in the Journal of Physics: Conference Series are the most contributors to publishing HOTs in science education. Quantitative methods are more widely adopted in HOTs research than research and development (R&D), qualitative, mixed, and non-empirical methods. In addition, models of teaching are also the most chosen theme in science education, and the most widely used mobile learning approach to increase HOTs. In the future, this study can become a reference for researchers to consider various solutions to maximize HOTs. [ABSTRACT FROM AUTHOR]

While extensive studies on informal online learning have been well documented to afford teachers' collaborative learning and knowledge sharing, little is still known about their motivational factors regarding the continuance intention of informal online learning. To this end, an extended expectation confirmation model (ECM) was proposed including intrinsic and extrinsic motivation. The proposed research model and several hypotheses were empirically evaluated using questionnaire surveys with the valid data collected from 231 Chinese in-service teachers in the shared mobile learning community. The results consolidate the appropriateness of the extended ECM to explain teachers' informal online learning continuance. Specifically, satisfaction is the major determinant of continuance intention, followed by perceived usefulness and intrinsic motivation. In addition, extrinsic motivation positively predicts perceived usefulness and confirmation. The results of this study provide some theoretical and practical implications into in-service teachers' continuance intention of informal online learning. [ABSTRACT FROM AUTHOR]

We explored the usage of smartphones in university classrooms across three different countries from the instructor’s perspective. Using a qualitative, cross-cultural approach, we interviewed 30 instructors via semi-structured interviews to understand their viewpoints and experience with student smartphone usage in class. Instructors reported positive benefits that included active learning and the accessibility and sustainability of the smartphone as a learning tool. Participants also highlighted the challenges that include preventing critical thinking and direct instructor issues including distraction, loss of control, and discouragement. Instead of implementing new technology strategies in a top-down manner, organisations should co-create strategies with students and instructors to enhance motivation and focus. With technology being a common cohabitant in the classroom, this paper adds value from the perspective of instructors regarding their struggles, opportunities, and strategies when dealing with smartphones. [ABSTRACT FROM AUTHOR]

In recent years, many mobile edge computing network solutions have enhanced data privacy and security and built a trusted network mechanism by introducing blockchain technology. However, this also complicates the task-offloading problem of blockchain-enabled mobile edge computing, and traditional evolutionary learning and single-agent reinforcement learning algorithms are difficult to solve effectively. In this paper, we propose a blockchain-enabled mobile edge computing task-offloading strategy based on multi-agent reinforcement learning. First, we innovatively propose a blockchain-enabled mobile edge computing task-offloading model by comprehensively considering optimization objectives such as task execution energy consumption, processing delay, user privacy metrics, and blockchain incentive rewards. Then, we propose a deep reinforcement learning algorithm based on multiple agents sharing a global memory pool using the actor–critic architecture, which enables each agent to acquire the experience of another agent during the training process to enhance the collaborative capability among agents and overall performance. In addition, we adopt attenuatable Gaussian noise into the action space selection process in the actor network to avoid falling into the local optimum. Finally, experiments show that this scheme's comprehensive cost calculation performance is enhanced by more than 10% compared with other multi-agent reinforcement learning algorithms. In addition, Gaussian random noise-based action space selection and a global memory pool improve the performance by 38.36% and 43.59%, respectively. [ABSTRACT FROM AUTHOR]

The digital era has transformed education, making online learning platforms crucial to the teaching process. The Ruangguru app has become one of the e-learning platforms widely used in Indonesia, as evidenced by more than 10 million downloads on the Play Store. This study aims to identify and analyze usability issues in the Ruangguru application using the Heuristic Evaluation method. Three experts assessed the app using Nielsen's ten Usability Heuristics principles. The evaluation revealed that while Ruangguru generally demonstrates acceptable usability, specific areas need improvement, particularly in user control and freedom, recognition rather than recall, and aesthetic and minimalist design. Recommendations include adding undo and redo functions, simplifying the user interface, and ensuring consistency in design elements. By implementing these suggestions, developers can enhance user engagement and satisfaction, further promoting interest in learning and education. [ABSTRACT FROM AUTHOR]

In today's digital age, the integration of digital technologies into the educational process has become a priority. The objective of this study is to analyze the conditions under which technology can support teachers in the design, implementation, and evaluation of teachinglearning (E/A) processes. This study specifically sought to answer the research question: "Can a self-evaluation tool help teachers' guide and design mobile learning activities?" by emphasizing the criterion of effectiveness. Through a qualitative analysis within the framework of the educational design research (EDR) methodology, a series of focus groups were carried out and classified by teachers' levels of experience, from beginners to experts. These groups provided detailed insights on the perception and use of mobile devices in education, specifically on the use of a self-assessment tool that supports teachers in designing activities with mobile devices. The results indicated a generally positive perception of mobile technologies in education, although variations in adoption and perception were observed depending on the teacher's level of experience. The mobile self-assessment tool was also commended. The findings support literature on mobile learning and teacher support tools, emphasizing the importance of teaching training and the transformative potential of mobile devices in modern pedagogy. [ABSTRACT FROM AUTHOR]