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Search Results (766)

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Keywords = k-12 education

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23 pages, 800 KiB  
Article
“Innovatives” or “Sceptics”: Views on Sustainable Food Packaging in the New Global Context by Generation Z Members of an Academic Community
by Gerasimos Barbarousis, Fotios Chatzitheodoridis, Achilleas Kontogeorgos and Dimitris Skalkos
Sustainability 2025, 17(15), 7116; https://doi.org/10.3390/su17157116 - 6 Aug 2025
Abstract
The growing concern over environmental sustainability has intensified the focus on consumers’ perceptions of eco-friendly food packaging, especially among younger generations. This study aims to investigate the attitudes, preferences, and barriers faced by Greek university students regarding sustainable food packaging, a demographic considered [...] Read more.
The growing concern over environmental sustainability has intensified the focus on consumers’ perceptions of eco-friendly food packaging, especially among younger generations. This study aims to investigate the attitudes, preferences, and barriers faced by Greek university students regarding sustainable food packaging, a demographic considered pivotal for driving future consumption trends. An online questionnaire assessing perceptions, preferences, and behaviours related to sustainable packaging was administered to students, with responses measured on a five-point Likert scale. Three hundred and sixty-four students took part in this survey, with the majority (60%) of them being female. Principal component analysis was employed to identify underlying factors influencing perceptions, and k-means cluster analysis revealed two consumer segments: “Innovatives”, including one hundred and ninety-eight participants (54%), who demonstrate strong environmental awareness and willingness to adopt sustainable behaviours, and “Sceptics”, including one hundred sixty-six participants (46%), who show moderate engagement and remain cautious in their choices. Convenience, affordability, and clear product communication emerged as significant factors shaping student preferences. The findings suggest that targeted educational campaigns and transparent information are essential to converting positive attitudes into consistent purchasing behaviours. This research provides valuable insights for policymakers and marketers looking to design effective sustainability strategies tailored to the student population. Full article
(This article belongs to the Section Sustainable Food)
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18 pages, 1588 KiB  
Article
EEG-Based Attention Classification for Enhanced Learning Experience
by Madiha Khalid Syed, Hong Wang, Awais Ahmad Siddiqi, Shahnawaz Qureshi and Mohamed Amin Gouda
Appl. Sci. 2025, 15(15), 8668; https://doi.org/10.3390/app15158668 (registering DOI) - 5 Aug 2025
Abstract
This paper presents a novel EEG-based learning system designed to enhance the efficiency and effectiveness of studying by dynamically adjusting the difficulty level of learning materials based on real-time attention levels. In the training phase, EEG signals corresponding to high and low concentration [...] Read more.
This paper presents a novel EEG-based learning system designed to enhance the efficiency and effectiveness of studying by dynamically adjusting the difficulty level of learning materials based on real-time attention levels. In the training phase, EEG signals corresponding to high and low concentration levels are recorded while participants engage in quizzes to learn and memorize Chinese characters. The attention levels are determined based on performance metrics derived from the quiz results. Following extensive preprocessing, the EEG data undergoes several feature extraction steps: removal of artifacts due to eye blinks and facial movements, segregation of waves based on their frequencies, similarity indexing with respect to delay, binary thresholding, and (PCA). These extracted features are then fed into a k-NN classifier, which accurately distinguishes between high and low attention brain wave patterns, with the labels derived from the quiz performance indicating high or low attention. During the implementation phase, the system continuously monitors the user’s EEG signals while studying. When low attention levels are detected, the system increases the repetition frequency and reduces the difficulty of the flashcards to refocus the user’s attention. Conversely, when high concentration levels are identified, the system escalates the difficulty level of the flashcards to maximize the learning challenge. This adaptive approach ensures a more effective learning experience by maintaining optimal cognitive engagement, resulting in improved learning rates, reduced stress, and increased overall learning efficiency. Our results indicate that this EEG-based adaptive learning system holds significant potential for personalized education, fostering better retention and understanding of Chinese characters. Full article
(This article belongs to the Special Issue EEG Horizons: Exploring Neural Dynamics and Neurocognitive Processes)
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20 pages, 367 KiB  
Article
Power Dynamics and Discourse Technologies in Jordanian Colloquial Arabic Allophonic Consonant Variations
by Bassel Alzboun, Raed Al Ramahi and Nisreen Abu Hanak
Languages 2025, 10(8), 190; https://doi.org/10.3390/languages10080190 - 5 Aug 2025
Viewed by 142
Abstract
Most academic papers on Jordanian colloquial Arabic allophonic consonant variants have primarily examined their influence on the social status of speakers and their role in shaping linguistic prestige. However, there is a significant lack of research exploring the potential for manipulation and establishment [...] Read more.
Most academic papers on Jordanian colloquial Arabic allophonic consonant variants have primarily examined their influence on the social status of speakers and their role in shaping linguistic prestige. However, there is a significant lack of research exploring the potential for manipulation and establishment of power through the deliberate use of consonantal variants by Jordanian speakers in Arabic. Using a variety of allophonic consonantal variants, this study investigates how speakers of Jordanian colloquial Arabic attempt to construct their discourse of power. The targeted phonemes in the current study were /q/, /θ/, /ð/, and /k/. Focus groups were used to gather data, which were then examined within the framework of Fairclough’s technologized discourse and thematic approaches. Twenty persons, 10 women and 10 men, ranging in age from 18 to 45 years, comprised each of the two groups. The duration of each focus group session was 50 min. Analysis of the data indicates that the presence of [q], [θ], [ð], and [k] allophones in Standard Arabic is restricted to particular social circumstances, such as official and scientific environments. This usage is a common trait among those who have received formal education and privileged social standing. The findings also reveal that participants strategically utilize the allophonic variants [g], [ʔ], [k], [t̪], [d̪], and [tʃ] to exert influence over interlocutors by demonstrating authority related to social identity, gender, and emotional state. This study intends to advance discussions on allophonic consonant variants in Jordanian colloquial Arabic by providing insights into their manipulative functions. Full article
17 pages, 1707 KiB  
Article
A Structural Causal Model Ontology Approach for Knowledge Discovery in Educational Admission Databases
by Bern Igoche Igoche, Olumuyiwa Matthew and Daniel Olabanji
Knowledge 2025, 5(3), 15; https://doi.org/10.3390/knowledge5030015 - 4 Aug 2025
Viewed by 142
Abstract
Educational admission systems, particularly in developing countries, often suffer from opaque decision processes, unstructured data, and limited analytic insight. This study proposes a novel methodology that integrates structural causal models (SCMs), ontological modeling, and machine learning to uncover and apply interpretable knowledge from [...] Read more.
Educational admission systems, particularly in developing countries, often suffer from opaque decision processes, unstructured data, and limited analytic insight. This study proposes a novel methodology that integrates structural causal models (SCMs), ontological modeling, and machine learning to uncover and apply interpretable knowledge from an admission database. Using a dataset of 12,043 records from Benue State Polytechnic, Nigeria, we demonstrate this approach as a proof of concept by constructing a domain-specific SCM ontology, validate it using conditional independence testing (CIT), and extract features for predictive modeling. Five classifiers, Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) were evaluated using stratified 10-fold cross-validation. SVM and KNN achieved the highest classification accuracy (92%), with precision and recall scores exceeding 95% and 100%, respectively. Feature importance analysis revealed ‘mode of entry’ and ‘current qualification’ as key causal factors influencing admission decisions. This framework provides a reproducible pipeline that combines semantic representation and empirical validation, offering actionable insights for institutional decision-makers. Comparative benchmarking, ethical considerations, and model calibration are integrated to enhance methodological transparency. Limitations, including reliance on single-institution data, are acknowledged, and directions for generalizability and explainable AI are proposed. Full article
(This article belongs to the Special Issue Knowledge Management in Learning and Education)
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25 pages, 3590 KiB  
Article
Effectiveness of Firefighter Training for Indoor Intervention: Analysis of Temperature Profiles and Extinguishing Effectiveness
by Jan Hora
Fire 2025, 8(8), 304; https://doi.org/10.3390/fire8080304 - 1 Aug 2025
Viewed by 228
Abstract
This study assessed the effectiveness of stress-based cognitive-behavioral training compared to standard training in firefighters, emphasizing their ability to distribute extinguishing water and cool environments evenly during enclosure fires. Experiments took place at the Zbiroh training facility with two firefighter teams (Team A [...] Read more.
This study assessed the effectiveness of stress-based cognitive-behavioral training compared to standard training in firefighters, emphasizing their ability to distribute extinguishing water and cool environments evenly during enclosure fires. Experiments took place at the Zbiroh training facility with two firefighter teams (Team A with stress-based training and Team B with standard training) under realistic conditions. Using 58 thermocouples and 4 radiometers, temperature distribution and radiant heat flux were measured to evaluate water distribution efficiency and cooling performance during interventions. Team A consistently achieved temperature reductions of approximately 320 °C in the upper layers and 250–400 °C in the middle layers, maintaining stable conditions, whereas Team B only achieved partial cooling, with upper-layer temperatures remaining at 750–800 °C. Additionally, Team A recorded lower radiant heat flux densities (e.g., 20.74 kW/m2 at 0°) compared to Team B (21.81 kW/m2), indicating more effective water application and adaptability. The findings confirm that stress-based training enhances firefighters’ operational readiness and their ability to distribute water effectively during interventions. This skill is essential for safer and effective management of indoor fires under extreme conditions. This study supports the inclusion of stress-based and scenario-based training in firefighter education to enhance safety and operational performance. Full article
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21 pages, 1162 KiB  
Article
Positioning K-8 Classroom Teachers as Mathematics Instructional Leaders
by Melissa D. Boston, Juli K. Dixon, Sarah B. Bush, Lisa A. Brooks, Brian E. Moore, Treshonda Rutledge and Angel M. Maldonado
Educ. Sci. 2025, 15(8), 982; https://doi.org/10.3390/educsci15080982 (registering DOI) - 1 Aug 2025
Viewed by 179
Abstract
In this research report, we consider how to empower K-8 teachers as mathematics instructional leaders to initiate and sustain improvements within their schools, as a practical and sustainable model of enacting change in mathematics education more broadly by developing leadership from within. We [...] Read more.
In this research report, we consider how to empower K-8 teachers as mathematics instructional leaders to initiate and sustain improvements within their schools, as a practical and sustainable model of enacting change in mathematics education more broadly by developing leadership from within. We share the theoretical framework and findings from a 5-year National Science Foundation project. We utilized a longitudinal mixed methods approach, collecting data on teachers’ knowledge, instructional practices, leadership practices, and self-perception of growth throughout the project, triangulated with focus group data from teachers’ school administrators and project leaders and logs of leadership activities. Findings indicate positive changes in teachers’ knowledge and practices and in their role as instructional leaders in their schools, districts, and the mathematics education community. We conclude by sharing factors that appeared to support teachers’ growth as instructional leaders and implications for practice and research. Full article
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23 pages, 854 KiB  
Article
Adopting Generative AI in Future Classrooms: A Study of Preservice Teachers’ Intentions and Influencing Factors
by Yang Liu, Qiu Wang and Jing Lei
Behav. Sci. 2025, 15(8), 1040; https://doi.org/10.3390/bs15081040 - 31 Jul 2025
Viewed by 422
Abstract
This study investigated pre-service teachers’ (PTs) intentions to adopt generative AI (GenAI) tools in future classrooms by applying an extended Technology Acceptance Model (TAM). Participants were enrolled in multiple teacher-preparation programs within a single U.S. higher education institution. Through a structured GenAI-integrated activity [...] Read more.
This study investigated pre-service teachers’ (PTs) intentions to adopt generative AI (GenAI) tools in future classrooms by applying an extended Technology Acceptance Model (TAM). Participants were enrolled in multiple teacher-preparation programs within a single U.S. higher education institution. Through a structured GenAI-integrated activity using Khanmigo, a domain-specific AI platform for K-12 education, PTs explored AI-supported instructional tasks. Post-activity data were analyzed using PLS-SEM. The results showed that perceived usefulness (PU), perceived ease-of-use (PEU), and self-efficacy (SE) significantly predicted behavioral intention (BI) to adopt GenAI, with SE also influencing both PU and PEU. Conversely, personal innovativeness in IT and perceived cyber risk showed insignificant effects on BI or PU. The findings underscored the evolving dynamics of TAM constructs in GenAI contexts and highlighted the need to reconceptualize ease-of-use and risk within AI-mediated environments. Practically, the study emphasized the importance of preparing PTs not only to operate AI tools but also to critically interpret and co-design them. These insights inform both theoretical models and teacher education strategies, supporting the ethical and pedagogically meaningful integration of GenAI in K-12 education. Theoretical and practical implications are discussed. Full article
(This article belongs to the Special Issue Artificial Intelligence and Educational Psychology)
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51 pages, 1047 KiB  
Review
Healthy Food Service Guidelines for Worksites and Institutions: A Scoping Review
by Jane Dai, Reena Oza-Frank, Amy Lowry-Warnock, Bethany D. Williams, Meghan Murphy, Alla Hill and Jessi Silverman
Int. J. Environ. Res. Public Health 2025, 22(8), 1194; https://doi.org/10.3390/ijerph22081194 - 30 Jul 2025
Viewed by 271
Abstract
Healthy food service guidelines (HFSG) comprise food, nutrition, behavioral design, and other standards to guide the purchasing, preparation, and offering of foods and beverages in worksites and institutional food service. To date, there have been few attempts to synthesize evidence for HFSG effectiveness [...] Read more.
Healthy food service guidelines (HFSG) comprise food, nutrition, behavioral design, and other standards to guide the purchasing, preparation, and offering of foods and beverages in worksites and institutional food service. To date, there have been few attempts to synthesize evidence for HFSG effectiveness in non-K-12 or early childhood education sectors, particularly at worksites and institutional food services. We conducted a scoping review to achieve the following: (1) characterize the existing literature on the effectiveness of HFSG for improving the institution’s food environment, financial outcomes, and consumers’ diet quality and health, and (2) identify gaps in the literature. The initial search in PubMed and Web of Science retrieved 10,358 articles; after screening and snowball searching, 68 articles were included for analysis. Studies varied in terms of HFSG implementation settings, venues, and outcomes in both U.S. (n = 34) and non-U.S. (n = 34) contexts. The majority of HFSG interventions occurred in venues where food is sold (e.g., worksite cafeterias, vending machines). A diversity of HFSG terminology and measurement tools demonstrates the literature’s breadth. Literature gaps include quasi-experimental study designs, as well as interventions in settings that serve dependent populations (e.g., universities, elderly feeding programs, and prisons). Full article
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18 pages, 3269 KiB  
Article
Long-Term Traffic Prediction Using Deep Learning Long Short-Term Memory
by Ange-Lionel Toba, Sameer Kulkarni, Wael Khallouli and Timothy Pennington
Smart Cities 2025, 8(4), 126; https://doi.org/10.3390/smartcities8040126 - 29 Jul 2025
Viewed by 512
Abstract
Traffic conditions are a key factor in our society, contributing to quality of life and the economy, as well as access to professional, educational, and health resources. This emphasizes the need for a reliable road network to facilitate traffic fluidity across the nation [...] Read more.
Traffic conditions are a key factor in our society, contributing to quality of life and the economy, as well as access to professional, educational, and health resources. This emphasizes the need for a reliable road network to facilitate traffic fluidity across the nation and improve mobility. Reaching these characteristics demands good traffic volume prediction methods, not only in the short term but also in the long term, which helps design transportation strategies and road planning. However, most of the research has focused on short-term prediction, applied mostly to short-trip distances, while effective long-term forecasting, which has become a challenging issue in recent years, is lacking. The team proposes a traffic prediction method that leverages K-means clustering, long short-term memory (LSTM) neural network, and Fourier transform (FT) for long-term traffic prediction. The proposed method was evaluated on a real-world dataset from the U.S. Travel Monitoring Analysis System (TMAS) database, which enhances practical relevance and potential impact on transportation planning and management. The forecasting performance is evaluated with real-world traffic flow data in the state of California, in the western USA. Results show good forecasting accuracy on traffic trends and counts over a one-year period, capturing periodicity and variation. Full article
(This article belongs to the Collection Smart Governance and Policy)
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27 pages, 2966 KiB  
Article
Identifying Weekly Student Engagement Patterns in E-Learning via K-Means Clustering and Label-Based Validation
by Nisreen Alzahrani, Maram Meccawy, Halima Samra and Hassan A. El-Sabagh
Electronics 2025, 14(15), 3018; https://doi.org/10.3390/electronics14153018 - 29 Jul 2025
Viewed by 265
Abstract
While prior work has explored learner behavior using learning management systems (LMS) data, few studies provide week-level clustering validated against external engagement labels. To understand and assist students in online learning platforms and environments, this study presents a week-level engagement profiling framework for [...] Read more.
While prior work has explored learner behavior using learning management systems (LMS) data, few studies provide week-level clustering validated against external engagement labels. To understand and assist students in online learning platforms and environments, this study presents a week-level engagement profiling framework for e-learning environments, utilizing K-means clustering and label-based validation. Leveraging log data from 127 students over a 13-week course, 44 activity-based features were engineered to classify student engagement into high, moderate, and low levels. The optimal number of clusters (k = 3) was identified using the elbow method and assessed through internal metrics, including a silhouette score of 0.493 and R2 of 0.80. External validation confirmed strong alignment with pre-labeled engagement levels based on activity frequency and weighting. The clustering approach successfully revealed distinct behavioral patterns across engagement tiers, enabling a nuanced understanding of student interaction dynamics over time. Regression analysis further demonstrated a significant association between engagement levels and academic performance, underscoring the model’s potential as an early warning system for identifying at-risk learners. These findings suggest that clustering based on LMS behavior offers a scalable, data-driven strategy for improving learner support, personalizing instruction, and enhancing retention and academic outcomes in digital education settings such as MOOCs. Full article
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16 pages, 224 KiB  
Article
Developing a Preliminary List of Indicators for Green Restaurants in Taiwan: An Expert Consensus Approach
by Der-Fa Chen, Chun-Chung Liao, Shang-Hao Cheng, Wen-Jye Shyr and Chin-Chung Huang
Sustainability 2025, 17(15), 6882; https://doi.org/10.3390/su17156882 - 29 Jul 2025
Viewed by 220
Abstract
This study aims to develop a preliminary list of indicators suitable for green restaurants in Taiwan. The research methodology includes expert consensus (Delphi method) and incorporates interviews with field experts. An analysis of the responses provided by these industry experts led to the [...] Read more.
This study aims to develop a preliminary list of indicators suitable for green restaurants in Taiwan. The research methodology includes expert consensus (Delphi method) and incorporates interviews with field experts. An analysis of the responses provided by these industry experts led to the identification of five dimensions of evaluation indicators for green restaurants. The K–S test involves using a z-test on ordinal variables for single samples to determine whether the sample distribution diverges from the frequency distribution. This study analyzed the responses provided by the interviewed experts to identify and extract evaluation indicators for green restaurants. The extracted indicators comprise five dimensions (resource management, ingredient and product selection, environmental and indoor quality, green certification and management, and customer awareness and participation), 15 sub-dimensions, and 70 detailed indicators. The research results can serve as a reference for course planning in related programs at universities and colleges, as well as for industry planning of green restaurants, and as a reference for the promotion of national sustainable environmental policies in Taiwan. Therefore, based on the results of this study, recommendations are provided for educational institutions related to green restaurants, official organizations related to green restaurants, the industry related to green restaurants, and future researchers. Full article
(This article belongs to the Section Environmental Sustainability and Applications)
14 pages, 3906 KiB  
Article
An Investigation of the Process of Risk Coupling and the Main Elements of Coal-Mine Gas-Explosion Risk
by Shugang Li and Lu Gao
Fire 2025, 8(8), 294; https://doi.org/10.3390/fire8080294 - 25 Jul 2025
Viewed by 439
Abstract
This study suggests a method for analyzing the risk of methane explosions using the N-K model and Social Network Analysis (SNA) to understand how different risk factors related to coal-mine methane explosions are connected and change over time, aiming to prevent these accidents [...] Read more.
This study suggests a method for analyzing the risk of methane explosions using the N-K model and Social Network Analysis (SNA) to understand how different risk factors related to coal-mine methane explosions are connected and change over time, aiming to prevent these accidents effectively. We identified 41 secondary risk factors and four fundamental risk factors—human, equipment, environment, and management—based on the 4M accident causation theory. The SNA model was utilized to determine the main risk factors and their evolutionary routes, while the N-K model was utilized to quantify the degree of risk coupling. The findings show that the number of risk variables engaged in the methane-explosion risk system closely correlates with the number of accidents that occur and the maximum coupling level among the four elements. The primary control factors in the methane-explosion risk system are poor equipment management, broken safety monitoring and control systems, inadequate safety education and training, safety regulation violations, and poor safety production responsibility system implementation. We utilized the primary evolution paths and key elements to propose risk control approaches. A reference for ensuring safety in coal-mine operations can be found in the research findings. Full article
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14 pages, 215 KiB  
Article
Instructional Practices in K-12 Climate Change Education Across Disciplines: A Study of Early Adopters from New Jersey
by Lauren Madden and Jillian Baden Bershtein
Sustainability 2025, 17(15), 6722; https://doi.org/10.3390/su17156722 - 24 Jul 2025
Viewed by 306
Abstract
The United Nations’ 2030 Agenda for Sustainable Development centers on the 17 Sustainable Development Goals (SDGs). Among these goals, two address climate change education: Goal 13, Climate Action, and Goal 4, Quality Education. In order to build a more sustainable future, climate change [...] Read more.
The United Nations’ 2030 Agenda for Sustainable Development centers on the 17 Sustainable Development Goals (SDGs). Among these goals, two address climate change education: Goal 13, Climate Action, and Goal 4, Quality Education. In order to build a more sustainable future, climate change education is critical. In 2022, New Jersey became the first state in the US to integrate climate change into learning standards across subjects and grade levels K-12. In an effort to better understand the way in which teachers began to include climate change in their instruction, 50 teachers were observed implementing a lesson of their choosing that included climate change throughout the 2023–2024 academic year. Though most of the observed lessons featured science, many subject areas were included in the dataset, such as art, technology, history, and physical education. Teachers engaging in climate change instruction tended to use a variety of instructional practices. In nearly all cases, a multitude of methodologies were used in each lesson. However, small group instruction was featured in nearly all observed lessons. Quantitative descriptions of the findings are followed by three vignettes of exemplar instruction to provide a clearer understanding of the context of this work. These findings provide a scope for how climate change can be integrated in instructional settings at scale and suggestions for leveraging the experiences of early adopters of this innovation to support widespread implementation. Full article
22 pages, 1006 KiB  
Article
Technostress, Burnout, and Job Satisfaction: An Empirical Study of STEM Teachers’ Well-Being and Performance
by Liya Tu, Zebo Rao, Haozhe Jiang and Ling Dai
Behav. Sci. 2025, 15(7), 992; https://doi.org/10.3390/bs15070992 - 21 Jul 2025
Viewed by 374
Abstract
This study investigates the creators, effects, and inhibitors of technostress among STEM teachers, addressing a critical yet underexplored issue in the digitalization of education. Grounded in the technostress model and the job demands–resources (JD-R) model, the study examines the relationships among technostress creators, [...] Read more.
This study investigates the creators, effects, and inhibitors of technostress among STEM teachers, addressing a critical yet underexplored issue in the digitalization of education. Grounded in the technostress model and the job demands–resources (JD-R) model, the study examines the relationships among technostress creators, burnout, organizational effects (job satisfaction, organizational commitment, and work performance), and technostress inhibitors. A cross-sectional survey was conducted with 378 STEM teachers from Zhejiang Province, China. Structural equation modeling (SEM) was employed to test the hypothesized paths. The results revealed that technostress creators significantly increased teacher burnout and negatively affected organizational commitment and work performance. Burnout mediated the impact of technostress creators on job satisfaction and organizational commitment. Technostress inhibitors were found to alleviate burnout, mitigate technostress creators, and enhance STEM teachers’ commitment. These findings validate the applicability of the technostress model in the context of K–12 STEM education in China and highlight the importance of organizational mechanisms for supporting teacher well-being and performance. The study contributes to both theory and practice by proposing an integrative model of technostress and offering actionable recommendations for school leadership to effectively manage technostress. Full article
(This article belongs to the Section Educational Psychology)
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40 pages, 17591 KiB  
Article
Research and Education in Robotics: A Comprehensive Review, Trends, Challenges, and Future Directions
by Mutaz Ryalat, Natheer Almtireen, Ghaith Al-refai, Hisham Elmoaqet and Nathir Rawashdeh
J. Sens. Actuator Netw. 2025, 14(4), 76; https://doi.org/10.3390/jsan14040076 - 16 Jul 2025
Viewed by 1137
Abstract
Robotics has emerged as a transformative discipline at the intersection of the engineering, computer science, and cognitive sciences. This state-of-the-art review explores the current trends, methodologies, and challenges in both robotics research and education. This paper presents a comprehensive review of the evolution [...] Read more.
Robotics has emerged as a transformative discipline at the intersection of the engineering, computer science, and cognitive sciences. This state-of-the-art review explores the current trends, methodologies, and challenges in both robotics research and education. This paper presents a comprehensive review of the evolution of robotics, tracing its development from early automation to intelligent, autonomous systems. Key enabling technologies, such as Artificial Intelligence (AI), soft robotics, the Internet of Things (IoT), and swarm intelligence, are examined along with real-world applications in healthcare, manufacturing, agriculture, and sustainable smart cities. A central focus is placed on robotics education, where hands-on, interdisciplinary learning is reshaping curricula from K–12 to postgraduate levels. This paper analyzes instructional models including project-based learning, laboratory work, capstone design courses, and robotics competitions, highlighting their effectiveness in developing both technical and creative competencies. Widely adopted platforms such as the Robot Operating System (ROS) are briefly discussed in the context of their educational value and real-world alignment. Through case studies, institutional insights, and synthesis of academic and industry practices, this review underscores the vital role of robotics education in fostering innovation, systems thinking, and workforce readiness. The paper concludes by identifying the key challenges and future directions to guide researchers, educators, industry stakeholders, and policymakers in advancing robotics as both technological and educational frontiers. Full article
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