Search Results (899)

Background/Objectives: People living with neurological conditions are inactive despite widespread literature showing physical activity (PA) is beneficial for this population. To impact long term changes in PA behavior, physical therapists treating individuals with neurological conditions need to provide effective PA promotion in combination with behavior change techniques (BCTs). The purpose of this study is to (1) characterize the use of BCTs during neurorehabilitation, and (2) to gain an understanding of considerations related to PA promotion. Methods: Observations of outpatient physical therapy encounters with subsequent semi-structured interviews were conducted. Observations were transcribed with detailed field notes and analyzed with descriptive analysis and deductive coding. Interviews were analyzed with thematic analysis. Results: Observations indicated that PA promotion in neurorehabilitation practice emphasizes home exercise programs, with less focus on aerobic activity. The most common BCTs used were instruction on how to perform the behavior, behavioral practice and rehearsal, and social support. Primary themes that impacted physical therapist use of BCTS for PA promotion included knowledge, decision processes, perceived role, beliefs, environmental context, and social influences. Conclusions: PA promotion from physical therapists in neurorehabilitation is not targeting activity guidelines and there is uncertainty about using behavior change strategies and PA guidelines for PA promotion. Full article

Auxiliary frequency modulation (FM) for coal-fired units has been recognized as a promising approach through multiple batteries, which is due to their rapid charging and discharging characteristics. However, long-period engineering application needs continuous optimization of operational strategies to resist the decay characteristics of the battery, which greatly increases the difficulty of promotion. Hence, two replacement strategies of the battery were first proposed in this work, and they are characterized by simple operation. To test their feasibility, a lead–acid battery was selected as one study example, and the corresponding relationship between the duration day and the replacement scheme was emphatically analyzed, according to the AGC instruction and the self-adjustment capacity of coal-fired units. Results showed that the replacement capacity of the battery is nearly linear in the duration day, while the difference from the discharge depth is negligible in this study. In addition, the capacity ratio of 1.3 to 5 is considered to have the best application potential because of the same duration days between old and new batteries. The commutative replacement can immortally extend the duration day, and obviously, the replacement process of old and new batteries always maintains that two battery groups work. Conclusively, the case analysis for two replacement strategies showed that they deeply lowered the initial capacity of the battery, which can reduce the investment costs. In a word, two replacement strategies for the battery proposed in this study provide a reference for the economic evaluation and optimization of battery use for auxiliary FM. Full article

This study investigates the effectiveness of integrating explicit learning-strategy instruction into gatekeeper mathematics courses to foster a math growth mindset, self-regulated learning (SRL), and improved academic performance among underrepresented minority students. The intervention was implemented across four key courses—College Algebra I/II and Calculus I/II—and incorporated evidence-based cognitive, metacognitive, and behavioral learning strategies through course materials, class discussions, and reflective assignments. Grounded in a conceptual framework linking learning-strategy instruction, growth mindset, SRL, and performance—while accounting for students’ social identities—the study explores both direct and indirect effects of the intervention. Using an explanatory sequential mixed-methods design, we first collected quantitative data via pre- and post-surveys/tests and analyzed performance outcomes, followed by qualitative focus groups to contextualize the findings. Results showed no significant effects of the intervention on growth mindset or SRL, nor evidence of mediation through these constructs. The direct effect of the intervention on performance was negative, though baseline mindset, SRL, and pre-course preparedness strongly predicted outcomes. No moderation effects were detected by student identities. The findings suggest that while explicit learning-strategy instruction may not independently shift mindset or SRL in the short term, pre-existing differences in these areas are consequential for performance. Qualitative findings provided further context for understanding how students engaged with the strategies and how instructor implementation shaped outcomes. These insights inform how learning strategies might be more effectively embedded in introductory math to support success and equity in STEM pathways, particularly in post-COVID educational contexts. Full article

Assessing teaching behavior is essential for improving instructional quality, particularly in Physical Education, where classroom interactions are fast-paced and complex. Traditional evaluation methods such as questionnaires, expert observations, and manual discourse analysis are often limited by subjectivity, high labor costs, and poor scalability. These challenges underscore the need for automated, objective tools to support pedagogical assessment. This study explores and compares the use of Transformer-based language models for the automatic classification of teaching behaviors from real classroom transcriptions. A dataset of over 1300 utterances was compiled and annotated according to the teaching styles proposed in the circumplex approach (Autonomy Support, Structure, Control, and Chaos), along with an additional category for messages in which no style could be identified (Unidentified Style). To address class imbalance and enhance linguistic variability, data augmentation techniques were applied. Eight pretrained BERT-based Transformer architectures were evaluated, including several pretraining strategies and architectural structures. BETO achieved the highest performance, with an accuracy of 0.78, a macro-averaged F1-score of 0.72, and a weighted F1-score of 0.77. It showed strength in identifying challenging utterances labeled as Chaos and Autonomy Support. Furthermore, other BERT-based models purely trained with a Spanish text corpus like DistilBERT also present competitive performance, achieving accuracy metrics over 0.73 and and F1-score of 0.68. These results demonstrate the potential of leveraging Transformer-based models for objective and scalable teacher behavior classification. The findings support the feasibility of leveraging pretrained language models to develop scalable, AI-driven systems for classroom behavior classification and pedagogical feedback. Full article

Integrating information and communication technology (ICT) into STEM education enhances instructional quality and cultivates students’ interdisciplinary problem-solving. STEM teachers’ attitudes—driven by perceived ease of use (PEOU) and perceived usefulness (PU)—are pivotal in ICT adoption, and pre-service training offers a vital opportunity to shape these attitudes. Yet, empirical studies investigating how specific training strategies influence ICT attitudes via PEOU and PU remain scarce. Using a mixed-methods approach combining questionnaires and interviews, the results indicate that pre-service training significantly improved STEM teachers’ attitudes toward ICT-enhanced teaching. Socially interactive strategies (role models and collaboration) enhanced attitudes via PEOU by boosting confidence and reducing technology-related anxiety, cognitive design strategies (reflection and instructional design) operated through PU by emphasizing ICT’s pedagogical value, and experiential feedback strategies (authentic experience and feedback) influenced attitudes through both PEOU and PU, fostering integrated technical and pedagogical development. These findings support an integrated SQD–TAM framework and provide practical guidance for designing pre-service STEM teacher programs to promote sustained ICT adoption in China, and meanwhile highlights the importance of strategically sequencing training to cultivate both technological competence and pedagogical insight among future STEM educators. Full article

Following the significant disruption of traditional teaching by the COVID-19 pandemic, gamified education—an approach integrating technology and cognitive strategies—has gained widespread attention and use among educators and learners. This study explores how game-based learning, supported by situated learning theory and game design elements, can boost learner motivation and knowledge construction. Using 20,293 user comments from the Chinese video platform Bilibili, the study applies sentiment analysis and LDA to uncover users’ sentimental tendencies and cognitive themes. The analysis identifies four core themes: (1) The application of contextual strategies in language learning, (2) Autonomous exploration and active participation in gamified learning, (3) Progressive enhancement of logical thinking in gamified environments, and (4) Teaching innovation in promoting knowledge construction and deepening. Building on these findings, the study further develops a cognitive scaffolding model integrating “contextualization–procedural logic–active construction” to explain the mechanisms of motivation–cognition interaction in gamified learning. Methodologically, this study innovatively combines LDA topic modeling with sentiment analysis, offering a new approach for multidimensional measurement of learner attitudes in gamified education. Theoretically, it extends the application of situated learning theory to digital education, providing systematic support for instructional design and meaning-making. Findings enrich empirical research on gamified learning and offer practical insights for optimizing educational platforms and personalized learning support. Full article

Digital transformation (DT) is rapidly reshaping education at multiple levels, including curriculum, instructional practices, and institutional culture. Within this context, entrepreneurship education has become a key field for preparing individuals to navigate uncertainty and generate social and economic value in a digital society. Entrepreneurial competencies are increasingly conceptualized as a multidimensional construct that encompasses creativity, problem-solving, critical thinking, collaboration, and digital literacy. This study aims to identify core entrepreneurial competencies relevant to the digital era and examine how technology-integrated instructional strategies contribute to their development. A systematic literature review was conducted in accordance with PRISMA 2020 guidelines, analyzing 72 peer-reviewed journal articles published between January 2021 and June 2025. The findings indicate that DT drives structural changes in education beyond tool adoption, with technologies such as artificial intelligence (AI), data analytics, and digital collaboration platforms serving as catalysts for innovative thinking and entrepreneurial behavior. These technologies are not merely supportive tools but are embedded in competency-based learning processes. This review provides a comprehensive competency framework integrating three domains, AI-collaborative pedagogy validation, and implementation strategies, enabling educators, curriculum developers, and policymakers to redesign entrepreneurship education that aligns with the realities of digital learning environments and fosters future-ready entrepreneurial capabilities. This conceptual framework theoretically systematizes the integration of innovative thinking and ethical execution capabilities required in the digital era, contributing to defining the future direction of entrepreneurship education. Full article

Background: This study used inertial measurement unit (IMU)-based posture angle estimates to define proxy risk labels and investigated whether these labels can be predicted from demographic, anthropometric, and lifestyle variables through machine learning analysis. Methods: Thirty healthy individuals aged 18–25 years were included. Demographic and anthropometric data and information on daily living activities were collected. The IMU sensors were placed at vertebral levels C1, C7, T5, T12, and L5. Participants were instructed to stand in an upright posture, followed by a relaxed daily posture. Anatomic postural changes between these positions were analyzed. Cervical lordosis, thoracic kyphosis, lumbar lordosis, and scoliosis risks were predicted using machine learning algorithms, including Random Forest (RF) and Artificial Neural Networks (ANN). Results: Incorrect postures during desk work and phone use were associated with an increased likelihood of posture-related deviations, such as cervical lordosis, thoracic kyphosis, and lumbar lordosis. Conversely, daily physical activity reduced these deviations. Using LOSO and stratified cross-validation with imbalance handling, balanced accuracies ranged between 0.55 and 0.82 across targets, with majority-class baselines between 0.53 and 0.87. For cervical lordosis risk, RF achieved a 0.82 balanced accuracy (95% CI: 0.74–0.97), while other categories showed a moderate but consistent performance. AUPRC values exceeded baseline levels across all models. Conclusions: IMU-based posture angle estimates can be used to identify posture-related risk categories. In this study, ML models have demonstrated predictive relationships with demographic, anthropometric, and lifestyle variables. These findings provide exploratory evidence based on IMU-derived proxy labels in a small cohort of healthy young adults. They represent exploratory indicators of postural deviation rather than clinical outcomes and may motivate future studies on preventive strategies. Importantly, the results remain underpowered relative to the a priori power targets and should be interpreted qualitatively. Full article

Non-traditional learners comprise approximately 73% of undergraduate enrollment, representing diverse populations including first-generation college students, adult learners, veterans, multilingual learners, and students with family responsibilities. Despite their numerical dominance, these students face systemic barriers that traditional pedagogical approaches often fail to address. This mixed-methods study examined how Universal Design for Learning (UDL) principles impact non-traditional learners’ educational experiences in higher education. Using a convergent parallel design with 154 participants from a Hispanic-serving institution, the study collected quantitative data through the validated Personalized Learning Supporting Instrument (PLSI) and qualitative data from open-ended questions. The refined 12-item PLSI demonstrated strong psychometric properties. While UDL factors showed limited direct association with GPA overall, Flexible Instructional Methods and Materials significantly predicted academic performance. Qualitative analysis identified six barrier themes (online learning difficulties, course content issues, financial constraints, balancing responsibilities, accessibility challenges, and health interruptions) and five positive impact themes (interactive learning, supportive environments, skill development, goal clarification, and effective assignments). Demographic analysis revealed counterintuitive patterns—students with traditional “barriers” achieved high GPAs at rates of 73–76%, while first-generation students showed the lowest high GPA rate (53.2%). These findings challenge deficit-based assumptions about non-traditional learners while revealing important equity gaps. This study demonstrates both the promise and limitations of UDL for diverse populations, suggesting institutions need comprehensive approaches with differentiated support strategies. Full article

(1) Background: Physical Education sessions subject students to various physical and physiological demands that teachers must understand to design interventions aimed at improving health and fitness. This study aimed to quantify and compare external and internal load before and after implementing two intervention programs: one based on the Game-Centered Model and another Hybrid Model that combines the Game-Centered Model with the Sport Education Model. (2) Methods: A total of 47 first-year secondary school students participated, divided into two naturally formed groups. Pre- and post-intervention assessments involved 4 vs. 4 matches monitored using WIMU Pro™ inertial measurement units and heart rate monitors to collect kinematic, neuromuscular, and physiological data. The combined use of inertial sensors and heart rate monitors enabled the objective quantification of students’ physical demands. (3) Results: No significant improvements were observed between pre- and post-tests, possibly due to the short duration of the interventions. However, the Hybrid Model generated higher levels of external load, both kinematic and neuromuscular, in the post-test. (4) Conclusions: The Hybrid Model appears more effective in increasing students’ physical engagement. Inertial sensors represent a valid and practical tool for monitoring and adjusting instructional strategies in school-based Physical Education. Full article

Providing feedback is a key instructional strategy in motor learning. Recently, interest has grown in self-controlled (SC) feedback, which allows learners to choose when to receive feedback. However, evidence on its effectiveness remains mixed, and its impact across different learning phases and populations is unclear. This meta-analysis assessed the effect of SC feedback on motor skill learning during acquisition, retention, and transfer phases, compared to passively received (PR) feedback and Yoked (YK) feedback groups. A comprehensive search of three datasets identified 29 studies comprising 1147 participants. Although SC feedback did not bring a significant advantage in the acquisition phase, it facilitated motor skill learning in both the retention and transfer phases. Moderator analyses revealed that cognitive status influenced SC feedback efficacy during the acquisition phase, with significant effects observed only in cognitively impaired individuals. Additionally, skill type moderated outcomes in the transfer phase, with series skills showing greater benefits than discrete skills, and this effect was significant only when compared to the YK group. In summary, SC feedback-enhanced motor skill learning in the retention and transfer phases, supporting its role in the consolidation and generalization of learning. Additionally, cognitively impaired individuals showed greater benefits during the acquisition phase, and series skills demonstrated more substantial gains in the transfer phase. Full article

Background: Adolescent obesity represents a global public health issue, with an urgent need for tailored intervention strategies, particularly in school settings. Objectives: This study investigated the impact of adapted basketball training—with and without video summaries—on BMI, fitness, motivation, and technical skills in adolescents with moderate obesity. Methods: Fifty-five participants were randomly assigned to one of three instructional modalities—a modified basketball program enriched with video summaries (ADAPT + VID), a modified program without videos (ADAPT), and a traditional program (CONT)—and completed an eight-week intervention. Before (T0) and after (T1) the intervention, participants in all three groups underwent testing to assess: (i) anthropometric measurements (BMI), (ii) technical accuracy (passing, dribbling, and shooting), (iii) physical fitness using the Intermittent Fitness Test 15-15 (Spartacus Test), and (iv) motivation using the Situational Motivation Scale. Results: Assessments revealed significant improvements in the ADAPT + VID group, with notable reductions in BMI (d = 0.25, p < 0.05), enhanced physical fitness (d = 0.19, p = 0.002), and improved performance in passing (d = 1.42, p < 0.001), dribbling (d = 1.30, p < 0.001), and free-throw shooting (d = 0.80, p < 0.001). Moreover, a significant increase in intrinsic motivation (d = 1.18, p < 0.001) and a reduction in amotivation (d = 1.12, p < 0.001) were observed. Conclusions: These findings suggest that incorporating pedagogical video summaries into an adapted basketball program may effectively improve physical health, motor skills, and motivation among adolescents with moderate obesity. Full article

Risk identification is a critical concern in the construction industry. In recent years, there has been a growing trend of applying artificial intelligence (AI) tools to detect risks from unstructured data sources such as news articles, social media, contracts, and financial reports. The rapid advancement of large language models (LLMs) in text analysis, summarization, and generation offers promising opportunities to improve construction risk identification. This study conducts a comprehensive benchmarking of natural language processing (NLP) and LLM techniques for automating the classification of risk items into a generic risk category. Twelve model configurations are evaluated, ranging from classical NLP pipelines using TF-IDF and Word2Vec to advanced transformer-based models such as BERT and GPT-4 with zero-shot, instruction, and few-shot prompting strategies. The results reveal that LLMs, particularly GPT-4 with few-shot prompts, achieve a competitive performance (F1 = 0.81) approaching that of the best classical model (BERT + SVM; F1 = 0.86), all without the need for training data. Moreover, LLMs exhibit a more balanced performance across imbalanced risk categories, showcasing their adaptability in data-sparse settings. These findings contribute theoretically by positioning LLMs as scalable plug-and-play alternatives to NLP pipelines, offering practical value by highlighting how LLMs can support early-stage project planning and risk assessment in contexts where labeled data and expert resources are limited. Full article

Objective: U.S. courts have implemented interventions educating jurors about implicit bias, although evidence for their effectiveness remains limited. We explored public perceptions of these interventions that might influence their ability to improve trial fairness and identified psychological factors predicting such perceptions. Hypotheses: We hypothesized that certain psychological factors (i.e., political conservatism, psychological reactance, skepticism toward social scientists, implicit and explicit racial bias, advantaged-group identity management strategies) would predict support for implicit bias interventions in courts. Method: White participants (N = 1016)—some of whom watched an implicit bias intervention in one of two formats (educational video, judicial instructions)—provided their perceptions of implicit bias interventions, evaluated the intervention they watched (if applicable), and completed individual difference measures. Results: Overall, participants supported implicit bias interventions in both formats. However, political conservatism and other hypothesized individual difference measures were associated with less favorable perceptions. We further explored participants’ perspectives via a thematic content analysis of open-ended impressions of the interventions. Conclusions: Courts are adopting implicit bias interventions despite mixed research regarding their effectiveness and a limited understanding of how they are perceived. Our findings suggest that White participants generally favor these interventions and offer insight into the nuances of their perceptions. Full article

Artificial Intelligence (AI) is reshaping higher education by enabling personalized learning (PL) and enhancing teaching and learning practices. To examine global research trends, pedagogical paradigms, equity and sustainability considerations, instructional strategies, learning outcomes, and interdisciplinary collaboration, this study systematically reviewed 29 articles indexed in the Social Sciences Citation Index (SSCI) Q1, representing the top 25% of cited articles, published between January 2020 and December 2024 in the Web of Science database. Results indicate that AI-PL research is concentrated in Asia, particularly China, and predominantly situated within education and computer science. Quantitative designs prevail, often complemented by qualitative insights, with supervised machine learning as the most common algorithm. While constructivist principles implicitly guide most studies, explicit theoretical grounding improves AI-pedagogy alignment and educational outcomes. AI demonstrates potential to enhance instructional approaches such as PBL, STEAM, gamification, and UDL, and to foster higher-order skills, yet uncritical use may undermine learner autonomy. Systematic attention to equity and SDG-related objectives remains limited. Emerging interdisciplinary collaborations show promise but are not yet fully institutionalized, constraining integrative system design. These findings underscore the need for stronger theoretical framing, alignment of AI with pedagogical and societal imperatives, and professional development to enhance educators’ AI literacy. Coordinated efforts among academia, industry, and policymakers are essential to develop scalable, context-sensitive AI solutions that advance inclusive, adaptive, and transformative higher education. Full article