Search Results (812)

Genomic selection (GS) estimates the GEBV from genome-wide markers to reduce generation intervals and optimize germplasm selection, which is particularly advantageous for high-cost or late-expressed traits. While models like GBLUP are popular, they assume a polygenic architecture. In contrast, the Bayesian alphabet and machine learning (ML) can accommodate other types of genetic architectures. Given that no single model is universally optimal, stacking ensembles, which train a meta-model using predictions from diverse base learners, emerge as a compelling solution. However, the application of stacking in GS often overlooks non-additive effects. This study evaluated different stacking configurations for genomic prediction across 10 simulated traits, covering additive, dominance, and epistatic genetic architectures. A 5-fold cross-validation scheme was used to assess predictive ability and other evaluation metrics. The stacking approach demonstrated superior predictive ability in all scenarios. Gains were especially pronounced in complex architectures (100 QTLs, h² = 0.3), reaching an 83% increment over the best individual model (BayesA with dominance), and also in oligogenic scenarios with epistasis (10 QTLs, h² = 0.6), with a 27.59% gain. The success of stacking was attributed to two key strategies: base learner selection and the use of robust meta-learners (such as principal component or penalized regression) that effectively handled multicollinearity. Full article

During the past decade, chatbots have been integrated into commercial platforms to facilitate second language acquisition (SLA) by providing opportunities for interactive conversations. However, SLA learner progress is limited by chatbots that lack the contextualization typically added by instructors to college and university courses. The present study focuses on a collaborative Digital Learning Incubator (DLI) project dedicated to creating and testing a chatbot with a physical form, or avatar chatbot, called Slabot (Second Language Acquisition Bot), in two upper-level university courses at the University of Tennessee, asynchronous online Spanish 331 (Introduction to Hispanic Culture), and in-person Spanish 434 (Hispanic Culture Through Film). Students in these two courses believe that their oral skills would benefit from more opportunities to speak in Spanish. To provide the students with more practice and instructors with a tool for assessing Spanish oral skills in online and in-person courses, the DLI project objective was to advance current avatar chatbot platforms by enabling Slabot to elicit student responses appropriate for evaluation according to the American Council on the Teaching of Foreign Languages (ACTFL) standards. An initial test of Slabot was conducted, and the results demonstrated the potential for Slabot to achieve the project objective. Full article

The use of interpreter services is an important component of medical care. It is critical for medical students to practice this during training. It is known that simulation and role play provide important opportunities for students to practice skills. This scoping review maps the experience that medical students around the world have practicing with interpreter services in a simulated environment. We searched within three major databases (PubMed, ERIC, and SCOPUS) using a wide range of search terms for publications from the past 15 years. This scoping review was conducted according to PRISMA-ScR guidelines. Of the 1341 studies initially obtained from search terms, 22 were ultimately found to meet inclusion criteria. There is variability in curricula offered including when in medical school, what other specialties are involved, and how the education is conveyed. Most publications lacked longitudinal follow-up and assessment of learner competence was limited. Review articles, a prevalence study, and proof of concept studies also serve to demonstrate the breadth of publications on this subject. This is an area of important consideration within medical education today. Many studies highlight the relative scarcity of formal programs as well as a lack of consistency. Where programs do exist, the importance of including simulation is highlighted. Full article

Since the introduction of Carol Dweck’s landmark work in mindset, many scholars have studied the impact of a change in mindset on learning, behavior, and health. National and international large-scale studies have validated the consistent correlation between learners developing a growth mindset (knowing that they can learn and improve) and performance on learning outcomes and longer-term learning behaviors. Whilst mindset interventions can have a positive impact on student learning, recent years have shown the need for more than a change in messaging. For widescale and lasting improvements in mathematics learning, messages need to be specific to mathematics, and delivered through a change in teaching approach, with mindset ideas infused through teaching practices and through assessment. This paper shares the evidence on the need for a “mathematical mindset” approach and the wide scale benefits that the approach promises to bring about. Full article

This study aimed to develop an immersive virtual reality (VR) program and conduct preliminary evaluation of its feasibility and learner perception for enhancing nursing students’ clinical practicum education. The VR program was designed using the ADDIE model (analysis, design, development, implementation, and evaluation) and implemented on the UNITY 3D platform. Expert evaluation was conducted through a VR application, and its effectiveness was further assessed among 25 fourth-year nursing students in terms of immersion, presence, and satisfaction. The expert evaluation yielded a mean score of 6.54 out of 7, indicating acceptable content validity. Among learners, evaluation demonstrated immersion at 42.28 ± 2.37 out of 50 (95% CI: 41.30–43.26), presence at 81.36 ± 7.32 out of 95 (95% CI: 78.34–84.38), and satisfaction at 13.48 ± 1.26 out of 15 (95% CI: 12.96–14.00). Overall, the developed VR program demonstrated acceptable expert validity and positive learner perceptions. These preliminary findings suggest feasibility as a supplementary practicum. However, the single-group design without control comparison and reliance on self-reported measures preclude conclusions about educational effectiveness. Full article

This study developed and evaluated an adaptive e-learning environment for selected number theory topics using a mixed-methods research design, conducted over an eleven-month period across secondary and early tertiary education contexts. The evaluation focused on three primary outcome domains: (1) learning-related outcomes (problem-solving accuracy and task success rate), (2) learner engagement and activity indicators (daily logins and tasks completed per day), and (3) system usability, assessed according to Jakob Nielsen’s usability dimensions. Quantitative data were collected through student and teacher questionnaires (N = 264 students; N = 52 teachers) and large-scale logfile analytics comprising more than 825,000 recorded system interactions. Qualitative feedback from students and teachers complemented the quantitative analyses. The results indicate statistically significant increases in learner activity, task completion rates, and problem-solving success following the introduction of the adaptive system, as demonstrated by inferential statistical analyses with confidence intervals. Post-use evaluations further indicated high levels of learner motivation and self-confidence, along with positive perceptions of system usability. Teachers evaluated the system positively in terms of learnability, efficiency, and instructional integration. Logfile analyses also revealed sustained growth in daily engagement and task success over time. Overall, the findings suggest that adaptive e-learning environments can effectively support engagement, usability, and learning-related performance in number theory education, although further research is required to examine the sustainability of learning-related outcomes over extended periods and to further refine error-handling mechanisms. Full article

Effective communication is essential for professional practice, yet medical curricula rarely incorporate systematic, performance-based training. The Sell Yourself!—Presentation Techniques course was developed to address this gap through a two-day, practice-oriented program integrating rhetorical training, evolutionary psychology, and structured peer feedback. We examined anonymized institutional evaluations from 450 medical students using descriptive statistics and combined inductive–deductive thematic and content coding to gauge the perceived educational utility of the course. The course received a mean satisfaction rating of 9.6/10, with approximately 74% of students assigning the maximum score. Inductive analysis identified interactivity (143 mentions), practical usefulness (76), feedback and improvement (75), positive atmosphere (51), instructor quality (47), and multimedia examples (37) as key strengths, while critiques primarily concerned breaks and scheduling (62), course length and intensity (59), and smaller concerns regarding feedback processes, content structure, and technical issues. Deductive coding indicated perceived improvements across five predefined dimensions: increased confidence, rhetorical fluency, feedback quality, peer recognition, and cultural inclusivity. Structured rhetorical training appears to be well received by learners and may provide a feasible model for embedding communication competence in medical education. These findings also offer a transferable template for integrating performance-based communication training into other programs. However, conclusions are limited by reliance on self-reported perceptions and the absence of a control group or direct assessment of applied communication outcomes. Full article

Accurate credit risk assessment for small firms and farmers is crucial for financial stability and inclusion; however, many models still rely on binary default labels, overlooking the continuum of borrower vulnerability. To address this, we propose Transformer–LightGBM–Stacked Logistic Regression (TL-StackLR), a hybrid stacking framework for multi-class loan default prediction. The framework combines three learners: a Feature Tokenizer Transformer (FT-Transformer) for feature interactions, LightGBM for non-linear pattern recognition, and a stacked LR meta-learner for calibrated probability fusion. We transform binary labels into three risk tiers, Low, Medium, and High, based on quantile-based stratification of default probabilities, aligning the model with real-world risk management. Evaluated on datasets from 3045 firms and 2044 farmers in China, TL-StackLR achieves state-of-the-art ROC-AUC scores of 0.986 (firms) and 0.972 (farmers), with superior calibration and discrimination across all risk classes, outperforming all standalone and partial-hybrid benchmarks. The framework provides SHapley Additive exPlanations (SHAP) interpretability, showing how key risk drivers, such as income, industry experience, and mortgage score for firms and loan purpose, Engel coefficient, and income for farmers, influence risk tiers. This transparency transforms TL-StackLR into a decision-support tool, enabling targeted interventions for inclusive lending, thus offering a practical foundation for equitable credit risk management. Full article

This study examined how cultural background and self-reported gender influence student–faculty email communication in English-Medium Instruction (EMI) settings. Advanced international language learners (N = 113) wrote emails in English to either Korean or international professors without prior instruction. The emails were analyzed for framing elements and request strategies using holistic assessment. The findings revealed significant patterns in formality and strategy use based on professor nationality and student gender. Emails to Korean professors exhibited higher formality levels, especially among students with better framing appropriateness scores. Cultural differences emerged in request strategies: international students favored performative requests, while Korean students preferred disarmers. Self-reported gender also correlated with different framing strategies, particularly when communicating with Korean professors. These findings highlight the complex interaction among culture, gender, and pragmatic awareness in EMI academic correspondence. The study underscores the importance of understanding cross-cultural communication patterns in diverse educational environments and suggests the need for further research into multilingual communication practices in higher education to better support international student populations. Full article

United Nations Sustainable Development Goal 4 advocates for equitable access to and participation in quality inclusive education for all learners. Inclusive education has gained worldwide recognition for promoting equity and social justice for students with special educational needs. Although the existing literature acknowledges the significant role of teachers’ intention and behaviour towards the successful implementation of inclusive education, this area is under-researched in Sub-Saharan countries, including Ghana. In this study, applying the theory of planned behaviour (TPB), 484 teachers at pilot inclusive schools completed an online survey assessing the factors predicting their intention and teaching behaviour towards inclusive education. A path analysis of the TPB variables revealed that only attitude and self-efficacy significantly predicted the teachers’ intention to teach in inclusive classrooms. Moreover, both self-efficacy and intention were found to significantly predict inclusive behaviour. This study’s findings will strengthen the national commitment to implementing inclusive education policy and guide future research aimed at improving and expanding inclusive education in Ghana. Full article

Pronunciation assessment is central to computer-assisted pronunciation training (CAPT) and speaking tests, yet most systems still adopt a native norm, treating deviations from canonical L1 pronunciations as errors. In contrast, rating rubrics and psycholinguistic evidence emphasize intelligibility for a target listener population and show that listeners rapidly adapt their phonetic categories to new accents. We argue that automatic assessment should likewise be referenced to the target learner group. We build a Transformer-based mispronunciation detection (MD) model that computationally mimics listener adaptation: it is first pre-trained on multi-speaker Librispeech, then fine-tuned on the non-native L2-ARCTIC corpus that represents a specific learner population. Fine-tuning, using either synthetic or human MD labels, constrains updates to the phonetic space (i.e., the representation space used to encode phone-level distinctions, the learned phone/phonetic embedding space, and its alignment with acoustic representations), which means that only the phonetic module is updated while the rest of the model stays fixed. Relative to the pre-trained model, L2 adaptation substantially improves MD recall and F1, increasing ROC–AUC from 0.72 to 0.85. The results support a target-population norm and inform the design of perception-aligned, fairer automatic pronunciation assessment systems. Full article

Spatial skills, while vital to STEM (Science, Technology, Engineering, and Mathematics) and STEAM (Science, Technology, Engineering, Arts, and Mathematics) fields, are fundamental to understanding all mathematics. Yet the absence of spatial development in elementary curricula, particularly geometry, where such skills can be deeply explored, is compounded by a lack of theoretical and empirical research, especially in deaf education, where little research addresses learners’ spatial-geometric understandings and the ways their bodies contribute to developing such understandings. We first review relevant literature to interrelate mathematics, spatial activity, and embodied cognition with aesthetics for STEAM (Science, Technology, Engineering, Arts, and Mathematics) in deaf education. We then present a case study in which we observed and assessed Evan, a deaf student, as he worked on three geometry tasks. This video-based research utilises the Pirie–Kieren Theory/Model to further consider the aesthetic, spatially dynamic, and embodied ways that Evan’s geometric understandings emerged and evolved into more formal mathematical activity. Finally, we discuss the ways the study findings focused on spatial-geometric development support future STEAM education research and classroom mathematics towards growth-oriented learning for deaf students. Full article

Research on second language (L2) writing has primarily focused on linguistic skills, with limited attention to higher-order cognitive skills such as inference making. This study expands prior research by examining both concurrent and longitudinal effects of linguistic skills (vocabulary, grammatical knowledge, and morphological awareness) and inference making on L2 English writing quality among 135 Chinese high school English learners. Students’ linguistic skills, inference making, and writing were assessed in Grade 10 and Grade 11. Regression analyses showed that, in Grade 10, vocabulary, grammatical knowledge, and inference making significantly predicted writing quality, whereas in Grade 11, morphological awareness, grammatical knowledge, and inference making were significant predictors. Longitudinally, Grade 10 morphological awareness uniquely contributed to L2 writing quality in Grade 11 after controlling for the autoregressive effect of L2 writing quality in Grade 10. These findings highlight the key role of inference making in writing development and reveal that linguistic skills contribute to writing differently across grades. Pedagogically, the results underscore the importance of targeting grade-specific skills to support higher-quality English writing. Full article

Experiential learning involves gaining knowledge and understanding from real-life experiences, which helps develop new theories through fresh insights. Kolb described learning as the process of creating knowledge through transforming experience. Its main idea is that challenges and experiences, followed by reflection, lead to learning and growth. An experiential learning module (ELM) is a type of simulation that replicates a real-world situation, simplified to help participants understand complex problems from their perspective. It is based specifically on Kolb’s experiential learning cycle. ELMs use pictures, videos, and voice-over presentations to create a rich, contextually relevant, vicarious learning experience for classroom learners. In this study, the main ELM developed in Haiti was based on Kolb’s learning cycle. The primary goal of the ELM was to help global agriculturalists tackle complex issues related to food insecurity in developing countries. The purpose of this paper is to explain what experiential learning modules are and how to implement them in a study abroad program. An ELM on plantain production in Haiti was used as a case example. Students completed pre- and post-reflection surveys to evaluate their initial assumptions, expectations, and knowledge about the subject, as well as what they learned. A learning assessment measured their understanding of the ELM content. By analyzing the participants’ comments, the instructional approach proved effective in providing a vicarious experience within the classroom. The results from the initial classroom use of the banana and plantain learning module, along with student reactions, offered valuable feedback that led to proposed revisions and improvements to the tool. Full article

Bond wire degradation represents the predominant failure mechanism in IGBT modules, accounting for approximately 70% of power converter failures and posing significant reliability challenges in modern power electronic systems. Existing monitoring techniques face inherent trade-offs between measurement accuracy, implementation complexity, and electromagnetic compatibility. This paper proposes a physics-constrained ensemble learning framework for non-intrusive bond wire health assessment via V_ce-on prediction. The methodological innovation lies in the synergistic integration of multidimensional feature engineering, adaptive ensemble fusion, and domain-informed regularization. A comprehensive 16-dimensional feature vector is constructed from multi-physical measurements, including electrical, thermal, and aging parameters, with novel interaction terms explicitly modeling electro-thermal stress coupling. A dynamic weighting mechanism then adaptively fuses three specialized gradient boosting models (CatBoost for high-current, LightGBM for thermal-stress, and XGBoost for late-life conditions) based on context-aware performance assessment. Finally, the meta-learner incorporates a physics-based regularization term that enforces fundamental semiconductor properties, ensuring thermodynamic consistency. Experimental validation demonstrates that the proposed framework achieves a mean absolute error of 0.0066 V and R² of 0.9998 in predicting V_ce-on, representing a 48.4% improvement over individual base models while maintaining 99.1% physical constraint compliance. These results establish a paradigm-shifting approach that harmonizes data-driven learning with physical principles, enabling accurate, robust, and practical health monitoring for next-generation power electronic systems. Full article