Search Results (1,117)

In this post-digital age, writing assessment has been markedly influenced by advancements in artificial intelligence (AI), emphasizing the role of automated formative feedback in supporting second language (L2) writing. This study investigates how Norwegian teachers use an AI-driven automated feedback tool, the Essay Assessment Technology (EAT), in process writing for the first time. Framed by the second and third-order barriers framework, we looked at teachers’ beliefs and the design level changes that they made in their teaching. Data were collected in Autumn 2022, during the testing of EAT’s first prototype. Teachers were first introduced to EAT in a workshop. A total of 3 English as a second language teachers from different schools were informants in this study. Teachers then used EAT in the classroom with their 9th-grade students (13 years old). Through individual teacher interviews, this descriptive qualitative study explores teachers’ perceptions, user experiences, and pedagogical decisions when incorporating EAT into their practices. The findings indicate that teachers’ beliefs about technology and its role in student learning, as well as their views on students’ responsibilities in task completion, significantly influence their instructional choices. Additionally, teachers not only adopt AI-driven tools but are also able to reflect and solve complex teaching and learning activities in the classroom, which demonstrates that these teachers have applied design thinking processes in integrating technology in their teaching. Based on the results in this study, we suggest the need for targeted professional development to support effective technology integration. Full article

Background and Objectives: This study aimed to assess the prevalence of colistin prescriptions among patients with multidrug-resistant (MDR) Pseudomonas aeruginosa (P. aeruginosa) infections admitted to a tertiary teaching hospital in Jordan. Additionally, the study evaluated the appropriateness of colistin prescriptions and assessed resistance levels of this strain. Materials and Methods: In this retrospective study, adult patients who were infected with MDR P. aeruginosa and were admitted to Jordan University Hospital between January 2018 and March 2024 were included. Data on demographics, clinical characteristics, sources of infection, antibiotic therapy, and clinical outcomes were collected. Results: Out of the 85 patients who met the inclusion criteria for having MDR P. aeruginosa, colistin was administered to 16 patients (18.8%). Notably, approximately two-thirds (68.7%) of the isolates from patients who received colistin were classified as extensively drug-resistant (XDR). Among the isolates, 15 out of 16 (93.8%) were resistant to both ciprofloxacin and imipenem. Among the patients requiring colistin, five (31.3%) discontinued therapy, while two (12.5%) remained on colistin despite the availability of safer alternatives. No significant difference was observed in 30-day all-cause mortality between patients treated with colistin (0%) and those who were not (4.3%, p = 1.00). Similarly, the incidence of acute kidney injury did not differ significantly between the colistin group (0%) and the non-colistin group (p = 1.00). No significant difference was found in the hospital stay between colistin-treated patients (median 10.5 days, IQR [5.0–14.0]) and those not treated with colistin (median 13.0 days, IQR [7.0–21.0]), (p = 0.22). Conclusions: This study demonstrated that colistin was selectively initiated in high-risk patients, particularly those with XDR P. aeruginosa. However, its inappropriate continuation despite safer alternatives, as well as its discontinuation when no other options existed, raise concerns about antibiotic de-escalation practices. Interestingly, no significant differences in mortality or acute kidney injury were observed between patients who were treated with colistin and those who were not. These findings emphasize the need for antimicrobial stewardship programs and highlight the importance of large-scale trials to evaluate colistin’s efficacy and safety in MDR infections. Full article

Analyses of teaching behaviors in physical education (PE) classrooms are critical for evaluating teaching quality. Traditional evaluation methods primarily rely on manual analysis, which suffers from complex coding procedures, low efficiency, and suboptimal accuracy, hindering long-term sustainability in teaching quality improvement. Artificial intelligence (AI) technology offers a novel approach by enabling real-time data collection, automated annotation, and in-depth analysis of teaching behaviors, thereby supporting sustainable PE teaching optimization. Leveraging the CogVLM2-Video model, the research presents a system for real-time data collection, automated annotation, and in-depth analysis of teaching behaviors. It consists of four key modules: The perception layer handles data acquisition and input providing foundational data for analysis. The platform layer manages data processing and storage, ensuring integrity and security for long-term evaluation. The model layer focuses on behavior recognition and analysis, employing advanced algorithms for precise interpretation of teaching behaviors. The application layer delivers real-time feedback and adaptive recommendations, promoting sustained teaching improvement. The system architecture was initially validated using 50 basketball lesson videos. Then, the recognition model was trained on a Kinetics-400 subset, achieving 92% accuracy and 95% consistency with manual annotations. These results demonstrate the system’s practical value and long-term applicability, offering an efficient, precise solution for PE classroom teaching behavior assessment. Full article

In this article, we describe an approach to teaching introductory quantum mechanics and machine learning techniques. This approach combines several key concepts from both fields. Specifically, it demonstrates solving the Schrödinger equation using the discrete-variable representation (DVR) technique, as well as the architecture and training of neural network models. To illustrate this approach, a Python-based Jupyter notebook is developed. This notebook can be used for self-learning or for learning with an instructor. Furthermore, it can serve as a toolbox for demonstrating individual concepts in quantum mechanics and machine learning and for conducting small research projects in these areas. Full article

Background: The effective use of electronic health records (EHRs) is an essential clinical skill, but medical schools have traditionally provided limited systematic teaching on the topic. Inefficient use of EHRs results in delays in diagnosis, fragmented care, and clinician burnout. This study investigates the impact on medical students’ confidence, efficiency, and proficiency in extracting clinically pertinent information from patient records following an organised EHR teaching programme. Methods: This observational cohort involved 60 final-year medical students from three London medical schools. Participants received a structured three-phase intervention involving an introductory workshop, case-based hands-on practice, and guided reflection on EHR navigation habits. Pre- and post-intervention testing involved mixed-method surveys, simulated case tasks, and faculty-assessed data retrieval exercises to measure changes in students’ confidence, efficiency, and ability to synthesise patient information. Quantitative data were analysed using paired t-tests, while qualitative reflections were theme-analysed to identify shifts in clinical reasoning. Results: All 60 students successfully finished the intervention and assessments. Pre-intervention, only 28% students reported feeling confident in using EHRs effectively, with a confidence rating of 3.0. Post-intervention, 87% reported confidence with a rating of 4.5 (p < 0.01). Efficiency in the recovery of critical patient information improved from 3.2 to 4.6 (p < 0.01). Students also demonstrated enhanced awareness regarding system-related issues, such as information overload and fragmented documentation, and provided recommendations on enhancing data synthesis for clinical decision making. Conclusions: This study emphasises the value of structured EHR instruction in enhancing the confidence and proficiency of medical students in using electronic records. The integration of structured EHR education to medical curricula can better prepare future physicians in managing information overload, improve diagnostic accuracy, and enhance the quality of patient care. Future research should explore the long-term impact of structured EHR training on clinical performance, diagnostic accuracy, and patient outcomes during real-world clinical placements and postgraduate training. Full article

This paper proposes a KeyBERT-based method for constructing a knowledge graph of the electronic information curriculum system, aiming to enhance the structured representation and relational analysis of educational content. Electronic Information Engineering curricula encompass diverse and rapidly evolving topics; however, existing knowledge graphs often overlook multi-word concepts and more nuanced semantic relationships. To address this gap, this paper presents a KeyBERT-enhanced method for constructing a knowledge graph of the electronic information curriculum system. Utilizing teaching plans, syllabi, and approximately 500,000 words of course materials from 17 courses, we first extracted 500 knowledge points via the Term Frequency–Inverse Document Frequency (TF-IDF) algorithm to build a baseline course–knowledge matrix and visualize the preliminary graph using Graph Convolutional Networks (GCN) and Neo4j. We then applied KeyBERT to extract about 1000 knowledge points—approximately 65% of extracted terms were multi-word phrases—and augment the graph with co-occurrence and semantic-similarity edges. Comparative experiments demonstrate a ~20% increase in non-zero matrix coverage and a ~40% boost in edge count (from 5100 to 7100), significantly enhancing graph connectivity. Moreover, we performed sensitivity analysis on extraction thresholds (co-occurrence ≥ 5, similarity ≥ 0.7), revealing that (5, 0.7) maximizes the F1-score at 0.83. Hyperparameter ablation over n-gram ranges [(1,1),(1,2),(1,3)] and top_n [5, 10, 15] identifies (1,3) + top_n = 10 as optimal (Precision = 0.86, Recall = 0.81, F1 = 0.83). Finally, GCN downstream tests show that, despite higher sparsity (KeyBERT 64% vs. TF-IDF 40%), KeyBERT features achieve Accuracy = 0.78 and F1 = 0.75, outperforming TF-IDF’s 0.66/0.69. This approach offers a novel, rigorously evaluated solution for optimizing the electronic information curriculum system and can be extended through terminology standardization or larger data integration. Full article

Music learning can provide students with significant emotional and educational benefits, including stress relief, increased confidence, self-expression, brain development, enhanced attention, and perseverance. Despite these advantages, many students perceive music lessons as less important and tend to pay less attention in music classrooms. Consequently, motivation plays a crucial role for both teachers and students in facilitating an effective teaching and learning process in music education. Although motivation is generally understood as a student’s desire to engage in the learning process, it remains a complex concept that researchers and educators continue to explore across various educational contexts. This study aimed to review students’ motivation for classroom music by analyzing research from different studies in music education. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, 27 studies were reviewed and included. The findings identified four theoretical constructs related to motivation in classroom music: expectancy–value theory, self-determination theory, mastery motivation, and achievement goal theory. Among these, the first two were most commonly discussed across the reviewed studies. All the reviewed studies employed self-reported questionnaires as their primary assessment tools. Most studies indicated that girls demonstrated higher motivation levels than boys in music classrooms. Additionally, the findings emphasized that teachers’ instructional strategies are critical for enhancing students’ motivation. This study contributes to the field of music education by highlighting the pivotal role of teacher strategies in fostering motivation and by providing a comprehensive review of motivational theories, assessment tools, and gender-related motivational differences in music education. Full article

This study aims to develop and validate a user experience scale to construct an Actual System Use model for online courses based on the Technology Acceptance Model, allowing for a comprehensive assessment of the multidimensional factors affecting Learning Outcomes and Actual System Use in the context of online courses. The scale includes six core dimensions: Interactive Experience, Content Quality, Learning Outcomes, Teaching Quality, Technical Support, and Learning Motivation. Through a literature review, pre-survey, exploratory factor analysis, and confirmatory factor analysis, the reliability and validity of the developed scale were verified. A second-order complex Structural Equation Model was used to measure users’ Actual System Use with respect to online courses. The results demonstrate that the Interactive Experience and Learning Motivation dimensions play crucial roles in enhancing learners’ engagement and learning satisfaction, while Perceived Usefulness and Perceived Ease of Use significantly influence system usage behaviors. This study provides a systematic theoretical basis and empirical data for the design of online courses, offering valuable insights for optimizing course design and enhancing user experiences. Full article

With the need to ensure equitable and inclusive computer science (CS) education for K-12 students, much effort has been devoted to promoting secondary CS teachers’ practices and pedagogies. However, there is a lack of focus on elementary teachers’ experiences, especially those of teachers of color. This study stands at the intersections of Black/African American teachers teaching at an elementary school with a majority of historically underrepresented and economically disadvantaged students (Black/African Americans and Hispanic/Latinx). Using a basic qualitative approach with constant comparative analysis, this study revealed important insights regarding the professional growth manifested by six teachers of color over the course of computer science professional development and classroom implementation. Data analysis revealed five main themes reflecting the teachers’ growth: (1) Teachers reported positive outcomes including improved understanding, confidence, and intentions regarding CS integration as a result of attending PD; (2) Teachers demonstrated enhanced abilities to use a variety of tools and resources in CS teaching after PD; (3) Teachers discussed various pedagogies, including culturally and personally responsive pedagogical practices, and racial awareness to promote inclusive instruction in the classroom and used strategies to promote personal relevance more than the collective cultural values or beliefs in CS teaching specifically; (4) Teachers reported having ongoing reflections on how they can implement successful CS-integrated instruction with their enhanced knowledge and beliefs; (5) Positive student outcomes were both reported by the teachers and observed by the researchers as a result of teachers’ experimentation, which gave the teachers more confidence to enact CS teaching. Areas for improvement were also identified. This paper discussed the important implementations of fostering professional growth in teachers of color for broadening minoritized students’ participation in computing. Full article

To correspond to the advancement of technology, programming has become an indispensable course in university curricula. However, students easily become confused by the rules governing program execution or by complex logical structures. Mastering program structure and logic often is a significant challenge for beginners, especially. Despite the availability of information on programming on various websites and tools, including generative artificial intelligence (AI), there is still a gap between conceptual understanding and practical application for beginners. They overlook important implementation details or struggle to grasp the flow of a program, making the mastery of program logic a persistent challenge. To address these issues, we have developed a system that dynamically generates process architecture diagrams. Users upload their code, and the system produces corresponding diagrams that decompose and execute the code line by line. Its visual representation allows users to observe the program’s execution and aids them in comprehending the sequence and operational flow of the code. By understanding the structure and logic of the program intuitively, this system supplements traditional teaching methods and AI-assisted question-and-answer tools. The experimental results demonstrated that students found the system helpful to track their learning progress (87%) and improved their understanding of program code (81%). Additionally, 84% of students reported that the system was easy to use, highlighting its user-friendliness. In terms of student interest, 83% of students agreed that the interactive elements made learning more engaging, indicating that the system positively contributed to dynamic and enjoyable learning. However, 63% of students reported an improvement in coding and were influenced by the complexity of the programming tasks assigned. Despite this, the overall satisfaction with the system developed in this study was high. Full article

This paper presents a human-in-the-loop interactive framework for skeleton-based posture recognition, designed to support model training and artistic education. A total of 4870 labeled images are used for training and validation, and 500 images are reserved for testing across five core posture categories: standing, sitting, jumping, crouching, and lying. From each image, comprehensive skeletal features are extracted, including joint coordinates, angles, limb lengths, and symmetry metrics. Multiple classification algorithms—traditional (KNN, SVM, Random Forest) and deep learning-based (LSTM, Transformer)—are compared to identify effective combinations of features and models. Experimental results show that deep learning models achieve superior accuracy on complex postures, while traditional models remain competitive with low-dimensional features. Beyond classification, the system integrates posture recognition with a visual recommendation module. Recognized poses are used to retrieve matched examples from a reference library, allowing instructors to browse and select posture suggestions for learners. This semi-automated feedback loop enhances teaching interactivity and efficiency. Among all evaluated methods, the Transformer model achieved the best accuracy of 92.7% on the dataset, demonstrating the effectiveness of our closed-loop framework in supporting pose classification and model training. The proposed framework contributes both algorithmic insights and a novel application design for posture-driven educational support systems. Full article

Identifying and classifying mathematical errors is crucial to improving the teaching and learning process, particularly for first-year engineering students who often struggle with foundational mathematical competencies. This study aims to select the most appropriate theoretical framework for error classification by applying the Analytic Hierarchy Process (AHP), a multicriteria decision-making method. Five established classification models—Newman, Kastolan, Watson, Hadar, and Polya—were evaluated using six pedagogical criteria: precision in error identification, ease of application, focus on conceptual and procedural errors, response validation, and viability in improvement strategies. Expert judgment was incorporated through pairwise comparisons to compute priority weights for each criterion. The results reveal that the Newman framework offers the highest overall performance, primarily due to its structured approach to error analysis and its applicability in formative assessment contexts. Newman’s focus on reading, comprehension, transformation, and encoding addresses the most common errors encountered in the early stages of mathematical learning. The study demonstrates the utility of the AHP as a transparent and replicable methodology for educational model selection. It addresses a gap in the literature regarding evidence-based criteria for designing diagnostic instruments. These findings support the development of targeted pedagogical interventions in mathematics education for engineering programs. Full article

This study explores the development of reflective thinking among Physical Education Teachers (PETs) through a professional development program grounded in Paralympic values and inclusive education. Employing a qualitative action research design, the study involved seven PETs teaching in Greek primary schools. The intervention lasted ten weeks and comprised 20 Physical Education lessons, during which data were collected via semi-structured interviews, reflective journals, and non-participatory observation. The findings indicate a progressive shift in the teachers’ reflective thinking—from Technical Reflection to Practical and Critical Reflection—as defined in van Manen’s theoretical model. Most participants initially demonstrated a practical orientation but gradually developed a critical stance, incorporating values such as fairness, inclusion, and respect for diversity into their pedagogical thinking. Furthermore, the results revealed increased student sensitivity toward inclusive practices and a more democratic classroom climate. These findings suggest that targeted professional development programs can foster deeper reflective engagement and promote inclusive practices within Physical Education. This study highlights the potential of Paralympic values as meaningful educational content and reinforces the role of Physical Education as a catalyst for social inclusion and teacher professional growth. Full article

The acquisition of new knowledge by students represents a significant area of interest for universities, which seek to facilitate this process to enhance educational experience. There are two principal categories of learning approaches: surface and deep. The prevalence of a particular approach is contingent upon a number of individual and contextual factors. The aim of this study is to determine whether there are discernible differences in learning styles based on the geographical area of origin of the student. To this end, a multivariate analysis will be employed to compare the predominant learning approaches of health science university students using the Biggs R-SPQ-2F scale. A sample of 464 students was subjected to a multivariate analysis, specifically a Manova-Biplot, with the objective of facilitating the graphical representation of the relationships between the two learning approaches. A confirmatory factor analysis was conducted on the sample to corroborate the factor structure of the R-SPQ-2F. The findings indicated that the majority of students demonstrated proclivity towards deep learning, although their profiles exhibited heterogeneity related to their geographical context. The results may prove valuable in the characterization of the predominant learning approaches in a university community and the design of teaching strategies. Full article

Communication between families and schools is foundational for students’ academic success, community support for schools, and teachers’ experience. Yet, few preservice teacher education programs teach novices how to engage in equitable and effective collaborations with families. This manuscript reports on a pilot study in which preservice teachers traveled to a local community school and role-played academic conversations with adults whose children were enrolled in the school. The analysis of the transcripts of the role plays, a debrief panel by family participants, and written reflections of eight participating preservice teachers and six family participants used codes derived from the Dual Capacity-Building Framework for Family-School Partnerships. The findings show that both groups of participants found opportunities for thoughtful engagement. In the role plays, preservice teachers were most likely to demonstrate cognition by thinking flexibly about how to accommodate family and student needs. Family participants were most likely to demonstrate confidence by displaying their expertise and coaching the teachers. Activities like this may be useful sites for teachers and community members to practice effective collaboration skills. More broadly, the results may point toward an underutilized capacity within preservice teacher education to support family- and community-oriented schooling. Full article