Hasil untuk "Special aspects of education"

M. Greenberg, C. Kusché, Elizabeth T. Cook et al.

Alan J. Dutson, R. H. Todd, S. Magleby et al.

Makbule Buse Dundar Sari, Berkant Sezer

Abstract Background In recent years, advancements in artificial intelligence (AI) have led to the widespread integration of large language models and their chatbot applications into various fields, including dental education. This study aimed to evaluate the accuracy of ChatGPT-4 Omni (ChatGPT-4o) and Gemini Advanced in answering multiple-choice questions from the Turkish Dentistry Specialization Exams (DUS) across various disciplines. Methods A total of 1,504 multiple-choice questions from 10 years of DUS exams were analyzed to compare the accuracy of ChatGPT-4o and Gemini Advanced. The questions were categorized into Fundamental Medical Sciences (n = 514) and Clinical Dental Sciences (n = 990). Each question was submitted to both chatbots, resulting in 3,008 responses. Accuracy was assessed using the official answer keys. Chi-square tests and Bonferroni post-hoc analyses were used to compare accuracy across disciplines and examine year-based variations. Results ChatGPT-4o achieved an overall accuracy rate of 84%, while Gemini Advanced achieved 81.8% (p = 0.110). For the Fundamental Medical Sciences questions, no statistically significant differences were observed across sub-disciplines, with overall accuracies of 92.6% for ChatGPT-4o and 93.4% for Gemini Advanced. For the Clinical Dental Sciences questions, ChatGPT-4o outperformed Gemini Advanced in Prosthetic Dentistry (p = 0.013) and Dentomaxillofacial Radiology (p = 0.001), whereas Gemini Advanced showed higher accuracy in Pediatric Dentistry (p = 0.008). Across all Clinical Dental Sciences questions, ChatGPT-4o achieved an accuracy of 79.5%, compared to 75.8% for Gemini Advanced, and this difference was statistically significant (p = 0.046). Conclusions AI-based chatbots demonstrate strong potential in answering multiple-choice dentistry questions. However, variations in performance across disciplines were observed, indicating differences in accuracy depending on the subject area. These findings highlight the potential educational implications of integrating AI into dental curricula, particularly as supplementary tools for exam preparation and knowledge reinforcement. Nevertheless, cautious integration is required to ensure that AI supports, rather than replaces, critical thinking and professional expertise.

Danielle R. Thomas, Conrad Borchers, Kirk P. Vanacore et al.

Generative Artificial Intelligence (GenAI) is now widespread in education, yet the efficacy of GenAI systems remains constrained by the quality and interpretation of the labeled data used to train and evaluate them. Studies commonly report inter-rater reliability (IRR), often summarized by a single coefficient such as Cohen's kappa (k), as a gatekeeper to ``ground truth.'' We argue that many educational assessment and practice support settings include challenges, such as high-inference constructs, skewed label distributions, and temporally segmented multimodal data, which yield potential misapplication or misinterpretation of threshold-based heuristics for IRR. The growing use of large language models as annotators and judges introduces risks such as automation bias and circular validation. We propose four practical shifts for establishing ground truth: (1) treat IRR as a diagnostic signal to localize disagreement and refine constructs rather than a mechanical acceptance threshold (e.g., k > 0.8); (2) require transparent reporting of rater expertise, codebook development, reconciliation procedures, and segmentation rules; (3) mitigate risks in LLM annotation through bias audits and verification workflows; and (4) complement agreement statistics with validity and effectiveness evidence for the intended use, including uncertainty-aware labeling (e.g., assigning different labels to the same item to capture nuance), criterion-related checks (e.g., predictive tests to check if labels forecast the intended outcome), and close-the-loop evaluations of whether systems trained on these labels improve learning beyond a reasonable control. We illustrate these shifts through case studies of multimodal tutoring data and provide actionable recommendations toward strengthening the evidence base of labeled AIED datasets.

R. Vonlanthen, P. Lodge, J. Barkun et al.

Objectives: To critically assess centralization policies for highly specialized surgeries in Europe and North America and propose recommendations. Background/Methods: Most countries are increasingly forced to maintain quality medicine at a reasonable cost. An all-inclusive perspective, including health care providers, payers, society as a whole and patients, has ubiquitously failed, arguably for different reasons in environments. This special article follows 3 aims: first, analyze health care policies for centralization in different countries, second, analyze how centralization strategies affect patient outcome and other aspects such as medical education and cost, and third, propose recommendations for centralization, which could apply across continents. Results: Conflicting interests have led many countries to compromise for a health care system based on factors beyond best patient-oriented care. Centralization has been a common strategy, but modalities vary greatly among countries with no consensus on the minimal requirement for the number of procedures per center or per surgeon. Most national policies are either partially or not implemented. Data overwhelmingly indicate that concentration of complex care or procedures in specialized centers have positive impacts on quality of care and cost. Countries requiring lower threshold numbers for centralization, however, may cause inappropriate expansion of indications, as hospitals struggle to fulfill the criteria. Centralization requires adjustments in training and credentialing of general and specialized surgeons, and patient education. Conclusion/Recommendations: There is an obvious need in most areas for effective centralization. Unrestrained, purely “market driven” approaches are deleterious to patients and society. Centralization should not be based solely on minimal number of procedures, but rather on the multidisciplinary treatment of complex diseases including well-trained specialists available around the clock. Audited prospective database with monitoring of quality of care and cost are mandatory.

Huixin Gao, Tanya Evans, Anna Fergusson

This scoping review examines the use of student explanation strategies in postsecondary mathematics and statistics education. We analyzed 46 peer-reviewed articles published between 2014 and 2024, categorizing student explanations into three main types: self-explanation, peer explanation and explanation to fictitious others. The review synthesizes the theoretical underpinnings of these strategies, drawing on the retrieval practice hypothesis, generative learning hypothesis, and social presence hypothesis. Our findings indicate that while self-explanation and explaining to fictitious others foster individual cognitive processes enhancing generative thinking, peer explanation have the potential to combine these benefits with collaborative learning. However, explanation to fictitious others have the potential to mitigate some of the negative impacts that may occur in peer explanation, such as more knowledgeable students dominating peer discussions. The efficacy of the methods varies based on implementation, duration, and context. This scoping review contributes to the growing body of literature on generative learning strategies in postsecondary education and provides insights for optimizing the integration of student explanation techniques in mathematics and statistics.

Hasan Abu-Rasheed, Constance Jumbo, Rashed Al Amin et al.

While learning personalization offers great potential for learners, modern practices in higher education require a deeper consideration of domain models and learning contexts, to develop effective personalization algorithms. This paper introduces an innovative approach to higher education curriculum modelling that utilizes large language models (LLMs) for knowledge graph (KG) completion, with the goal of creating personalized learning-path recommendations. Our research focuses on modelling university subjects and linking their topics to corresponding domain models, enabling the integration of learning modules from different faculties and institutions in the student's learning path. Central to our approach is a collaborative process, where LLMs assist human experts in extracting high-quality, fine-grained topics from lecture materials. We develop a domain, curriculum, and user models for university modules and stakeholders. We implement this model to create the KG from two study modules: Embedded Systems and Development of Embedded Systems Using FPGA. The resulting KG structures the curriculum and links it to the domain models. We evaluate our approach through qualitative expert feedback and quantitative graph quality metrics. Domain experts validated the relevance and accuracy of the model, while the graph quality metrics measured the structural properties of our KG. Our results show that the LLM-assisted graph completion approach enhances the ability to connect related courses across disciplines to personalize the learning experience. Expert feedback also showed high acceptance of the proposed collaborative approach for concept extraction and classification.

Т. А. Ivashchenko, Е. S. Dvoynikovaon.ru

In modern society knowledge of Creative Pedagogy ensures high potential of educational activities and achievement of a high level of education quality. The education system is faced with the task of qualitatively preparing a modern graduate for further education and work. Solving this problem requires the search and implementation of new teaching methods, which are a meaningful component of Creative Pedagogy. Scientific works do not fully present a comprehensive analysis of Creative Pedagogy. They primarily reveal only some aspects related to the creativity of a teacher and the principles of organizing a creative environment. The weak theoretical development of the issue under consideration allowed us to formulate the following research problem: what are the essential characteristics of Creative Pedagogy? The purpose of the article is to substantiate theoretical approaches to the definition of “Creative Pedagogy”. Solving the stated goal required the use of theoretical research methods (analysis of scientific literature, synthesis of existing knowledge on the problem under study, generalization). The empirical basis of the study is represented by analytical materials and information resources on the Internet.The results of the research include 1) the concept of “Creative Pedagogy” is considered as an innovative direction of the new education paradigm; 2) the potential of Creative Pedagogy in educational activities has been revealed; 3) the pedagogical conditions for the creative orientation of pedagogical activity have been determined; 4) the basic principles of creating a creative educational environment have been outlined.Key findings: The study extends pedagogical theory toward understanding the content of Creative Pedagogy; the presented basic principles can be used by teachers when organizing a creative educational environment, and the proposed creative teaching methods can be used to stimulate the cognitive activity of students.

Raden Aulia Utami Hidayat, Sri Setyaarini, Gin Gin Gustine et al.

Critical literacy has become an essential approach in education, yet research on its perception and implementation at the primary school level, particularly in EFL contexts, still needs to be completed. This study examines primary school teachers' perceptions of critical literacy in English as a Foreign Language (EFL) classroom in West Java. Using a mixed-methods approach, data were collected from 50 primary school teachers through structured surveys and open-ended questionnaires. Quantitative data were analyzed using descriptive statistics, while qualitative responses were analyzed thematically. Findings indicate a limited understanding of critical literacy, although teachers are strongly willing to incorporate it into their teaching. Key challenges include inadequate professional development, insufficient resources, and rigid curriculum constraints, emphasizing the need for targeted training to enhance students' learning experiences. Limitations include the small sample size and focus on one region. Future research should explore broader contexts and the longitudinal impacts of critical literacy practices. However, it is still expected that the study could give primary school teachers insight into critical literacy as one of the approaches in education.

Slamet Raharjo, Mustika Fitri, Mahmud Yunus et al.

Background and Study Aim. High-intensity interval training (HIIT) has become a popular exercise choice for people who have limited time but aim to maximize their workout results. This study aims to compare the impacts of high-intensity running interval training (HIRIT) and high-intensity progressive resistance training (HIPRT) on oxidative stress biomarkers and antioxidant levels in healthy young males. Material and Methods. The study included 30 healthy male adolescents aged 20–23 years who participated in HIRIT and HIPRT interventions over a four-week period. Data were collected by measuring levels of Malondialdehyde (MDA) and Superoxide Dismutase (SOD) as biomarkers of oxidative stress and antioxidants. These measurements were obtained before and after the intervention using Colorimetric Assay Kits. Data analysis was performed using paired sample t-tests and independent sample t-tests with a significance level set at 5%. Results. The results showed a significant decrease in MDA levels in both high-intensity training interventions. However, SOD levels increased significantly only in the high-intensity running interval training group (p ≤ 0.05). Additionally, comparisons between groups revealed a reduction in MDA levels and an increase in SOD levels (p ≤ 0.05). Conclusions. These findings suggest that both high-intensity running interval training and high-intensity progressive resistance training, conducted over a four-week period, are effective in reducing oxidative stress. Additionally, both types of training increase antioxidant levels in healthy young men. However, high-intensity running interval training proved to be more effective in reducing MDA levels and increasing SOD levels.

Nathan Hagen

We compare the optical science & engineering graduate-level educational environments at two universities in two countries: Utsunomiya University in Japan, and the University of Arizona in the United States. Because the university education systems in the two countries are so different, we also explain how financial resources drive many of these differences and discuss how these impact student and faculty life.

Stefanie Lenzer, Laura Pannullo, Andreas Nehring et al.

Scientific literacy, a central goal of modern science education, should be accessible to all students regardless of their backgrounds. Despite international education reforms focused on diversity, equity and inclusion many teachers struggle to create inclusive science lessons. One reason may be, that creating inclusive science lessons is challenging: it requires teachers to balance the demands of science education with the diverse needs of their students. Therefore, teachers need support in planning inclusive science lessons. To address this issue, members of a German network for inclusive science education (Netzwerk inklusiver naturwissenschaftlicher Unterricht, NinU) have developed the NinU framework. This framework integrates perspectives from both science education and inclusive pedagogy to support teachers in planning and reflecting inclusive science lessons. Originally created as a workshop resource for the NARST 2023 conference, this self-study manual provides 1) an overview of the theory behind the NinU framework, 2) examples and 3) exercises for acknowledging diversity, recognizing barriers and enabling participation using the framework.

Patrick Bassner, Eduard Frankford, Stephan Krusche

Integrating AI-driven tools in higher education is an emerging area with transformative potential. This paper introduces Iris, a chat-based virtual tutor integrated into the interactive learning platform Artemis that offers personalized, context-aware assistance in large-scale educational settings. Iris supports computer science students by guiding them through programming exercises and is designed to act as a tutor in a didactically meaningful way. Its calibrated assistance avoids revealing complete solutions, offering subtle hints or counter-questions to foster independent problem-solving skills. For each question, it issues multiple prompts in a Chain-of-Thought to GPT-3.5-Turbo. The prompts include a tutor role description and examples of meaningful answers through few-shot learning. Iris employs contextual awareness by accessing the problem statement, student code, and automated feedback to provide tailored advice. An empirical evaluation shows that students perceive Iris as effective because it understands their questions, provides relevant support, and contributes to the learning process. While students consider Iris a valuable tool for programming exercises and homework, they also feel confident solving programming tasks in computer-based exams without Iris. The findings underscore students' appreciation for Iris' immediate and personalized support, though students predominantly view it as a complement to, rather than a replacement for, human tutors. Nevertheless, Iris creates a space for students to ask questions without being judged by others.

Huihui Liu, Yao-yao Song, Guo-liang Yang

Abstract Cross-efficiency evaluation in data envelopment analysis (DEA) is a useful tool in evaluating the performance of decision-making units (DMUs). It is generally assumed that decision makers (DMs) are completely rational in common cross-efficiency evaluation models, which fail to consider the DM's risk attitude that plays an important role in the evaluation process. To fill this gap, we investigate the cross-efficiency evaluation in DEA based on prospect theory. First, we introduce a prospect value of the DMU to capture the non-rational psychological aspects of a DM under risk. Second, based on the prospect value, we propose a new cross-efficiency model termed the prospect cross-efficiency (PCE) model. Particularly, some existing cross-efficiency evaluation models can be deemed as the special cases of the PCE model with suitable adjustments of the parameters. Furthermore, this paper provides an empirical example to evaluate cross-efficiency with several selected universities directly managed by the Ministry of Education of China to illustrate the effectiveness of the PCE model in ranking DMUs.

Lilik Mudloyati Choiriyah, Lathifah Hanum

Topics of wedding plans can trigger disagreements in communication between young adults children and parents. Communication problems with parents can reduce the quality of relationships and increase stress on individuals, particularly if the issue has escalated into conflict. The quality of the relationship between young adults and their parents is significant to note in maintaining mental health by reducing stress. This study aims to examine the effectiveness of intergenerational communication-based intervention in reducing stress in young adults who experience communication problems with their parents regarding marriage plans. We conducted group intervention for young adults aged 18 to 29 with communication problems with their parents regarding marriage. Changes in the way of communication were assessed with the Global Perceptions of Intergenerational Communication Scale (GPIC) score, stress level with the Perceived Stress Scale (PSS) score, and life satisfaction with the Satisfaction with Life Scale (SWLS) score given before and after the intervention. Results found that emotion regulation, perspective-taking, and assertive communication taught through group intervention effectively help improve intergenerational communication skills, reduce stress levels, and increase life satisfaction in young adults. Considering the contribution of conflict with parents to stress levels, interventions based on intergenerational communication are considered worthwhile in overcoming psychological distress.

Josephine C. Meyer, Gina Passante, Bethany R. Wilcox

Driven in large part by the National Quantum Initiative Act of 2018, quantum information science (QIS) coursework and degree programs are rapidly spreading across US institutions. Yet prior work suggests that access to quantum workforce education is unequally distributed, disproportionately benefiting students at private research-focused institutions whose student bodies are unrepresentative of US higher education as a whole. We use regression analysis to analyze the distribution of QIS coursework across 456 institutions of higher learning as of fall 2022, identifying statistically significant disparities across institutions in particular along the axes of institution classification, funding, and geographic distribution suggesting today's QIS education programs are largely failing to reach low-income and rural students. We also conduct a brief analysis of the distribution of emerging dedicated QIS degree programs, discovering much the same trends. We conclude with a discussion of implications for educators, policymakers, and education researchers including specific policy recommendations to direct investments in QIS education to schools serving low-income and rural students, leverage existing grassroots diversity and inclusion initiatives that have arisen within the quantum community, and update and modernize procedures for collecting QIS educational data to better track these trends.

Zhengliang Liu, Xinyu He, Lei Liu et al.

This study aims at improving the performance of scoring student responses in science education automatically. BERT-based language models have shown significant superiority over traditional NLP models in various language-related tasks. However, science writing of students, including argumentation and explanation, is domain-specific. In addition, the language used by students is different from the language in journals and Wikipedia, which are training sources of BERT and its existing variants. All these suggest that a domain-specific model pre-trained using science education data may improve model performance. However, the ideal type of data to contextualize pre-trained language model and improve the performance in automatically scoring student written responses remains unclear. Therefore, we employ different data in this study to contextualize both BERT and SciBERT models and compare their performance on automatic scoring of assessment tasks for scientific argumentation. We use three datasets to pre-train the model: 1) journal articles in science education, 2) a large dataset of students' written responses (sample size over 50,000), and 3) a small dataset of students' written responses of scientific argumentation tasks. Our experimental results show that in-domain training corpora constructed from science questions and responses improve language model performance on a wide variety of downstream tasks. Our study confirms the effectiveness of continual pre-training on domain-specific data in the education domain and demonstrates a generalizable strategy for automating science education tasks with high accuracy. We plan to release our data and SciEdBERT models for public use and community engagement.

Graham Chamberlain, Ronish Gupta, Anna-Theresa Lobos

In-hospital pediatric cardiopulmonary arrest is rare. With more than 50% of patients not surviving to discharge following cardiopulmonary arrest, it is important that health-care providers (HCPs) respond appropriately to deteriorating patients. Our study evaluated the performance of basic life support skills using non-acute HCPs during pediatric inpatient resuscitation events. We conducted a retrospective chart review of all code blue team (CBT) activations in non-acute care areas of a tertiary care children’s hospital from 2008 to 2017. The main outcomes were frequency of life support algorithmic assessments and interventions (critical actions) performed by non-acute HCPs prior to the arrival of CBT. CBT activation and outcome data were summarized descriptively. Logistic regression was used to assess for an association of outcomes with the presence of established leadership. A total of 60 CBT activations were retrieved, 48 of which had data available on isolated non-acute HCP performance. Most children (93%) survived to discharge. Critical action performance review revealed that an airway, breathing and pulse assessment was documented to have occurred in 33%, 69% and 29% of cases, respectively. A full primary assessment was documented in 6% of cases. The presence of established leadership was associated with the performance of a partial ABC assessment. Our results suggest that resuscitation performance of pediatric inpatient non-acute HCPs often does not adhere to standard life support guidelines. These results highlight the need to reconsider the current approaches used for non-acute HCP resuscitation training.

Farida Farida, Nanang Supriadi, Siska Andriani et al.

Metaphorical thinking is important in improving the formation and discovery of learning ideas in the 21st-century. However, the metaphorical thinking of Indonesian students is below the international average in terms of cognitive process, according to PISA 2018. This study aims to identify differences in the ability of students' metaphorical thinking in learning STEM and Computer Science (STEM-CS). This research employed the experimental design with a simple random sampling technique to determine the sample. The population of this study was 280 junior high school students in Bandar Lampung, Indonesia. The data collection technique has been tested to see the improvement of metaphorical thinking. Hypothetical testing has been used by one-way ANOVA with a meaningful level of 5%. The results found that the average class value applied to the STEM-CS training model was 88.00, which was higher compared to the STEM class with an average score of 86.00 and the control class with an average score of 73.00. It is concluded that the STEM-CS model can be used as an alternative solution for learning in the industrial era 4.0.