Hasil untuk "Education"

K. Sugand, P. Abrahams, A. Khurana

D. Berliner, B. Biddle

D. Dolmans, W. D. de Grave, I. Wolfhagen et al.

Yizhou Qian, James Lehman

Ruth A

Bhagyesh Sapkale, Sonali Choudhari

Noma, also known as cancrum oris, is a rapidly progressive gangrenous infection that affects the oral and facial tissues of malnourished children in impoverished regions, particularly in sub-Saharan Africa. The disease typically begins as acute necrotising gingivitis and rapidly progresses to extensive tissue destruction, facial disfigurement, and high mortality if left untreated. This narrative review explores the clinical progression of Noma, its World Health Orgnisation (WHO) classification and simplified staging systems, the pathogenesis involving malnutrition, immunosuppression, and microbial dysbiosis, as well as the polymicrobial nature of the disease. Diagnosis of Noma is primarily clinical, although emerging microbiome-based diagnostic techniques show promise for early detection. Management requires a comprehensive approach that combines early antibiotic therapy, nutritional rehabilitation, wound care, and delayed reconstructive surgery. Preventive strategies include adequate vaccination (particularly against measles), community-based oral health education, and improvements in Water, Sanitation, and Hygiene (WASH) initiatives. Strengthening local healthcare infrastructure and establishing sustainable surgical programmes are essential for long-term control and rehabilitation of Noma. Addressing the socio-economic determinants of Noma through holistic public health efforts remains crucial for reducing its burden and achieving global eradication.

Xiaoshan Yu, Shangshang Yang, Ziwen Wang et al.

In recent years, trustworthiness has garnered increasing attention and exploration in the field of intelligent education, due to the inherent sensitivity of educational scenarios, such as involving minors and vulnerable groups, highly personalized learning data, and high-stakes educational outcomes. However, existing research either focuses on task-specific trustworthy methods without a holistic view of trustworthy intelligent education, or provides survey-level discussions that remain high-level and fragmented, lacking a clear and systematic categorization. To address these limitations, in this paper, we present a systematic and structured review of trustworthy intelligent education. Specifically, We first organize intelligent education into five representative task categories: learner ability assessment, learning resource recommendation, learning analytics, educational content understanding, and instructional assistance. Building on this task landscape, we review existing studies from five trustworthiness perspectives, including safety and privacy, robustness, fairness, explainability, and sustainability, and summarize and categorize the research methodologies and solution strategies therein. Finally, we summarize key challenges and discuss future research directions. This survey aims to provide a coherent reference framework and facilitate a clearer understanding of trustworthiness in intelligent education.

Woojin Kim, Changkwon Lee, Hyeoncheol Kim

Using Artificial Intelligence to improve teaching and learning benefits greater adaptivity and scalability in education. Knowledge Tracing (KT) is recognized for student modeling task due to its superior performance and application potential in education. To this end, we conceptualize and investigate counterfactual explanation as the connection from XAI for KT to education. Counterfactual explanations offer actionable recourse, are inherently causal and local, and easy for educational stakeholders to understand who are often non-experts. We propose KTCF, a counterfactual explanation generation method for KT that accounts for knowledge concept relationships, and a post-processing scheme that converts a counterfactual explanation into a sequence of educational instructions. We experiment on a large-scale educational dataset and show our KTCF method achieves superior and robust performance over existing methods, with improvements ranging from 5.7% to 34% across metrics. Additionally, we provide a qualitative evaluation of our post-processing scheme, demonstrating that the resulting educational instructions help in reducing large study burden. We show that counterfactuals have the potential to advance the responsible and practical use of AI in education. Future works on XAI for KT may benefit from educationally grounded conceptualization and developing stakeholder-centered methods.

Irzani Andi Abdulrahman, Satria Akbar Bachtiar

This study aims to evaluate the implementation of legal protection for coral reef ecosystems in Sawu Sea National Park, Kupang Regency, East Nusa Tenggara. The research employs an empirical legal approach using a descriptive qualitative method with a case study design, focusing on how conservation laws and policies are implemented and function in practice. Data were collected through in-depth interviews with area management authorities, law enforcement officials, local government representatives, civil society organizations, and coastal communities, complemented by field observations and analysis of relevant legal and policy documents. The findings indicate that although regulatory frameworks and conservation policies are formally in place, their implementation remains ineffective. Limited supervision capacity, reflected in the insufficient number of officers, inadequate patrol facilities, and suboptimal use of marine monitoring technology, constitutes a major obstacle. Complex geographical conditions and weak inter-agency coordination further undermine law enforcement, resulting in sanctions that fail to produce a deterrent effect. Community participation in conservation efforts is also relatively low due to high dependence on marine resources, limited awareness of the impacts of overexploitation, unequal distribution of tourism benefits, and inadequate environmental education and legal outreach. Additionally, external factors such as climate change, including coral bleaching and extreme weather events, exacerbate reef degradation. This study recommends strengthening surveillance infrastructure and monitoring technology, enhancing the capacity and coordination of law enforcement institutions, promoting participatory and community-based conservation approaches, and integrating climate change adaptation strategies into coral reef protection policies to ensure ecological sustainability and more equitable socio-economic benefits for coastal communities.

Woojin Kim, Hyeoncheol Kim

As machine learning models are increasingly used in educational settings, from detecting at-risk students to predicting student performance, algorithmic bias and its potential impacts on students raise critical concerns about algorithmic fairness. Although group fairness is widely explored in education, works on individual fairness in a causal context are understudied, especially on counterfactual fairness. This paper explores the notion of counterfactual fairness for educational data by conducting counterfactual fairness analysis of machine learning models on benchmark educational datasets. We demonstrate that counterfactual fairness provides meaningful insight into the causality of sensitive attributes and causal-based individual fairness in education.

Sofia C. Latini Gonçalves, Rodrigo Moreira, Larissa F. Rodrigues Moreira et al.

Programming, which is both economically significant and mentally stimulating, has been found to benefit the aging brain and to enhance cognitive function at various educational levels. Despite its advantages, challenges persist in standardizing and implementing programming education effectively across both the higher and secondary education levels in Brazil. To shed light on these issues, we carried out a systematic review of programming teaching methods in the Brazilian context, examining gaps, common techniques, approaches, and action opportunities in programming education. Our findings provide valuable recommendations for educational policymakers and educators to develop effective and updated national policies to teach programming.

Yingfei Xiong, Zhenjiang Hu, Haiyan Zhao et al.

Art : Art in which the subject matter has been simplified or distorted to the point that it may or may not be easily descended. Acrylic: Resin that, when mixed with water and pigment, forms an inorganic and quick drying medium. Acrylic paint therefore is a fast drying synthetic paint made from acrylic resin. Action Painting: The technique of dripping and splashing paint onto canvases stretched on the floor Aesthetic: Pertaining to the appreciation of the beautiful as opposed to the functional or utilitarian, and, by extention, to the appreciation of any form of art. Aesthetic value: The impact of a work of art on our senses, intellect, and emotions. Analogous colours:Pairs of colour such as yellow and orange, that are adjudscent to each other on the colour wheel. Acqatint : A print making process that includes etching and that permits broad areas of black and gray tones. Air brush: Atomizer operated by compressed air used for spraying on paint. Art Deco: A popular art and design style of the 1920s and 30s, and characterized by its intergration of organic and geometric forms. Art: The special expression of ideas, feelings and values in visual forms. Art criticism: A special concentrated way of looking at a piece of art with a purpose to recieve maximum enjoyment and meaning from it. Abstract Expressionism: A twentieth-century painting style in which artists applied paint freely to huge canvases in an effort to show feelings and emotions rather than realistic subject matter.Expressionism: A twentieth-century painting style in which artists applied paint freely to huge canvases in an effort to show feelings and emotions rather than realistic subject matter.

Z. Griliches, W. Mason

Oscar Jerez, Nicolás Alfredo Lavados Toro, Emilia Winkler

Este artículo entrega una sistematización de la metodología de la Investigación Basada en Casos (IBC) en el marco de la Educación Médica y en Ciencias de la Salud (ECS). La IBC es una metodología que enfatiza el estudio en profundidad de casos individuales con el fin de obtener una comprensión detallada y contextualizada de los fenómenos investigados, lo que la convierte en una herramienta de valor epistémico para la ECS. Objetivo: Identificar los fundamentos de la IBC, diseño, criterios de rigor y aspectos específicos de su comunicación científica. Métodos: A través de una revisión, síntesis de la literatura y buenas prácticas, se ofrece una orientación a los investigadores sobre cómo implementar la IBC. Conclusión: La IBC es una metodología valiosa en la educación en médica y de ciencias de la salud, pero su implementación requiere un diseño riguroso, criterios de calidad establecidos y una comunicación científica efectiva.

Kaska Porayska-Pomsta, Wayne Holmes, Selena Nemorin

The transition of Artificial Intelligence (AI) from a lab-based science to live human contexts brings into sharp focus many historic, socio-cultural biases, inequalities, and moral dilemmas. Many questions that have been raised regarding the broader ethics of AI are also relevant for AI in Education (AIED). AIED raises further specific challenges related to the impact of its technologies on users, how such technologies might be used to reinforce or alter the way that we learn and teach, and what we, as a society and individuals, value as outcomes of education. This chapter discusses key ethical dimensions of AI and contextualises them within AIED design and engineering practices to draw connections between the AIED systems we build, the questions about human learning and development we ask, the ethics of the pedagogies we use, and the considerations of values that we promote in and through AIED within a wider socio-technical system.

Yunshi Lan, Xinyuan Li, Hanyue Du et al.

Natural Language Processing (NLP) aims to analyze text or speech via techniques in the computer science field. It serves applications in the domains of healthcare, commerce, education, and so on. Particularly, NLP has been widely applied to the education domain and its applications have enormous potential to help teaching and learning. In this survey, we review recent advances in NLP with a focus on solving problems relevant to the education domain. In detail, we begin with introducing the related background and the real-world scenarios in education to which NLP techniques could contribute. Then, we present a taxonomy of NLP in the education domain and highlight typical NLP applications including question answering, question construction, automated assessment, and error correction. Next, we illustrate the task definition, challenges, and corresponding cutting-edge techniques based on the above taxonomy. In particular, LLM-involved methods are included for discussion due to the wide usage of LLMs in diverse NLP applications. After that, we showcase some off-the-shelf demonstrations in this domain, which are designed for educators or researchers. At last, we conclude with five promising directions for future research, including generalization over subjects and languages, deployed LLM-based systems for education, adaptive learning for teaching and learning, interpretability for education, and ethical consideration of NLP techniques. We organize all relevant datasets and papers in the open-available Github Link for better review https://github.com/LiXinyuan1015/NLP-for-Education.

Peder Thalén

The binary division between ‘religion’ and ‘secular’ as an analytical tool has long been criticised within the research field of ‘critical religion’ in religious studies. There has also been a parallel critique in the academic discussion about post-secularity. Recently, sociologists have picked up and deepened this criticism, as expressed in Mitsutoshi Horii’s book ‘<i>Religion</i>’ <i>and</i> ‘<i>Secular</i>’ <i>Categories in Sociology: Decolonizing the Modern Myth</i> (2021). Based on a critical processing of Horii’s application to sociology, the aim of this article is to discuss the challenges for non-confessional religious education (RE) that the ongoing dismantling of this binary division entails. In particular, it looks at how a non-confessional RE could be designed that transcends the binary division and how powerful knowledge could be understood in a non-binary context.

Sung-Un Park, Hyunkyun Ahn, Wi-Young So

The coronavirus disease 2019 (COVID-19) pandemic has prompted the implementation of social distancing policies worldwide, limiting participation in exercise and substantially impacting health behaviors. In accordance with the theory of planned behavior (TPB), the present study aimed to develop a model for predicting the intent to participate in exercise and engage in health behaviors among Korean men using the perception of COVID-19 risk as an exogenous variable. We analyzed data obtained from 374 Korean men who had completed a 32-item, online questionnaire. Structural equation modeling was performed to evaluate the effect of attitudes, subjective norms, and perceived behavioral control (PBC) on the intention to participate in exercise and health behaviors using COVID-19 risk perception as an antecedent variable. COVID-19 risk perception exerted significant negative effects on the attitude toward exercise participation (β = −0.857, p < 0.001), subjective norms associated with exercise participation (β = −0.862, p < 0.001), and PBC related to exercise (β = −0.738, p < 0.001). In addition, both attitude (β = 0.213, p < 0.001) and subjective norms (β = 0.168, p = 0.001) exerted significant effects on the intention to participate in exercise. PBC also exerted significant effects on the intention to participate in exercise (β = 0.580, p < 0.001) and health behaviors (β = 0.461, p < 0.001). Lastly, the intention to participate in exercise exerted a significant effect on health behaviors (β = 0.400, p < 0.001). The data indicated that, among TPB variables, PBC exerted the greatest influence on the intention to participate in exercise and had a significant effect on engagement in health behaviors. The current findings support TPB as an important theoretical model for predicting the intention to participate in exercise and patterns of health behavior among Korean men during the COVID-19 pandemic. Our study also highlights the importance of addressing PBC when designing interventions to promote exercise participation and health behaviors among Korean men.

Rafael Ferreira Mello, Elyda Freitas, Filipe Dwan Pereira et al.

With the emergence of generative artificial intelligence, an increasing number of individuals and organizations have begun exploring its potential to enhance productivity and improve product quality across various sectors. The field of education is no exception. However, it is vital to notice that artificial intelligence adoption in education dates back to the 1960s. In light of this historical context, this white paper serves as the inaugural piece in a four-part series that elucidates the role of AI in education. The series delves into topics such as its potential, successful applications, limitations, ethical considerations, and future trends. This initial article provides a comprehensive overview of the field, highlighting the recent developments within the generative artificial intelligence sphere.

Ming Li, Ariunaa Enkhtur, Beverley Anne Yamamoto et al.

Purpose:Generative Artificial Intelligence (GAI) models, such as ChatGPT, may inherit or amplify societal biases due to their training on extensive datasets. With the increasing usage of GAI by students, faculty, and staff in higher education institutions (HEIs), it is urgent to examine the ethical issues and potential biases associated with these technologies. Design/Approach/Methods:This scoping review aims to elucidate how biases related to GAI in HEIs have been researched and discussed in recent academic publications. We categorized the potential societal biases that GAI might cause in the field of higher education. Our review includes articles written in English, Chinese, and Japanese across four main databases, focusing on GAI usage in higher education and bias. Findings:Our findings reveal that while there is meaningful scholarly discussion around bias and discrimination concerning LLMs in the AI field, most articles addressing higher education approach the issue superficially. Few articles identify specific types of bias under different circumstances, and there is a notable lack of empirical research. Most papers in our review focus primarily on educational and research fields related to medicine and engineering, with some addressing English education. However, there is almost no discussion regarding the humanities and social sciences. Additionally, a significant portion of the current discourse is in English and primarily addresses English-speaking contexts. Originality/Value:To the best of our knowledge, our study is the first to summarize the potential societal biases in higher education. This review highlights the need for more in-depth studies and empirical work to understand the specific biases that GAI might introduce or amplify in educational settings, guiding the development of more ethical AI applications in higher education.