Hasil untuk "Education (General)"

Simin Yang, Hongli Zhang, Xiaoyan Sun et al.

Hepatic stellate cells (HSCs) trans-differentiation into myofibroblasts is central to liver fibrosis. Integrated stress response (ISR) signaling, including metabolic stress, plays a critical role in this process. However, the precise role of ISR signaling in HSCs activation-whether detrimental or protective-remains unclear. Here we identified that the noncanonical cap-binding protein EIF3d-mediated ATF4 expression is significantly upregulated in HSCs from both patients and mouse models of fibrotic livers, with its levels positively correlating with the degree of fibrosis. EIF3d-ATF4 signaling was induced by TGFβ1 in HSCs and was demonstrated to be both necessary and sufficient for promoting HSC survival, proliferation, activation, and extracellular matrix (ECM) production. Furthermore, genetic and pharmacological inhibition of EIF3d-ATF4 effectively prevented TGFβ1-induced HSC activation by suppressing mitochondrial activity and glycolysis. Mechanistically, EIF3d-ATF4 overexpression drove ATF4-dependent S100P transcription, which facilitated metabolic reprogramming and upregulated fibrogenic markers. This EIF3d-ATF4-S100P axis promoted liver fibrosis by activating JNK and NLRP3 signaling in HSCs, thereby inducing HSC activation and conferring resistance to apoptosis. Importantly, mice with HSC-specific ATF4 deletion or treated with our innovative ISR antagonist, ERMT1, were protected from three distinct mouse fibrotic models. These findings underscore the role of the EIF3d-ATF4-S100P signaling axis in liver fibrosis progression and HSC activation, presenting it as a promising therapeutic target for managing liver fibrosis and cirrhosis.

Rusdian Rifai, Turmudi, Jarnawi Afgani Dahlan et al.

This study aims to evaluate the extent to which the open-ended approach affects the achievement of students' mathematical understanding ability in vocational high schools (SMK). The method used was a quasi-experiment with a pretest-posttest control group design, in which two groups of students (experimental group and control group) were compared based on test results before and after treatment. The research sample consisted of 72 students who were selected by intact group (based on the class that had been formed) to minimize disruption to teaching and learning activities at school. This sampling was done by considering the real conditions in the field so as not to disrupt the existing learning schedule and class structure. The research data was obtained through a description test consisting of three questions, with a maximum score of 12 for each question. Data analysis was conducted in two stages: first, a normality test to check data distribution, and second, a mean difference test using the Mann-Whitney U test because the data were not normally distributed. The results showed that the group of students taught with the open-ended approach had better mathematical understanding achievement than the group that received direct learning. This finding indicates that the open-ended approach is effective in promoting mathematical conceptual understanding through independent exploration.

Razieh Hajizadeh, Hashem Piri, Nader Naserpour et al.

Fatigue decreases muscle strength and functional capacity, disrupting neuromuscular coordination by impairing load control. This negatively impacts the kinetics and kinematics of the ankle, knee, and hip joints, resulting in reduced performance and an increased risk of injury, particularly to the anterior cruciate ligament (ACL). This study aimed to compare the effects of fatigue protocols for lower extremity and core muscles on landing mechanics and performance of female athletes. This study used a cross-sectional, comparative, pretest-posttest design with a control group. A total of 105 female athletes, aged 11 to 49, were selected via convenience and purposive sampling. Participants were divided into three groups: core muscle fatigue, lower extremity muscle fatigue, and a control group. Data were gathered using the Landing Error Scoring System (LESS), Y-Balance, and 45-degree trunk flexion tests. Data were analyzed using descriptive statistics, Shapiro-Wilk test, Levene's test, one-way ANCOVA, and the Bonferroni post hoc test. A P of 0.05 or lower was considered statistically significant. ANCOVA results showed significant differences among the groups for the LESS (P=0.001) and 45 ° trunk flexion test (P=0.001). There was no significant difference between the two experimental groups regarding the LESS (P=1.00). However, a significant difference was observed between the two experimental groups in the trunk flexion test (P=0.001). Fatigue had a greater effect size on landing mechanics (ηp²=0.209) than on the trunk flexion test (ηp²=0.143). However, no significant difference was observed between the groups regarding the Y-Balance Test (P=0.996). The study revealed that fatigue protocols targeting lower extremity and core muscles had a negative impact on kinematic parameters associated with ACL injuries during jump-landing in female athletes. Additionally, core muscle fatigue significantly impacted the 45º trunk flexion test, while lower extremity muscle fatigue had no significant effect on it.

Lisa Bornscheuer, Evelina Landstedt, Karl Gauffin et al.

Abstract Background Childhood adversity places individuals in a vulnerable position, resulting in potentially enduring disadvantage across life domains like health and work. Studying the manifestation of this disadvantage is crucial for understanding which resources society can provide to mitigate or prevent it, which makes this subject a fundamental public health concern. This study investigated whether disadvantage patterns after childhood adversity differ by gender and educational level, using out-of-home care as proxy for early adversity. Methods We used register data from a 1953 Swedish birth cohort. Distinct profiles of socioeconomic and health disadvantage in individuals with out-of-home care experience were identified using group-based multi-trajectory modelling. Multinomial logistic regression was then used to determine whether gender and education, individually or in interaction with each other, predict group membership. Results In the population without history of out-of-home care, adulthood disadvantage was highly gendered, with women being more likely to experience disadvantage related to unemployment and poor health, while criminality and substance misuse was more common among men. History of out-of-home care was associated with a general increase in adulthood disadvantage, but the gender differences were largely absent. Women in this group were however less likely than men to experience disadvantage across multiple life domains (complex disadvantage OR = 0.56, p = 0.046; unemployment-related disadvantage OR = 0.51, p = 0.005). Higher level of education was associated with reduced likelihood of membership in the group marked by disabling health disadvantage (OR = 0.55, p = 0.002) and complex disadvantage (OR = 0.37, p = 0.001). An interaction term between gender and education was not significant. Conclusions Adulthood disadvantage was more common in the group with history of out-of-home care. The gender differences in disadvantage present in the full cohort were largely attenuated among individuals with out-of-home care history. We showed that using administrative data on outcomes across multiple life domains can provide rich descriptions of adult experiences after childhood adversity. Future research could examine gender differences in mechanisms translating into resilient or vulnerable trajectories, including the protective potential of education in relation to specific disadvantage patterns.

Qian Huang, Thijs Willems

As generative AI (Gen-AI) tools become more prevalent in education, there is a growing need to understand how educators, not just students, can actively shape their design and use. This study investigates how two instructors integrated four custom GPT tools into a Masters-level Qualitative Research Methods course for Urban Planning Policy students. Addressing two key gaps: the dominant framing of students as passive AI users, and the limited use of AI in qualitative methods education. The study explores how Gen-AI can support disciplinary learning when aligned with pedagogical intent. Drawing on the Technological Pedagogical Content Knowledge (TPACK) framework and action research methodology, the instructors designed GPTs to scaffold tasks such as research question formulation, interview practice, fieldnote analysis, and design thinking. Thematic analysis of student reflections, AI chat logs, and final assignments revealed that the tools enhanced student reflexivity, improved interview techniques, and supported structured analytic thinking. However, students also expressed concerns about cognitive overload, reduced immersion in data, and the formulaic nature of AI responses. The study offers three key insights: AI can be a powerful scaffold for active learning when paired with human facilitation; custom GPTs can serve as cognitive partners in iterative research practice; and educator-led design is critical to pedagogically meaningful AI integration. This research contributes to emerging scholarship on AI in higher education by demonstrating how empowering educators to design custom tools can promote more reflective, responsible, and collaborative learning with AI.

Ruiming Min, Minghao Liu

With the advancement of modern medicine and the development of technologies such as MRI, CT, and cellular analysis, it has become increasingly critical for clinicians to accurately interpret various diagnostic images. However, modern medical education often faces challenges due to limited access to high-quality teaching materials, stemming from privacy concerns and a shortage of educational resources (Balogh et al., 2015). In this context, image data generated by machine learning models, particularly generative models, presents a promising solution. These models can create diverse and comparable imaging datasets without compromising patient privacy, thereby supporting modern medical education. In this study, we explore the use of convolutional neural networks (CNNs) and CycleGAN (Zhu et al., 2017) for generating synthetic medical images. The source code is available at https://github.com/mliuby/COMP4211-Project.

Mike Zhang, Amalie Pernille Dilling, Léon Gondelman et al.

Providing high-quality feedback on student assignments is crucial for student success, but it is heavily limited by time and budgetary constraints. In this work, we introduce Synthetic Educational Feedback Loops (SEFL), a synthetic data framework designed to generate data that resembles immediate, on-demand feedback at scale without relying on extensive, real-world student assignments and teacher feedback. To obtain this type of data, two large language models (LLMs) operate in a teacher-student role to simulate assignment completion and formative feedback, generating 19.8K synthetic pairs of student work and corresponding critiques and actionable improvements from a teacher. With this data, we fine-tune smaller, more computationally efficient LLMs on these synthetic pairs, enabling them to replicate key features of high-quality, goal-oriented feedback. Through comprehensive evaluations with three LLM judges and three human experts, across a subset of 900 outputs, we demonstrate that SEFL-tuned models outperform both their untuned counterparts and an existing baseline in terms of feedback quality. The potential for societal impact is reinforced by extensive qualitative comments and ratings from human stakeholders -- both students and higher education instructors. SEFL has the potential to transform feedback processes for higher education and beyond.

Victoria Jackson, Susannah Liu, Andre van der Hoek

With the rapid adoption of Generative AI (GenAI) tools, software engineering educators have grappled with how best to incorporate them into the classroom. While some research discusses the use of GenAI in the context of learning to code, there is little research that explores the use of GenAI in the classroom for other areas of software development. This paper provides an experience report on introducing GenAI into an undergraduate software design class. Students were required to use GenAI (in the form of ChatGPT) to help complete a team-based assignment. The data collected consisted of the ChatGPT conversation logs and students' reflections on using ChatGPT for the assignment. Subsequently, qualitative analysis was undertaken on the data. Students identified numerous ways ChatGPT helped them in their design process while recognizing the need to critique the response before incorporating it into their design. At the same time, we identified several key lessons for educators in how to deploy GenAI in a software design class effectively. Based on our experience, we believe students can benefit from using GenAI in software design education as it helps them design and learn about the strengths and weaknesses of GenAI.

Mika Setälä, Ville Heilala, Pieta Sikström et al.

This study investigated the students' perceptions of using Generative Artificial Intelligence (GenAI) in upper-secondary mathematics education. Data was collected from Finnish high school students to represent how key constructs of the Technology Acceptance Model (Perceived Usefulness, Perceived Ease of Use, Perceived Enjoyment, and Intention to Use) influence the adoption of AI tools. First, a structural equation model for a comparative study with a prior study was constructed and analyzed. Then, an extended model with the additional construct of Compatibility, which represents the alignment of AI tools with students' educational experiences and needs, was proposed and analyzed. The results demonstrated a strong influence of perceived usefulness on the intention to use GenAI, emphasizing the statistically significant role of perceived enjoyment in determining perceived usefulness and ease of use. The inclusion of compatibility improved the model's explanatory power, particularly in predicting perceived usefulness. This study contributes to a deeper understanding of how AI tools can be integrated into mathematics education and highlights key differences between the Finnish educational context and previous studies based on structural equation modeling.

Nurul Aeni, Aulia Farkhan Habibi

The ability to understand mathematics is a fundamental aspect that must be possessed by students every student, because having this ability can help students to master other mathematical concepts. However, the fact is that many students have comprehension abilities low mathematics. One of the factors that causes low mathematical understanding abilities students is the application of a learning model that does not provide students with opportunities to play an active role during learning. This has an impact on students' difficulties in learning and understand mathematical material. So, a solution is needed to overcome this, one of which is is to apply the STAD type cooperative learning model with the ARIAS approach. This research aims to determine the mathematical understanding abilities of class VIII students function material by applying the STAD type cooperative learning model with approach ARIAS. The research population was all students of class VIII MTs Ma'arif NU 1 Sokaraja, with Using convenience sampling techniques, the research sample was obtained, namely class VIII A students as the experimental class and class VIII B students as the control class, totaling 53 students. Type The research used is quantitative research with experimental and design methods pre-test and post-test control group. Data collection techniques were carried out using the observation method and tests in the form of pre-test and post-test. After the data is collected, the data is analyzed using t test for two independent samples, with prerequisite tests in the form of a normality test and a homogeneity test. Based on analysis using the t test for two independent samples obtained a significance value in the post-test data of 0.000 > 0.05 which indicates there is a significant difference between the average value of the experimental class and control class. Thus, it can be concluded that the STAD type cooperative learning model The ARIAS approach has an effect on increasing mathematical understanding abilities class VIII students. Keywords: Mathematical Understanding Ability; ARIAS Models; STAD model

LOMPO Miyemba

Le Burkina Faso, à l’image d’autres pays de sahel traverse une crise sécuritaire et humanitaire sans précédent provoquée par des attaques de groupes terroristes et autres conflits exposant les femmes et les enfants à des multiples risques et dangers. S’inscrivant dans une démarche mixte, le présent article vise à analyser l’impact de la crise sur le quotidien de la femme déplacée interne. Les résultats de la recherche montrent que les déplacements de population ont renforcé le patriarcat et les violences basées sur le genre. La crise a engendré des stratégies d’adaptation en termes de réorganisation du travail dans les ménages.

Matthew Nyaaba, Alyson Wright, Gyu Lim Choi

This study examines how Generative Artificial Intelligence reproduces global power hierarchies in education and proposes a framework to address resulting inequities. Using a critical qualitative design, the study conducted zero-shot prompt testing with two leading systems, ChatGPT-4 Turbo and Gemini 1.5, and collected real-time outputs from Global North and South contexts. A critical interpretive analysis traced textual, visual, and structural patterns that revealed forms of digital neocolonialism and their implications for educational equity. Findings show six ways in which GenAI can reinforce Western dominance. Western curriculum assumptions appeared when Gemini listed the same four seasons for the United States and Ghana, reflecting Western climatology and overlooking regional knowledge systems. Other patterns included cultural stereotyping in imagery, Western-centered examples in instructional outputs, limited support for Indigenous and local languages, underrepresentation of non-Western identities in visuals, and access barriers linked to subscription-based models. These patterns demonstrate how GenAI can reproduce inequities even as it introduces new educational opportunities. In response, the study proposes a dual-pathway mitigation model. The Inclusive AI Design pathway includes three components: liberatory design methods that center non-Western epistemologies, anticipatory approaches to reduce representational harm, and decentralized GenAI hubs that support local participation and data sovereignty. The pedagogical pathway, human-centric prompt engineering, equips educators to contextualize prompts and critically engage with outputs. Together, these pathways position GenAI as a tool that can support more equitable and culturally responsive education.

Florencia Clara Mazzola

El Observatorio Federal de la Educación Sexual Integral (OFESI) introdujo dos rasgos novedosos para la efectiva implementación del Programa de Educación Sexual Integral (sancionado en 2006). Por un lado, la incorporación de la perspectiva de género y, por otro, la participación en las decisiones de la política educativa a estudiantes y docentes. En este artículo, abordaremos este último rasgo a partir del análisis de las responsabilidades estatales debatidas en el Congreso Nacional Argentino durante el proceso de la planificación de la Ley de Educación Sexual Integral 26.150/06, y de aquellas asumidas con la creación del OFESI para efectivizar la implementación de la ley, el 23 de octubre de 2020. Este organismo estatal, tiene como objetivo la evaluación e identificación de los obstáculos en la implementación del Programa de Educación Sexual Integral en cada una de las jurisdicciones de Argentina, para promover recomendaciones y estrategias de acción en cada localidad.

Melissa Dancy, Apriel K. Hodari

Abstract Background We present an analysis of interviews with 27 self-identified progressive white-male physics faculty and graduate students discussing race and gender in physics. White cis men dominate most STEM fields and are particularly overrepresented in positions of status and influence (i.e., full professors, chairs, deans, etc.), positioning them as a potentially powerful demographic for enacting systemic reform. Despite their proclaimed outrage at and interest in addressing inequity, they frequently engage in patterns of belief, speech and (in)action that ultimately support the status quo of white male privilege in opposition to their intentions. Results The white male physicists we interviewed used numerous discourses which support racist and sexist norms and position them as powerless to disrupt their own privilege. We present and discuss three overarching themes, seen in our data, demonstrating how highly educated, well-intentioned people of privilege maintain their power and privilege despite their own intentions: (1) denying inequity is physically near them; (2) locating causes of inequity in large societal systems over which they have little influence; and (3) justifying inaction. Conclusions Despite being progressively minded and highly educated, these men are frequently complicit in racism and sexism. We end with recommendations for helping cis men engage the power they hold to better work with marginalized people to disrupt inequity.

S. S. Manathunga, Y. A. Illangasekara

Large Language Models are increasingly being used for various tasks including content generation and as chatbots. Despite their impressive performances in general tasks, LLMs need to be aligned when applying for domain specific tasks to mitigate the problems of hallucination and producing harmful answers. Retrieval Augmented Generation (RAG) allows to easily attach and manipulate a non-parametric knowledgebases to LLMs. Applications of RAG in the field of medical education are discussed in this paper. A combined extractive and abstractive summarization method for large unstructured textual data using representative vectors is proposed.

Jens Dörpinghaus, David Samray, Robert Helmrich

As a result of transformation processes, the German labor market is highly dependent on vocational training, retraining and continuing education. To match training seekers and offers, we present a novel approach towards the automated detection of access to education and training in German training offers and advertisements. We will in particular focus on (a) general school and education degrees and schoolleaving certificates, (b) professional experience, (c) a previous apprenticeship and (d) a list of skills provided by the German Federal Employment Agency. This novel approach combines several methods: First, we provide a mapping of synonyms in education combining different qualifications and adding deprecated terms. Second, we provide a rule-based matching to identify the need for professional experience or apprenticeship. However, not all access requirements can be matched due to incompatible data schemata or non-standardizes requirements, e.g initial tests or interviews. While we can identify several shortcomings, the presented approach offers promising results for two data sets: training and re-training advertisements.

Mahdi Moeinikia, Shahram Mehravar Giglouu, Salim Kazami et al.

Background: Today, e-learning has become one of the basic components of education process, especially in higher education. Institutions and universities employ e-learning extensively in their educational operations. In light of this, the goal of the current research was to determine the advantages, disadvantages, possibilities, and dangers associated with e-learning in the Iranian higher education system.Method: The present research is applied in terms of purpose and with a qualitatively exploratory approach. The participants of present study were experts in the field of e-learning in public universities of the Ministry of Science, Research and Technology in 2021.Using purposive sampling and snowball sampling methods, 16 e-learning experts were selected as the participants. Semi-structured interviews were used to collect data and thematic analysis was employed to analyze the obtained data. Results: After analyzing the obtained data from the interview, the total number of 116 free codes were extracted from interviews content was 116 codes, which were classified in 18 concepts and finally were identified strengths (Use of office automation in universities, Establishment of information and communication technology centers in universities, Development of e-learning in universities, Familiarity of faculty members and students with virtual environments, The place of e-learning in upstream documents and university perspectives), weaknesses (Lack of proper infrastructure, equipment and facilities for e-learning, Lack of specialized manpower, Lack of formal regulations for e-learning in the field of higher education, Insufficient knowledge about e-learning), threats (Threats related to cost, facilities and time, Management threats, Threats to change the nature of the university, Threats related to interactions) and training opportunities (Increas access to e-learning, Expanding international and intercultural interactions, Environmental benefits, Providing economic opportunities , Development of educational justice) of e-learning in Iranian higher education system. Conclusion: Considering the research findings, to develop educational justice and the possibility of more population access to the University of the Student community, reviewing existing approaches and educational methods and using e-learning as a new educational strategy for higher education system are necessary

Zheng-Wei Chen, En-Ze Rui, Tang-Hong Liu et al.

Under strong winds, the effect of sudden windbreak transition (WT) on high-speed trains is severe, leading to a deterioration of train aerodynamics and sudden yawing motion of the car body. To address these problems, based on a high-speed train and the specific geometric conditions derived from Xinjiang railway, first, the impact of a WT on the train and reasons for sudden changes in aerodynamic forces were determined by flow structural analysis. Furthermore, based on a multibody system dynamic model, the dynamic responses to WT were analysed. The results show that the impacts of WT were the strongest on the head car. WT had a strong effect on the train due to the unreasonable structural shape and the insufficient height of the windbreak in the transition region. This led to a strong push effect on the train; subsequently, the train’s dynamic characteristics deteriorated.

Matti Tedre, Tapani Toivonen, Juho Kahila et al.

Over the past decades, numerous practical applications of machine learning techniques have shown the potential of data-driven approaches in a large number of computing fields. Machine learning is increasingly included in computing curricula in higher education, and a quickly growing number of initiatives are expanding it in K-12 computing education, too. As machine learning enters K-12 computing education, understanding how intuition and agency in the context of such systems is developed becomes a key research area. But as schools and teachers are already struggling with integrating traditional computational thinking and traditional artificial intelligence into school curricula, understanding the challenges behind teaching machine learning in K-12 is an even more daunting challenge for computing education research. Despite the central position of machine learning in the field of modern computing, the computing education research body of literature contains remarkably few studies of how people learn to train, test, improve, and deploy machine learning systems. This is especially true of the K-12 curriculum space. This article charts the emerging trajectories in educational practice, theory, and technology related to teaching machine learning in K-12 education. The article situates the existing work in the context of computing education in general, and describes some differences that K-12 computing educators should take into account when facing this challenge. The article focuses on key aspects of the paradigm shift that will be required in order to successfully integrate machine learning into the broader K-12 computing curricula. A crucial step is abandoning the belief that rule-based "traditional" programming is a central aspect and building block in developing next generation computational thinking.

Senad Bećirović, Damir Bešlija

The twenty-first century is the century of encounter of the different races, nations, cultures, religions and customs. In the twenty-first century, man is more and more exposed to various influences that leave a trace on the entire sphere of his social life, including education. Given that education systems play one of the key roles in the formation of both physically and morally healthy communities, it is of an enormous importance to analyze the phenomenon of a classroom composed of culturally diverse students.