Search Results (772)

First-generation students can experience a cultural mismatch between their values and those that colleges and universities tend to prioritize. This mismatch can increase cognitive load, leaving fewer resources available for learning. Effective and long-lasting learning requires actively processing new information and connecting it to existing knowledge—an effort that demands significant cognitive resources. Value affirmation exercises, where students select and reflect upon values that are important to them, have shown promise in reducing cultural mismatch and improving performance on cognitive tasks. However, the impact of these exercises on the learning and recall of new information is less clear. The current study investigated whether a value affirmation exercise, completed before reading an educational passage, would improve memory recall for that passage in a sample of 400 first-generation and continuing-generation young adults, as compared to not affirming. Our results failed to provide evidence that value affirmation exercises impacted recall performance, regardless of whether participants affirmed independent values, interdependent values, or both. Given the importance and implications of this outcome for student learning, we discuss possible explanations for these null findings and suggest future directions in affirmation research. Full article

Despite the increasing number of studies indicating that generative artificial intelligence is conducive to cultivating college students’ critical thinking skills, research on the impact of college students’ use of generative artificial intelligence on their critical thinking skills in an open learning environment is still scarce. This study aims to investigate whether the use of generative artificial intelligence by college students in an open learning environment can effectively enhance their critical thinking skills. The study is centered around the following questions: Does the use of generative artificial intelligence in an open learning environment enhance college students’ critical thinking skills (what)? What is the mechanism by which the use of generative artificial intelligence affects college students’ critical thinking (how)? From the perspective of self-regulated learning theory and learning motivation theory, what are the reasons for the impact of generative artificial intelligence on college students’ critical thinking skills (why)? To this end, the study employs questionnaires and interviews to collect data. The questionnaire data are subjected to descriptive statistical analysis, correlation analysis, multiple stepwise regression analysis, and mediation effect analysis. Based on the analysis of interview materials and survey questionnaire data, the study reveals the impacts and mechanisms of college students’ use of generative artificial intelligence tools on their critical thinking skills. The findings of the study are as follows. First, the frequency of artificial intelligence use is unrelated to critical thinking skills, but using it for reflective thinking helps to develop critical thinking skills. Second, students with strong self-regulated learning skills are more likely to use generative artificial intelligence for reflective thinking and achieve better development in critical thinking skills. Third, students with strong intrinsic learning motivation are more likely to use generative artificial intelligence for reflective thinking and achieve better development in critical thinking skills. Consequently, the article analyzes the reasons from the perspectives of self-regulated learning theory and learning motivation theory and offers insights into how to properly use generative artificial intelligence to promote the development of critical thinking skills from the perspectives of higher education institutions, college teachers, and college students. Full article

Background: In a global panorama marked by a progressive rise in obesity, metabolic syndrome, and chronic non-communicable disease prevalence, nutrition education (NE) might play a pivotal role in restoring adoption and strengthening adherence to dietary patterns that protect human health. Therefore, the primary purpose of this work is to review the existing scientific literature studying NE programs aimed at schoolchildren in the decade 2014–2024 and evaluate the effectiveness of intervention tools. Methods: During the first phase of this research, a qualitative analysis was conducted to track similarity in intervention tools and strategies used in nutrition education programs. In the second phase, a quantitative analysis was carried out, extracting common parameters among studies and assessing their potential influence in improving adherence to the Mediterranean diet (MD). Results: A high degree of heterogeneity was observed in educational program designs and intervention tools, which were usually not properly described and justified. All studies that measured adherence to the MD registered an improvement after the intervention, in some cases even higher than 10%. However, this study found no relationship between common parameters (i.e., number of formal tools, number of non-formal tools, lesson duration, and program length) used in NE and the improvement in students’ adherence to MD. Conclusions: This research has contributed to outlining a general framework of NE and to promoting a systematic approach in this research field. Full article

Network security courses rely on hands-on labs where students configure virtual Linux networks to practice attack and defense. Automated feedback is scarce because no standard exists for exchanging detailed configurations—interfaces, bridging, routing tables, iptables policies—between exercise software and large language models (LLMs) that could serve as tutors. We address this interoperability gap with an exercise-oriented YANG profile that augments the Internet Engineering Task Force (IETF) ietf-network module with a new network-devices module. The profile expresses Linux interface settings, routing, and firewall rules, and tags each node with roles such as linux-server or linux-firewall. Integrated into our LiNeS Cloud platform, it enables LLMs to both parse and generate machine-readable network states. We evaluated the profile on four topologies—from a simple client–server pair to multi-subnet scenarios with dedicated security devices—using ChatGPT-4o, Claude 3.7 Sonnet, and Gemini 2.0 Flash. Across 1050 evaluation tasks covering profile understanding (n = 180), instance analysis (n = 750), and instance generation (n = 120), the three LLMs answered correctly in 1028 cases, yielding an overall accuracy of 97.9%. Even with only minimal follow-up cues (≦3 turns) —rather than handcrafted prompt chains— analysis tasks reached 98.1% accuracy and generation tasks 93.3%. To our knowledge, this is the first exercise-focused YANG profile that simultaneously captures Linux/iptables semantics and is empirically validated across three proprietary LLMs, attaining 97.9% overall task accuracy. These results lay a practical foundation for artificial intelligence (AI)-assisted security labs where real-time feedback and scenario generation must scale beyond human instructor capacity. Full article

Medical education is the paradigm of 21st century education and the current changes involve the adoption of integrative and comprehensive patient-centered teaching and learning approaches. Thus, curricular developers from George Emil Palade University of Medicine, Pharmacy, Sciences, and Technology of Targu Mures (G.E. Palade UMPhST of Targu Mures) have recently designed and implemented an innovative medical curriculum, as well as two valuable assessment tools for both theoretical knowledge and practical skills. Thus, during the first three preclinical years, the students will benefit from an organ- and system-centered block teaching approach, while the clinical years will focus on enabling students to achieve the most important practical skills in clinical practice, based on a patient bedside teaching system. In terms of theoretical knowledge assessment, the UNiX center at G.E. Palade UMPhST of Targu Mures, a recently designed center endowed with the latest next-generation technology, enables individualized, secured multiple-choice question-based assessments of the student’s learning outcomes. Moreover, an intelligent assessment tool for practical skills was also recently implemented in our branch in Hamburg, the Objective Structured Clinical Examination (O.S.C.E). This system uses direct observations for testing the student’s practical skills regarding anamnesis, clinical exams, procedures/maneuvers, the interpretation of laboratory tests and paraclinical investigations, differential diagnosis, management plans, communication, and medical counselling. The integrative, comprehensive, patient-centered curriculum and the intelligent assessment system, implemented in G.E Palade UMPhST of Targu Mures, help define innovation in education and enable the students to benefit from a high-quality medical education. Full article

Effective studying is important to learn better and increase academic achievement in postsecondary education, which also holds true for the challenging content of science, technology, engineering, and mathematics (STEM). Informed by previous research, this study mainly aimed to investigate first-year STEM college students’ study habits and perceptions of the effectiveness of different study strategies, and the frequency of use of these strategies. To this end, this study employed a cross-sectional survey using the Prolific platform. The results revealed that participants use various study strategies, including more and less effective ones, generally do not study in a planned way nor believe that learning takes hard work, and also prioritize approaching deadlines. The results also showed that the participants (a) frequently use the study strategies that they think are effective, suggesting that perceived effectiveness can have an important role in students’ strategy choice, and (b) mostly use study strategies for studying only or for both studying and while learning for fun. However, the frequency of the use of strategies partially aligned with the perceived effectiveness of the strategies. Overall, these results suggest the need to further investigate the conditions under which college students find study strategies effective, which can affect their choices. Full article

Modulation recognition, as one of the key technologies in the field of wireless communications, holds significant importance in applications such as spectrum resource management, interference suppression, and cognitive radio. While deep learning has substantially improved the performance of Automatic Modulation Recognition (AMR), it heavily relies on large amounts of labeled data. Given the high annotation costs and privacy concerns, researching semi-supervised AMR methods that leverage readily available unlabeled data for training is of great significance. This study constructs a semi-supervised AMR method based on dual-student. Specifically, we first adopt a dual-branch co-training architecture to fully exploit unlabeled data and effectively learn deep feature representations. Then, we develop a dynamic stability evaluation module using strong and weak augmentation strategies to improve the accuracy of generated pseudo-labels. Finally, based on the dual-student semi-supervised framework and pseudo-label stability evaluation, we propose a stability-guided consistency regularization constraint method and conduct semi-supervised AMR model training. The experimental results demonstrate that the proposed DualBranch-AMR method significantly outperforms traditional supervised baseline approaches on benchmark datasets. With only 5% labeled data, it achieves a recognition accuracy of 55.84%, reaching over 90% of the performance of fully supervised training. This validates the superiority of the proposed method under semi-supervised conditions. Full article

The transformer-based deep learning approach represents the current state-of-the-art in machine translation (MT) research. Large-scale pretrained transformer models produce state-of-the-art performance across a wide range of MT tasks for many languages. However, such deep neural network (NN) models are often data-, compute-, space-, power-, and energy-hungry, typically requiring powerful GPUs or large-scale clusters to train and deploy. As a result, they are often regarded as “non-green” and “unsustainable” technologies. Distilling knowledge from large deep NN models (teachers) to smaller NN models (students) is a widely adopted sustainable development approach in MT as well as in broader areas of natural language processing (NLP), including speech, and image processing. However, distilling large pretrained models presents several challenges. First, increased training time and cost that scales with the volume of data used for training a student model. This could pose a challenge for translation service providers (TSPs), as they may have limited budgets for training. Moreover, CO₂ emissions generated during model training are typically proportional to the amount of data used, contributing to environmental harm. Second, when querying teacher models, including encoder–decoder models such as NLLB, the translations they produce for low-resource languages may be noisy or of low quality. This can undermine sequence-level knowledge distillation (SKD), as student models may inherit and reinforce errors from inaccurate labels. In this study, the teacher model’s confidence estimation is employed to filter those instances from the distilled training data for which the teacher exhibits low confidence. We tested our methods on a low-resource Urdu-to-English translation task operating within a constrained training budget in an industrial translation setting. Our findings show that confidence estimation-based filtering can significantly reduce the cost and CO₂ emissions associated with training a student model without drop in translation quality, making it a practical and environmentally sustainable solution for the TSPs. Full article

Background: Medical education is considered one of the most academically and emotionally demanding training programs. Throughout their education, medical students are exposed to various factors that can lead to psychological distress, depression, and anxiety. The aim of this longitudinal study was to examine the changes in psychological distress, depression, anxiety levels and internet addiction among medical students throughout their six-year education and to identify the contributing factors. Methods: The study cohort consisted of 282 students who enrolled in the medical faculty in the 2017–2018 academic year. A questionnaire including sociodemographic characteristics, the General Health Questionnaire-12 (GHQ-12), Beck Depression Inventory (BDI), State–Trait Anxiety Inventory (STAI), and Young Internet Addiction Test (IAT) was administered to the students during the first week of their education. The same questionnaire was readministered at the end of the third and sixth years. Friedman’s variance analysis was used to compare measurement data across the three time points, while Cochran’s Q Test was employed for categorical variables. Results: The median scores of the GHQ-12, BDI, S-Anxiety, and IAT significantly increased from the first to the sixth year (p < 0.05). The prevalence of depressive symptoms, S-Anxiety, and risky internet use significantly increased from the first to the final year, particularly between the third and sixth years. According to logistic regression analysis based on sixth-year data, students whose fathers were university graduates, who had been diagnosed with COVID-19, and who were dissatisfied with their social lives were found to be at increased risk for psychological distress and depression. Students with high parental expectations were found to be at risk of depression and S-anxiety. Those dissatisfied with their occupational choice were at risk for both psychological distress and S-anxiety. Conclusions: It was found that the mental health of medical students deteriorated during their education, especially during the clinical years. Given that these students will be responsible for protecting and improving public health in the future, it is essential to prioritize their own mental well-being. Interventions aimed at preserving the mental health of medical students should be planned. Full article

Given the growing relevance of issues of educational inclusion at an international level, educational curricula have pointed out the need to address the diversity of students in the classroom. In this article, a theoretical reflection is proposed around the Universal Design for Learning (UDL) guideline—as inclusive principles for generic teaching and learning processes—and Didactic Suitability Criteria (DSC) guideline—as specific principles for mathematical teaching and learning processes—to establish relationships and seek complementarities between both references. To this end, firstly, a document analysis of literature about UDL was conducted; secondly, UDL and DSC guidelines were contrasted, relating UDL principles and verification points to DSC components and indicators to design a first proposal of an integrated guideline between both references; and, thirdly, an expert validation was conducted with researchers familiar with DSC to adjust the guideline originally proposed. As a main result, a proposal of integration of the UDL and DSC guidelines was designed, which intends to organise the reflection of (prospective and practising) mathematics teachers on their teaching practice. This integrated proposal not only seeks to address current curricular needs, but also to delve deeper into theoretical development that contributes to refining existing tools to encourage reflection on teaching practice. Full article

This paper introduces arguments in favor of the intensive use of standards in both teaching the Machine Elements discipline and solving the first projects of mechanical design (gearboxes, jacks, pumps, tanks, etc.). The paper presents a SWOTT approach to the use of new in-force standards in teaching the design of machine elements. The use of information from standards in courses and design handbooks is regulated by various standardization associations at different levels internationally, such as the ISO (International Organization of Standardization), IEC (International Electrotechnical Commission), and ITU (International Telecommunication), and regional associations such as the CEN (European Commission for Standardization), CENELEC (European Committee for Electrotechnical Standardization) and ETSI (European Telecommunications Standards Institute), and national associations (for instance, the ASRO—Association of Standardization of Romania). In general, the conditions for using partial information from standards vary, but the authors present common lines and recommendations for introducing information from standards in books and design handbooks for engineering students. The use of information from standards for terms, materials, calculation models, test methods etc. is beneficial for students. This will provide them a good professional education towards adapting to a specific job in the field of mechanical engineering, where conformity to norms and standards is required by the dynamics of production, product quality and, not least, the safety of machines and operators. Full article

The increasing demand for data-driven decision making to maintain the innovations and competitiveness of organizations highlights the need for data science educations across academia and industry. At its core is a solid understanding of statistics, which is necessary for conducting a thorough analysis of data and deriving valuable insights. Unfortunately, conventional statistics learning often lacks practice in real-world applications using computer programs, causing a separation between conceptual knowledge of statistics equations and their hands-on skills. Integrating statistics learning into Python programming can convey an effective solution for this problem, where it has become essential in data science implementations, with extensive and versatile libraries. In this paper, we present a self-learning method for fundamental statistics through Python programming for data science studies. Unlike conventional approaches, our method integrates three types of interactive problems—element fill-in-blank problem (EFP), grammar-concept understanding problem (GUP), and value trace problem (VTP)—in the Programming Learning Assistant System (PLAS). This combination allows students to write code, understand concepts, and trace the output value while obtaining instant feedback so that they can improve retention, knowledge, and practical skills in learning statistics using Python programming. For evaluations, we generated 22 instances using source codes for fundamental statistics topics, and assigned them to 40 first-year undergraduate students at UPN Veteran Jawa Timur, Indonesia. Statistics analytical methods were utilized to analyze the student learning performances. The results show that a significant correlation ($\rho <0.05$) exists between the students who solved our proposal and those who did not. The results confirm that it can effectively assist students in learning fundamental statistics self-learning using Python programming for data science implementations. Full article

Technology is essential in higher education, yet disparities in access disproportionately affect first-generation college students. This study examines how technology access and financial stress impact academic performance for first-generation (FGCS) and continuing-generation college students (CGCS). Students (N = 430) were asked to reflect on their experiences during the COVID-19 pandemic, particularly their technology access and financial stress. Results showed that FGCS reported significantly lower technology access and higher levels of financial stress than CGCS. Greater technology access was a significant positive predictor of academic performance for FGCS but not CGCS. However, this effect diminished when financial stress was added to the regression model. Moderation analysis showed that financial stress significantly moderated the relation between technology access and academic performance. This suggests that under high financial stress, technology access becomes a critical resource for academic performance. Full article

Bias and fairness issues in artificial intelligence (AI) algorithms are major concerns, as people do not want to use software they cannot trust. Because these issues are intrinsically subjective and context-dependent, creating trustworthy software requires human input and feedback. (1) Introduction: This work introduces an interactive method for mitigating the bias introduced by machine learning models by allowing the user to adjust bias and fairness metrics iteratively to make the model more fair in the context of undergraduate student admissions. (2) Related Work: The social implications of bias in AI systems used in education are nuanced and can affect university reputation and student retention rates motivating a need for the development of fair AI systems. (3) Methods and Dataset: Admissions data over six years from a large urban research university was used to create AI models to predict admissions decisions. These AI models were analyzed to detect biases they may carry with respect to three variables chosen to represent sensitive populations: gender, race, and first-generation college students. We then describe a method for bias mitigation that uses a combination of machine learning and user interaction. (4) Results and Discussion: We use three scenarios to demonstrate that this interactive bias mitigation approach can successfully decrease the biases towards sensitive populations. (5) Conclusion: Our approach allows the user to examine a model and then iteratively and incrementally adjust bias and fairness metrics to change the training dataset and generate a modified AI model that is more fair, according to the user’s own determination of fairness. Full article

From an occupational health perspective, if not stored, handled, and disposed of properly, laboratory chemicals exhibit hazardous properties such as flammability, corrosion, and explosibility. Additionally, they can also cause a range of health effects in handlers, including irritation, sensitization, and carcinogenicity. Additionally, the chemical waste generated during the planned assay is a significant byproduct and, if left untreated, can cause detrimental effects on both living organisms and non-living elements when released into the environment. Chemically, laboratory waste contains reagents, organic and inorganic compounds, and diagnostic stains. These agents are more toxic and hazardous than residential waste and affect the personnel handling them and the environments in which they are released. Considering this, it is crucial to adhere to waste management regulations during the various stages including generation, segregation, collection, storage, transportation, and treatment. This is extremely important and necessary if we are to avoid harm to individuals and environmental contamination. This review encompasses the examination of laboratory medical waste, various categories of chemical waste, and strategies to minimize and ensure the safe disposal of these toxic agents. As far as the authors are aware, this is the first review that focuses on the effects of laboratory-generated chemical wastes and environmental ethics. This is a neglected topic in healthcare education, and this review will serve as a valuable resource for students. Full article