mdpi-ai/citelens huggingface dataset:
Metacognitive Monitoring in Written Communication: Improving Reflective Practice
Field: EDUCATION

Educational programs aimed at developing metacognitive skills usually focus on students, neglecting the development of teachers by teaching metacognitively aware instructional methods. The effectiveness of such development programs is well-established, but there is a gap between research findings and their application in schools. A framework for a training program was developed in the context of an international partnership project aimed at enhancing the metacognitive abilities of both children and teachers. The final form of classroom activities was developed at the country level using action research methods with the involvement of teachers. After implementing a 3-week educational program involving 35 experimental and 19 control groups from Romanian public schools, a comparison of pre- and post-test scores indicated a significant increase in the number of children in the experimental group with improved efficiency in metacognitive monitoring in reading. Teachers’ metacognitive awareness significantly improved after the Teacher Training Program, as indicated by a comparison of the pre- and post-training results of the Metacognitive Awareness Inventory for Teachers (MAIT). No correlation was found between teachers’ development scores (as expressed by differences between pre- and post-intervention MAIT results) and the number of students from their classes whose progress in metacognitive monitoring significantly increased. The cyclical process of the action research methodology proved to be useful for increasing the efficiency of the intervention program. However, due to methodological limitations, the results are primarily interpretable within a local context. The results confirm expert recommendations aimed at integrating the targeted development of metacognitive teaching skills into both pre-service and in-service teacher training programs.
Integration of Artificial Intelligence in K-12: Analysis of a Three-Year Pilot Study
Field: AI

A three-year pilot study investigated the effectiveness of artificial intelligence (AI) as a motivational tool for teaching programming concepts within the Croatian Informatics curriculum. The study was conducted in schools through the extracurricular activity EDIT CodeSchool with the Development of Intelligent Web Applications (RIWA) module. Twelve schools in Split-Dalmatia County in the Republic of Croatia participated, resulting in 112 successfully completed student projects. The program consisted of two phases: (1) theoretical instruction with examples and exercises, and (2) project-based learning, where students developed final projects using JavaScript and the ml5.js library. The study employed project analysis and semi-structured student interviews to assess learning outcomes. Findings suggest that AI-enhanced learning can effectively support programming education without increasing instructional hours, providing insights for integrating AI concepts into existing curricula.
Self-Regulation and Teacher Feedback in Problem-Based Learning on the Water Hardness
Field: EDUCATION

Problem-Based Learning has been recognized as a fundamental approach in Science Education. Studies show that the success of this approach depends on students’ ability to self-regulate their learning and on teacher feedback. However, research on how these aspects interact in formal science teaching contexts remains limited. This study aims to address this gap by investigating two questions: (1) What self-regulation strategies are used by different student groups when solving a problem related to water hardness? (2) How do different types of teacher feedback influence students’ problem-solving processes? The study involved 27 students and their Physics and Chemistry teacher. Students participated in an activity that required solving a problem related to water hardness. Data were collected through audio recordings, and the content of the transcriptions was analyzed. The results showed connections between self-regulation strategies and teacher feedback during the problem-solving process. Groups with high participation employed diverse self-regulation strategies, successfully solved the problem, and received varied teacher feedback. The group with the lowest participation received the least feedback from the teacher. Future research should focus on examining how different types of teachers’ feedback during specific interventions for less-participative groups influence the development of their self-regulation strategies.
Real-World Insights into the Impact of Durvalumab on Stage III Unresectable Non-Small Cell Lung Cancer—A Narrative Review
Field: ONCOLOGY

Introduction and Aim: Stage III Non-Small Cell Lung Cancer (NSCLC) has a poor prognosis, with median survival ranging from 9 to 34 months. The PACIFIC trial demonstrated that durvalumab after platinum-based chemoradiotherapy (CRT) improves overall survival (OS) and progression-free survival (PFS). This review evaluates real-world evidence (RWE) on durvalumab’s efficacy and safety, focusing on patient characteristics, prognostic factors, treatment protocols, and outcomes beyond progression. Materials and Methods: A literature search of PubMed, Embase, and Google Scholar identified 49 observational studies published from January 2017 to August 2024 on unresectable stage III NSCLC. Clinical trials, early-stage disease, and alternative treatments were excluded. Results: Compared to the PACIFIC trial, real-world patients were older, had poorer ECOG performance (≥2), and more comorbidities like COPD. Despite this, durvalumab provided consistent survival benefits. Positive prognostic factors included non-squamous histology, high PD-L1 expression, and timely durvalumab initiation (≤42 days post-CRT). Most radiotherapy regimens mirrored PACIFIC (54–66 Gy). Concomitant CRT was used in 90% of cases, with sequential CRT for frail patients. Chemotherapy regimens varied. Immune-mediated pneumonitis was a major adverse event, with incidence rates between 15% and 100%. Severe cases led to treatment discontinuation, impacting survival. Treatment beyond progression remains uncertain, with limited benefits from immunotherapy rechallenge. Conclusions: RWE supports durvalumab’s efficacy, emphasizing the need for personalized treatment strategies and further research to improve long-term outcomes.
Sentence Interaction and Bag Feature Enhancement for Distant Supervised Relation Extraction
Field: AI

Background: Distant supervision employs external knowledge bases to automatically match with text, allowing for the automatic annotation of sentences. Although this method effectively tackles the challenge of manual labeling, it inevitably introduces noisy labels. Traditional approaches typically employ sentence-level attention mechanisms, assigning lower weights to noisy sentences to mitigate their impact. But this approach overlooks the critical importance of information flow between sentences. Additionally, previous approaches treated an entire bag as a single classification unit, giving equal importance to all features within the bag. However, they failed to recognize that different dimensions of features have varying levels of significance. Method: To overcome these challenges, this study introduces a novel network that incorporates sentence interaction and a bag-level feature enhancement (ESI-EBF) mechanism. We concatenate sentences within a bag into a continuous context, allowing information to flow freely between them during encoding. At the bag level, we partition the features into multiple groups based on dimensions, assigning an importance coefficient to each sub-feature within a group. This enhances critical features while diminishing the influence of less important ones. In the end, the enhanced features are utilized to construct high-quality bag representations, facilitating more accurate classification by the classification module. Result: The experimental findings from the New York Times (NYT) and Wiki-20m datasets confirm the efficacy of our suggested encoding approach and feature improvement module. Our method also outperforms state-of-the-art techniques on these datasets, achieving superior relation extraction accuracy.
Transfer learning-based hybrid VGG16-machine learning approach for heart disease detection with explainable artificial intelligence
Field: AI

Heart disease is a leading cause of mortality worldwide, making accurate early detection essential for effective treatment and management. This study introduces a novel hybrid machine-learning approach that combines transfer learning using the VGG16 convolutional neural network (CNN) with various machine-learning classifiers for heart disease detection. A conditional tabular generative adversarial network (CTGAN) was employed to generate synthetic data samples from actual datasets; these were evaluated using statistical metrics, correlation analysis, and domain expert assessments to ensure the quality of the synthetic datasets. The dataset comprises tabular data with 13 features, which are reshaped into an image-like format and resized to 224x224x3 to meet the input requirements of the VGG16 model. Feature extraction is performed using VGG16, and the extracted features are then fused with the original tabular data. This combined feature set is then used to train various machine learning models, including Support Vector Machines (SVM), Gradient Boosting, Random Forest, Logistic Regression, K-nearest neighbors (KNN), and Decision Trees. Among these models, the VGG16-Random Forest hybrid achieved notable results across all evaluation metrics, including 92% accuracy, 91.3% precision, 92.2% recall, 91.82% specificity, 92.2% sensitivity, and 91.75% F1-score. The hybrid models were also evaluated using unseen datasets to assess the generalizability of the proposed approaches, with the VGG16-Random Forest combination showing relatively promising results. Additionally, explainability is integrated into the model using SHAP values, providing insights into the contribution of each feature to the model’s predictions. This hybrid VGG16-ML approach demonstrates the potential for highly accurate and interpretable heart disease detection, offering valuable support in clinical decision-making processes.
Improving sense of belonging in biomedical engineering students through student-faculty lunches
Field: EDUCATION

Introduction: Full undergraduate experience in biomedical engineering should feature cordial interactions between students and faculty as well as a good sense of belonging. However, both factors remain elusive for many students, rendering their undergraduate experience suboptimal. We designed the organized student-faculty lunches to promote informal student-faculty interactions and the formation of belonging among the student participants.Methods: During each lunch, an average of four student participants were paired with one faculty and a student assistant. Lunches were provided at no cost to all participants. Invites for students were based on matching interests during recruitment. A mixed-methods survey, including eight identical Likert-scale questions and up to three free-response questions, was distributed three times: before, immediately after, and 1 month after the lunch. We collected a total of 42 responses for the post-survey and 28 responses for the one-month survey. Four students participated in a 30-minute interview. We used paired t-tests to analyze the Likert-scale questions across the three surveys. We performed regression analysis to quantify the equity in the outcomes of these lunches. We obtained guidelines for conducting these lunches in the future through regression analysis and thematic coding of the surveys and the interviews.Results: We found that the student-faculty lunches generated significant positive impact across all eight Likert-scale questions across three domains of belonging: academic, social, and personal space. Improvements in survey questions within the social and personal space domains tend to be longer lasting and more statistically significant. The regression analyses revealed that our interventions resulted in better parity in sense of belonging among students with different years of academic experience, ethnic identities, and gender identities. These analyses also suggest that the most effective lunch is conducted in the middle of the Winter quarter with an Assistant Professor. Coding analyses revealed that the students were highly satisfied with the lunches and the current format of facilitation, while noting the benefits of these lunches in reducing the interaction barriers between students and faculty. We intend to perform more qualitative analyses on aspects of equity and faculty demographics concerning their impact on the outcomes of these lunches.
Integrating global perspectives in biomedical science education: the role of project-based learning in addressing Western-centric paradigms and enhancing student preparedness for global health challenges
Field: EDUCATION

Biomedical Sciences education has traditionally focused on Western paradigms, often overlooking the health challenges faced in less economically developed countries. Integrating global perspectives is essential, yet institutional guidelines lack clear directives for doing so. This perspective paper proposes a project-based learning (PBL) approach within undergraduate biomedical sciences modules, which focuses on tropical infectious diseases to promote decolonized learning by contrasting the Global North and South. In this model, students will work collaboratively to learn problem-solving techniques relevant to real-world issues like tropical diseases. Although in theory PBL is a useful way of learning, there are potential challenges with group dynamics and engagement. This paper discusses the various benefits and limitations of implementing this approach.
Exploring gender differences in workload and job performance: insights from junior high school teachers
Field: PSYCHOLOGY

Notable gender disparities exist in the workload and performance of junior high school teachers, although the specific ways in which these disparities manifest have not been fully elucidated. This study examines how specific aspects of teachers’ workload are related to gender differences in aspects of work performance. The study used survey data from 1135 junior high school educators. Teacher workload was assessed using the NASA Task Load Index. Teachers’ work performance was evaluated in terms of task performance and contextual performance. Demographic data included gender, teaching experience, teaching grade, titles, school ownership, rural school designation, marital status, whether they had children, were internal teachers, were multidisciplinary teachers, and whether they were the main subject teachers. Oaxaca–Blinder decomposition was used to analyze the specific contribution and mechanism of workload to the gender gap in work performance. The findings revealed distinct gender differences in work performance, with male teachers demonstrating higher task performance and female teachers reporting higher contextual performance, which mediated the observed disparity. Further analysis indicated that marital status also plays a role, with single teachers experiencing a more pronounced gender gap. These insights signify that gender is a pivotal factor in junior high school teachers’ workload and performance. The study advocates for a deeper investigation within the “gender-workload-capacity development” framework to assist educators in making informed decisions and to foster a more equitable work environment.
Coarse matching was sufficient to capture attention by working memory representations unless matching features with the target
Field: PSYCHOLOGY

Background: In most theoretical frameworks, the effectiveness of attentional selection relies significantly on the perceptual similarity between the target template and visual input. Nevertheless, ambiguity exists surrounding whether attentional capture triggered by irrelevant representations in Working Memory (WM) is influenced by the perceptual similarity levels of features between WM content and its matching distractors. Methods: We designed a hybrid WM and visual search task, varying such perceptual similarity of colors across three levels: exact, high-similar, and low-similar matching. To quantify the extent of the capture effect, we compared these conditions against a neutral baseline (i.e., completely different color) using eye movement and behavioral data in two experiments. Results: We consistently observed robust attentional capture effects across two experiments, evident in both eye movement indices and manual reaction times. In Experiment 1, where WM representations solely matched features to visual search distractors (task-irrelevant scenario), we found that changes in perceptual similarity did not influence attentional capture. Conversely, in Experiment 2, where WM representations had the potential to match the visual search target (task-relevant scenario), we observed a significantly more robust attentional capture effect for high-similar matching compared to low-similar matching conditions. Conclusions: These findings imply that coarse matching between distractors and WM contents is sufficient to capture attention, unless the matching features potentially correspond to the visual target. Furthermore, task relevance sharpens perceptual sensitivity to visual input, highlighting distinct mechanisms underlying attentional capture by irrelevant representations and target templates within WM.
The impact of AI-assisted pair programming on student motivation, programming anxiety, collaborative learning, and programming performance: a comparative study with traditional pair programming and individual approaches
Field: EDUCATION

This study investigates the impact of AI-assisted pair programming on undergraduate students’ intrinsic motivation, programming anxiety, and performance, relative to both human–human pair programming and individual programming approaches. A quasi-experimental design was conducted over two academic years (2023–2024) with 234 undergraduate students in a Java web application development course. Intact class sections were randomly assigned to AI-assisted pair programming (using GPT-3.5 Turbo in 2023 and Claude 3 Opus in 2024), human–human pair programming, or individual programming conditions. Data on intrinsic motivation, programming anxiety, collaborative perceptions, and programming performance were collected at three time points using validated instruments. Compared to individual programming, AI-assisted pair programming significantly increased intrinsic motivation (p < .001, d = 0.35) and reduced programming anxiety (p < .001), producing outcomes comparable to human–human pair programming. AI-assisted groups also outperformed both individual and human–human groups in programming tasks (p < .001). However, human–human pair programming fostered the highest perceptions of collaboration and social presence, surpassing both AI-assisted and individual conditions (p < .001). Mediation analysis revealed that perceived usefulness of the AI assistant significantly mediated the relationship between the programming approach and student outcomes, highlighting the importance of positive perceptions in leveraging AI tools for educational benefits. No significant differences emerged between the two AI models employed, indicating that both GPT-3.5 Turbo and Claude 3 Opus provided similar benefits. While AI-assisted pair programming enhances motivation, reduces anxiety, and improves performance, it does not fully match the collaborative depth and social presence achieved through human–human pairing. These findings highlight the complementary strengths of AI and human interaction: AI support can bolster learning outcomes, yet human partners offer richer social engagement. As AI capabilities advance, educators should integrate such tools thoughtfully, ensuring that technology complements rather than replaces the interpersonal dynamics and skill development central to effective programming education.
Moral transgressions, psychological well-being, and family conflict in the context of the COVID-19 pandemic: The role of self-forgiveness
Field: PSYCHOLOGY

The COVID-19 pandemic led many individuals to experience moral transgressions, exacerbating feelings of guilt and remorse. This study explored the role of the self-forgiveness of such transgressions in explaining their associations with psychological well-being and family conflict. We hypothesized that (a) higher levels of self-forgiveness would be associated with greater psychological well-being and reduced family conflict, (b) the perceived relevance of moral transgressions would be positively associated with self-forgiveness and indirectly associated with psychological well-being and family conflict through the mediation of self-forgiveness, and (c) the relationships between the variables of interest could vary across age. Adults (N = 277; M age = 30.04) completed anonymous online questionnaires assessing the relevance of transgressions committed, forgiveness and unforgiveness of self, psychological well-being, and family conflict during the first COVID-19 lockdown in Italy. Structural equation modeling revealed that transgression relevance was positively associated with both forgiveness and unforgiveness of self, and indirectly related to psychological well-being and family conflict via self-forgiveness. Greater forgiveness of self was related to greater eudaimonic well-being, whereas greater unforgiveness of self was linked to increased family conflict and reduced eudaimonic well-being. The findings also indicated that age moderated the relationship between forgiveness of self and hedonic well-being, with the association weakening as age increased. The results highlight the importance of promoting self-forgiveness to enhance psychological resilience and familial stability, particularly during challenging times.
Lattice-Based Decision Models for Green Urban Development: Insights from $$L_{q}*$$ q-Rung Orthopair Multi-fuzzy Soft Set
Field: AI

Location selection is a critical process in decision-making for projects that involve multiple criteria, such as urban planning, industrial site development, or green building projects. Multiple criteria decision making (MCDM) is a systematic approach that evaluates and ranks potential alternatives based on a set of often conflicting criteria. This study focuses on selecting the optimal urban location for a green building project by employing the \(L_{q}*\) q-rung orthopair multi-fuzzy soft-MCDM(\(L_{q}*\) q-ROMFS) techniques. The \(L_{q}*\) q-ROMFS set combines elements from two distinct theories with lattice ordering parameters: q-rung orthopair fuzzy set and multi-fuzzy soft set. It provides a mathematical framework with multiple parameters that effectively represents problems involving multi-dimensional data within a dataset. We expand this concept by establishing the algebraic structures of \(L_{q}*\) q-ROMFS sets, including properties like modularity and distributivity, while also analyzing their homomorphism under lattice mappings. Finally, leveraging the \(L_{q}*\) q-ROMFS matrix, we propose both a choice matrix and a weighted choice matrix to effectively address the selection of the optimal urban location for a green building project.