mdpi-ai/citelens huggingface dataset:
The Role of Growth Mindset on the Relationships between Students’ Perceptions of English Language Teachers’ Feedback and Their ESL Learning Performance
Field: EDUCATION

The importance of growth mindset and teachers’ feedback has been widely recognised to improve the English language performance of students; however, the impact of growth mindset as a mediator is least explored. Therefore, the study aimed to empirically analyse the interrelationships between growth mindset and teachers’ feedback levels on secondary school students’ English as a Second Language (ESL) performance and to study the mediation effects of growth mindset in the relationships. The research model examined growth mindset along with four types of feedback. The levels of feedback include task, process, self-regulation and self-based feedback that teachers provide to improve the ESL performance of students. Survey questionnaires were administered to 301 secondary school students in Class 9 from two private schools in India. The data were analysed using PLS-SEM 4.0 software. The results indicated that the direct effect of feedback that emphasised process and self-regulation fosters a growth mindset in ESL students. Feedback levels focused on task, process, self-regulation, and growth mindset significantly impact ESL performance. Moreover, growth mindset mediated the relationships between process and self-regulation-focused feedback and ESL performance. However, no evidence supports the relationship between self-focused feedback, growth mindset, and ESL performance. The study concludes with implications and directions for future research.
Pursuing beauty: socio-cultural and labor-economic determinants of cosmetic surgery consideration among female college students in China
Field: PSYCHOLOGY

Cosmetic surgery has a profound impact on health and other aspects. As a means of enhancing physical attractiveness, it is increasingly being considered by female college students in China. However, current knowledge about the determinants of cosmetic surgery consideration among Chinese female college students still needs to be improved due to the lack of systematic perspectives and large-scale representative data sets. This study aimed to contribute to the literature in these two aspects. We framed cosmetic surgery consideration as a function of two broad sets of determinants: socio-cultural and labor-economic. We used data from a large, nationally representative sample of female college students in China (N = 6658, mean age = 20.3 years). In terms of socio-cultural oriented factors, we found that family socioeconomic status, peers' cosmetic surgery practices, and media exposure were positively associated with the likelihood of considering cosmetic surgery. In terms of labor-economic oriented factors, we found that self-rated physical appearance, higher grades, and expected income after graduation were positively associated with a higher likelihood of considering cosmetic surgery. These findings suggest that the decision-making process for cosmetic surgery among Chinese female college students goes beyond personal factors and is significantly influenced by structural factors.
Comparing emotions in ChatGPT answers and human answers to the coding questions on Stack Overflow
Field: AI

Introduction: Recent advances in generative Artificial Intelligence (AI) and Natural Language Processing (NLP) have led to the development of Large Language Models (LLMs) and AI-powered chatbots like ChatGPT, which have numerous practical applications. Notably, these models assist programmers with coding queries, debugging, solution suggestions, and providing guidance on software development tasks. Despite known issues with the accuracy of ChatGPT’s responses, its comprehensive and articulate language continues to attract frequent use. This indicates potential for ChatGPT to support educators and serve as a virtual tutor for students.Methods: To explore this potential, we conducted a comprehensive analysis comparing the emotional content in responses from ChatGPT and human answers to 2000 questions sourced from Stack Overflow (SO). The emotional aspects of the answers were examined to understand how the emotional tone of AI responses compares to that of human responses.Results: Our analysis revealed that ChatGPT’s answers are generally more positive compared to human responses. In contrast, human answers often exhibit emotions such as anger and disgust. Significant differences were observed in emotional expressions between ChatGPT and human responses, particularly in the emotions of anger, disgust, and joy. Human responses displayed a broader emotional spectrum compared to ChatGPT, suggesting greater emotional variability among humans.Discussion: The findings highlight a distinct emotional divergence between ChatGPT and human responses, with ChatGPT exhibiting a more uniformly positive tone and humans displaying a wider range of emotions. This variance underscores the need for further research into the role of emotional content in AI and human interactions, particularly in educational contexts where emotional nuances can impact learning and communication.
How depression and ADHD relate to exercise addiction: a cross-sectional study among frequent exercisers
Field: PSYCHOLOGY

Background: To date, there are no official diagnostic criteria for the frequently reported phenomenon of exercise addiction. Therefore, the aim of the present study was to investigate how mental disorders, specifically depression and attention-deficit hyperactivity disorder (ADHD), are related to exercise addiction (EA).Methods: A total of 173 participants aged between 18 and 70 years, who reported exercising more than 10 h a week and continued to exercise despite injury or illness, answered questionnaires including the Exercise Dependence Scale, the Beck Depression Inventory, and the Homburger ADHD scale for adults. Multiple linear regression analyses were performed adjusting for relevant confounders (age, gender) and stepwise regression was used to identify which of the two mental disorders is the more influential predictor of EA.Results: Pearson correlation analysis showed that depressive symptoms [r (171) = 0.422, p < 0.00] and ADHD symptoms [r (171) = 0.308, p < 0.001] were positively correlated with EA symptoms. The relation between depressive symptoms and EA remained after adjusting for confounders in the regression model (B = 20.531; t(170) = 5.950; 95% CI [13.719, 27.343]; p < 0.001). Similarly, the positive link between ADHD symptoms and EA persisted after controlling for confounders (B = 15.507; t(170) = 3.771; 95% CI [7.389, 23.625]; p < 0.001). Additionally, a stepwise regression model identified that depressive symptoms are a stronger predictor for EA than ADHD symptoms.Conclusion: Depressive symptoms seem to be a stronger predictor for EA compared to ADHD symptoms in frequent exercisers. Although individuals with ADHD May exercise extensively, they might be less at risk for EA than individuals with depression. These results contribute to the complex characterization of the psychiatric profile of individuals with exercise addiction, and underline the need for further research elucidating the interplay between mental disorders and EA.
I’m not half and half: navigating being a “both” in discipline-based education research
Field: EDUCATION

Introduction and methods: Through years of conversations, three discipline-based education researchers used a duoethnographic process to interrogate their own discipline-based education research (DBER) identities. We present a description of how these individuals navigate being a “both,” gathered through reflections, discussions, and deeper research to explore perspectives of our professional identities and what we perceive those identities look like to our peers, supervisors, and trainees.Results: Our own definitions and eventually realized identities as a “both” emerged through this research process. We envision that science faculty have multiple roles, demands, and identities; at the most basic level, they are “both” an educator and a researcher. In the unique case of discipline-based education research (i.e., scholars studying the teaching and learning of science often in science departments), some faculty find an overlap between complementary yet sometimes competing research agendas (i.e., biology research (BR) and discipline-based education research (DBER)), of which they do “both.”Discussion: This article has two key contributions. First, it articulates this side-glancing process of our navigation of being a DBER “both,” leveraging each of our unique perspectives and the literature. Second, it represents how such an exploration may be useful to other interdisciplinary researchers in understanding and embracing all parts of their identities.
Efficient incremental training using a novel NMT-SMT hybrid framework for translation of low-resource languages
Field: AI

The data-hungry statistical machine translation (SMT) and neural machine translation (NMT) models offer state-of-the-art results for languages with abundant data resources. However, extensive research is imperative to make these models perform equally well for low-resource languages. This paper proposes a novel approach to integrate the best features of the NMT and SMT systems for improved translation performance of low-resource English–Tamil language pair. The suboptimal NMT model trained with the small parallel corpus translates the monolingual corpus and selects only the best translations, to retrain itself in the next iteration. The proposed method employs the SMT phrase-pair table to determine the best translations, based on the maximum match between the words of the phrase-pair dictionary and each of the individual translations. This repeating cycle of translation and retraining generates a large quasi-parallel corpus, thus making the NMT model more powerful. SMT-integrated incremental training demonstrates a substantial difference in translation performance as compared to the existing approaches for incremental training. The model is strengthened further by adopting a beam search decoding strategy to produce k best possible translations for each input sentence. Empirical findings prove that the proposed model with BLEU scores of 19.56 and 23.49 outperforms the baseline NMT with scores 11.06 and 17.06 for Eng-to-Tam and Tam-to-Eng translations, respectively. METEOR score evaluation further corroborates these results, proving the supremacy of the proposed model.
Long non-coding RNA FAM87A is associated with overall survival and promotes cell migration and invasion in gastric cancer
Field: ONCOLOGY

Background: The role of long non-coding RNAs (lncRNAs) in the invasion and metastasis of gastric cancer remains largely unclear.Methods: Integrating transcriptome data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, differentially expressed genes were identified in gastric cancer. Using the Catalogue of Somatic Mutations in Cancer (COSMIC) database-curated gene set, lncRNAs associated with invasion and metastasis were identified. The Cox analyses were performed to identify prognostic lncRNAs. The competing endogenous RNA (ceRNA) regulation network was constructed to identify hub lncRNAs in gastric cancer. Functional and pathway analyses were used to investigate the function of identified lncRNAs. RT-qPCR and Transwell assays were used to investigate the expression in gastric cancer tissues and functions in gastric cancer cell lines.Results: Based on GEO and TCGA databases, 111 differentially expressed lncRNAs were identified between gastric cancer and normal samples. A total of 43 lncRNAs were significantly correlated with hallmark genes of cancer invasion and metastasis. Among them, as a hub lncRNA in the invasion-related ceRNA regulation network, FAM87A showed potential regulation on MAPK signaling and transforming growth factor (TGF) signaling cascade, such as TGFB2, TGFBR1, and TGFBR2. Furthermore, FAM87A also showed a significant correlation with cell adhesion molecules, such as Integrin alpha 6 (ITGA6) and Contactin-1 (CNTN1). RT-qPCR experiments showed that FAM87A expression was upregulated in gastric cancer tissues compared to normal samples (n = 30). Transwell assays showed that FAM87A knockdown inhibited the migration and invasion abilities of gastric cancer cells in vitro. Notably, clinical data analysis showed that lncRNA FAM87A could be an independent factor for the overall survival of patients with gastric cancer.Conclusion: LncRNA FAM87A may play a pivotal role in regulating migration and invasion of gastric cancer cells. FAM87A could be a potential biomarker for the overall survival of patients with gastric cancer.
The role of extracellular vesicles in the pathogenesis of gynecological cancer
Field: ONCOLOGY

Gynecological cancer, the most common form of cancers in women worldwide, initiates in the reproductive organs of females. More often, the common treatment measures, i.e. surgery, radiation, and medical oncology are found to be unsuccessful in the treatment of gynecological tumors. Emerging evidence indicates that extracellular vesicles (EVs) play a significant role in the pathogenesis of gynecological cancers by distinct mechanisms. The present review highlights how EVs contribute to the progression of different types of gynecological cancers such as cervical cancer, endometrial cancer, ovarian cancer, vaginal cancer, uterine sarcoma, gestational trophoblastic disease (GTD), and vulvar cancer. The primary focus is to understand how EVs’ cargo alters the phenotypic response of the recipient cells, thereby contributing to the progression of the disease, thus can be considered as a prognostic and diagnostic biomarker. A brief discussion on the role of EVs in the diagnosis and prognosis of different gynecological cancer types is also highlighted. Targeting the biogenesis of the EVs, their inside cargo, and EVs uptake by the recipient cells could be a potential therapeutic approach in the treatment of gynecological cancer beside conventional therapeutic means.
PD-1 expression in tumor infiltrating lymphocytes as a prognostic marker in early-stage non-small cell lung cancer
Field: ONCOLOGY

Introduction: Programmed death ligand – 1 (PD-L1) expression is a well-established predictive biomarker for immunotherapy in non-small cell lung cancer (NSCLC). Programmed death – 1 (PD-1) serves as the target protein to PD-L1 and their interaction serves as a crucial pathway for immune evasion. This study aimed to investigate the expression pattern of PD-1 on Tumor-infiltrating lymphocytes (TILs) in early-stage NSCLC, and its potential role as prognostic biomarker.Materials & methods: PD-1 was evaluated in 474 surgical resected early-stage NSCLC specimens, using Tissue microarray and immunohistochemical staining. Expression was scored as negative (<1%) or positive. Positive PD-1 expression was further divided into low (<10%) and high (≥10%). None of the patients had received treatment with PD-1/PD-L1 inhibitors.Results: PD-1 expression ≥1% in TILs was observed in 83.5% of cases and was associated with pT stage (p=0.02), grade 3 (p=0.004), and adenocarcinoma subtype (p=0.05). Individuals with high PD-1 expression (≥10%) experienced reduced 10-year overall survival (Log-Rank test = 0.005). In addition, high PD-1 expression emerged as an independent factor associated with reduced survival on multivariate analysis (HR: 1.328 (95% CI: 1.074-1.641).Conclusions: Patients with early-stage NSCLC who exhibited PD-1 expression of ≥10% on TILs had an unfavorable 10-year OS rate. These findings indicate that elevated PD-1 expression on TILs can be associated with immune evasion during the early stages of malignancy evolution in the NSCLC setting and further research is required to further delineate the role of PD-1/PD-L1 pathway on tumor immune senescence. These results underline the potential role of PD-1/PD-L1 inhibitors in the treatment of early-stage NSCLC.
Enhancing mathematical problem posing competence: a meta-analysis of intervention studies
Field: EDUCATION

Mathematical problem posing, generally defined as the process of interpreting given situations and formulating meaningful mathematical problems, is academically important, and thus several interventions have been used to enhance this competence among students and teachers. Yet little is known about the interventions’ various components and their relative or combined effectiveness. In this meta-analysis of 26 intervention studies in mathematics, we identified nine intervention components and found that the interventions had a medium, positive, and significant mean weighted effect size. A stepwise meta-regression analysis revealed that intervention efficacy varied by moderators relevant to the research design, sample characteristics, and intervention characteristics. The findings obtained from this meta-analysis are expected to serve as a foundation for future efforts to design and implement (more) effective interventions to improve mathematical problem posing competence.
“Not a cookie cutter situation”: how neurodivergent students experience group work in their STEM courses
Field: EDUCATION

Although group work is increasingly used in STEM courses and may lead to improved academic outcomes, there is evidence that some implementations of group work may lead to unintended barriers for certain students’ learning. Despite the growing number of neurodivergent undergraduate students, there is limited research on neurodivergent students’ experiences with group work in STEM courses. To address this knowledge gap, the current research investigated the experiences of 22 neurodivergent undergraduate students with group work in STEM courses at a range of institution types and in a variety of STEM disciplines. Participants shared experiences with in-class and out-of-class group work assignments for lecture and laboratory courses. Through inductive thematic coding of semi-structured interview transcripts, we identified seven themes impacting participants’ experiences. Three themes were individual level: personal characteristics that participants associated with their neurodivergence; strategies for academic success (with subthemes of organization/time management, adaptive communication, and self-advocacy); and beliefs on group work’s value. Four themes were group level/classroom level: group dynamics; role in group (including leadership roles); the competitive culture within STEM; and recommendations for instructors. Through a social-relational perspective on disability, we proposed a model showcasing how group and classroom factors serve as supports or barriers to neurodivergent students’ full participation in group work, as well as to their sense of belonging. Using the seven themes we articulated, we outlined a set of practices for designing group work assignments. In addition, we propose how pairing inclusive assignment design with instructor reflection and articulating anti-ableist values can support neurodivergent student belonging by disrupting discourses of normalcy in STEM. As one of the first studies exploring the impact that group work in STEM courses has on neurodivergent undergraduates, this work may inform reimaginations of group work practices to better address the needs of neurodivergent STEM students and support a more inclusive culture in STEM classrooms. In addition, our conceptual model may serve as the basis for future research regarding interactions between individual-level and group-level factors associated with neurodivergent students’ learning through group work and other active learning practices.
The mediating role of meaning in work in promoting teachers’ technology integration
Field: EDUCATION

Teachers’ integration of technology has been a critical focus for both teachers and researchers over the past three decades. This emphasis has intensified due to the COVID-19 pandemic, where technology integration has become a key factor in the success of classroom teaching and learning processes. Despite this attention, previous studies have shown limited exploration of the relationship between teachers’ technology integration and meaning in work as an internal variable. Therefore, using AMOS-structural equation modeling (SEM) analysis, this study aimed to develop a conceptual model examining the mediating role of meaning in work in the relationship between digital leadership, self-efficacy, and teachers’ technology integration. The study involved 200 junior high school teachers from Balikpapan City, East Kalimantan Province, a region in eastern Indonesia projected to become the new capital. A total of four variables were analyzed in this study: meaning in work, digital leadership, self-efficacy, and teachers’ technology integration (Z, X, and Y, respectively). The results showed that (1) digital leadership affected meaning in work and teachers’ technology integration, (2) self-efficacy affected meaning in work and teachers’ technology integration, (3) meaning in work affected teachers’ technology integration, and (4) meaning in work could mediate the relationship between digital leadership and self-efficacy in teachers’ technology integration. These findings contribute to a deeper understanding of the relationships among digital leadership, self-efficacy, and meaning in work, and their collective impact on teachers’ technology integration. Furthermore, the study highlights the significant role of meaning in work as a mediator in these relationships, providing a foundation for the development of digital leadership strategies and training programs aimed at improving technology integration in education.
Bayesian Optimization with Additive Kernels for a Stepwise Calibration of Simulation Models for Cost-Effectiveness Analysis
Field: AI

A critical aspect of simulation models used in cost-effectiveness analysis lies in accurately representing the natural history of diseases, requiring parameters such as probabilities and disease burden rates. While most of these parameters can be sourced from scientific literature, they often require calibration to align with the model’s expected outcomes. Traditional optimization methods can be time-consuming and computationally expensive, as they often rely on simplistic heuristics that may not ensure feasible solutions. In this study, we explore using Bayesian optimization to enhance the calibration process by leveraging domain-specific knowledge and exploiting structural properties within the solution space. Specifically, we investigate the impact of additive kernel decomposition and a stepwise approach, which capitalizes on the sequential block structure inherent in simulation models. This approach breaks down large optimization problems into smaller ones without compromising solution quality. In some instances, parameters obtained using this methodology may exhibit less error than those derived from naive calibration techniques. We compare this approach with two state-of-the-art high-dimensional Bayesian Optimization techniques: SAASBO and BAxUS. Our findings demonstrate that Bayesian optimization significantly enhances the calibration process, resulting in faster convergence and improved solutions, particularly for larger simulation models. This improvement is most pronounced when combined with a stepwise calibration methodology.