Search Results (9,373)

This study examines how justice-oriented modeling lessons promote elementary students’ capacity to mathematize complex situations, develop civic empathy, and take action to address inequities and injustices in their communities. Through qualitative methods using multiple data sources including teacher interviews, lesson transcripts, student work, and classroom artifacts we share cases of modeling tasks that use mathematics as an empowerment tool to address empathy, representation, access, fairness and taking action. Findings illustrated critical moment-to-moment instructional decisions teachers made to elicit students’ justice-oriented reasoning. The modeling tasks involved addressing food waste in the school cafeteria, creating an inclusive play area, diversifying the school library collections, and choosing items for a sensory space to positively impact students’ individual and community well-being. Implications for teachers and teacher educators will be discussed. Full article

Objectives: This study aims to examine Internet addiction profiles, their associations with comorbid anxiety and depression, and characterize network architectures of anxiety and depression across profiles. Methods: From November 2022 to November 2023, we conducted a short-term l cohort study including 2503 students. Latent profile analysis (LPA) and multinomial logistic regression analysis were employed to investigate the association between Internet addiction and comorbidity of anxiety and depression, and network analysis was used to characterize anxiety–depression network structure within each profile. Results: LPA identified three profiles of Internet addiction, which were labeled: “regular” (66.60%) profile, “risk” profile (23.09%), and “addiction” profile (10.31%). The incidence of comorbid anxiety and depression was 10.67%. Both the “risk” (adjusted OR = 1.76, 95% CI: 1.27–2.44) and “addiction” (adjusted OR = 2.12, 95% CI: 1.39–3.24) profiles were significantly associated with increased comorbidity risk. The “dass13” (“Downhearted and blue”) emerged as a core symptom, and “dass15” (“Close to panic”) was identified as a key bridge symptom across three network models. The edge weight for the dass05–dass21 (Lack of motivation–Meaninglessness of life) was higher in the “risk” profile than in the “addiction” profile. Conclusions: Children and adolescents in the “risk” and “addiction” profiles were significantly more likely to experience comorbid anxiety and depression. “dass13” (“Downhearted and blue”) and “dass15” (“Close to panic”) can be used as the key target during intervention. Targeted interventions can be implemented for children and adolescents in the “risk” and “addiction” profiles. Full article

Background: It is well known that the university period is an important stage for young adults, involving significant academic and psychosocial adjustments. Students with greater Mental Health Literacy (MHL), which is defined as the knowledge, beliefs, and skills individuals have regarding mental health and mental illness, are better able to identify difficulties, seek help, and adopt healthier coping strategies. This study aims to describe the MHL levels of undergraduate health students and identify associated factors related to academic life, mental health and psychological state. Methods: A cross-sectional, self-administered, web-based survey was conducted using a non-probability sampling strategy among undergraduate students in health-related degrees at a Portuguese higher-education institution. Data was collected using a general characterization questionnaire and the following instruments: MHL Questionnaire, Academic Life Satisfaction, Subjective Happiness Scale, Psychological Well-Being Scale (PWBS), and Depression Anxiety Stress Scale. Bivariate and linear regression analyses were employed to identify factors associated with MHL. Results: A total of 306 students (79% female, mean age = 21.6 years; 59% nursing students) participated. The median MHL score was 70 (range: 30–80). The linear regression model explained 17.5% of the variance in MHL. Higher MHL levels were associated with having the course as a first choice, holding a previous degree, reporting taking psychotropic medication use (which may reflect previous mental health service utilization), and higher levels of psychological well-being. Conclusions: This study provides evidence on factors associated with MHL among undergraduate health students, suggesting that higher MHL is associated with greater psychological well-being, highlighting the potential importance of integrating strategies to promote MHL and psychological well-being in health and nursing education. However, these findings should be interpreted with caution due to the single-institution convenience sample, potential self-selection and reporting biases, and cross-sectional design, which limits causal inferences. Full article

Student retention has become a major challenge for higher education institutions due to the influence that academic, socioeconomic, family, and motivational factors exert on students’ academic continuity. In this context, understanding the determinants that explain university persistence is essential for designing effective retention strategies. Based on the analysis of factors related to motivation, commitment, attitude, academic integration, and social and economic conditions, retention patterns were examined in a population of 532 university students, of whom 57.7% showed high retention, 38.2% medium retention, and 4.1% low retention. To identify the factors with the greatest influence on academic continuity, educational data mining techniques and supervised classification models were applied and evaluated using stratified 10-fold cross-validation. Tree-based ensemble models showed the most consistent predictive performance, with Random Forest achieving the best results (accuracy = 0.729 ± 0.058; F1-macro = 0.636 ± 0.136). Model interpretability was examined through SHAP analysis, which revealed that transportation conditions (0.249), task completion (0.170), absence of work obligations (0.168), and course completion (0.164) were the most influential predictors in the classification of retention levels. In addition, sensitivity analysis indicated that academic commitment accounts for 41.6% of the predictive impact, followed by motivation (23.5%). These findings demonstrate that student retention is shaped by the interaction of academic, motivational, and contextual factors and provide practical implications for the development of **early warning systems, personalized tutoring programs, psychosocial support initiatives, and financial assistance policies aimed at strengthening university retention. Full article

Based on data from 208 students involved in entrepreneurship studies at Tecnológico de Monterrey, Mexico, this paper examines whether prior experience with AI, expectations, gender, and age reinforce future AI use. To achieve this objective, we applied binary logistic regression with random oversampling to balance the dataset. We complemented it with additional model performance metrics, including the confusion matrix, sensitivity, specificity, and area under the ROC curve. The results show that prior experience with AI, age-related technology use, and positive expectations regarding AI are associated with a higher likelihood of reinforcing future AI use. In terms of gender, the results indicate a gender gap favoring women, who are more likely to use AI when they perceive greater utility and confidence, as well as a stronger desire to succeed. Full article

This study, which aimed to instill values effective in developing social adaptation skills in students with special needs through traditional Turkish Cypriot children’s games, employed an action research model within a qualitative research design. The participants in the study were 5 students with mild intellectual disabilities aged 9 to 12. In the first step of the two-stage implementation plan, data were collected by having the students play traditional Turkish Cypriot children’s games, selected by the researchers and containing the relevant values, three times a week. In the second step, on the day following the game phase, the students’ acquisition of the target value was assessed through worksheets containing activities prepared by the researchers, which covered the basic points related to the target value. The aim was to instill 8 core values through applications that continued for a total of 5 weeks. After the completion of the application phase, a one-week break was given. During this period, the aim was to determine the short-term retention level of the targeted values. After a one-week follow-up, the researchers evaluated whether the students had learned the relevant values permanently in the short term through visuals and texts. The findings of this study, in which traditional Turkish Cypriot children’s games were practiced three times a week for five weeks, show that the games positively contributed to the learning of the targeted values and that the children adopted these values. However, the findings reflect only short-term retention; longer-term follow-up studies are needed to assess the long-term internalization of the values. Full article

Background: Several factors influence reasons for cannabis use in the U.S. This study examines reasons for cannabis use (recreational only, medical only, both) and their frequency of use in association with demographic variables, health-risk behaviors, legal status, and chronic disease. Methods: We performed a cross-sectional analysis of 466,355 adults (aged ≥18) in the 2018–2021 BRFSS surveys in areas that administered the cannabis module. The primary outcome variables were whether cannabis was used in the past 30 days and, if so, reasons for its use and the number of days of use. Regression techniques were used to assess these outcome measures according to selected variables. Results: Approximately 11.5% (SE = 0.1%) used cannabis in the past 30 days. The reasons for use were 36.7% (SE = 0.5%) recreation only, 36.4% (SE = 0.5%) medical and recreation, and 26.9% (SE = 0.4%) medical only. Cannabis use was significantly greater in areas where it was legal for medical and recreational use, but among those who used it, reasons for its use were not significantly associated with legal status. Among those who used cannabis in the past 30 days, using it for recreation only versus medical reasons only was significantly greater in the youngest age group, men, NH Blacks, never married, employed, students, college/technical school graduates, binge drinkers, never smokers, and non-obese and in the years 2020–2021 (vs. 2018–2021). Using it for both medical and recreational reasons versus medical reasons only tended to show similar results. Among those who used cannabis in the past 30 days, the mean number of days of cannabis use was 6.8 (SE = 0.3) days greater for those who used it for medical and recreational reasons vs. recreation only and 5.7 (SE = 0.3) days greater for those who used it for medical reasons only vs. recreation only, after adjusting for several potential confounders. Mean number of days of cannabis use varied significantly across the levels of several variables, including chronic disease status, in the adjusted model. Of those who used cannabis in the past 30 days and had arthritis, asthma, CHD, COPD, depression, diabetes, a heart attack, kidney disease, or cancer, less than half used it for medical purposes only. Conclusions: Cannabis use is more common in areas where it is legal for medical and recreational use, but legal status is not significantly associated with reasons for use. Those who use cannabis for medical purposes use it more often than those who use it for recreation only. Reasons for cannabis use vary by the levels of several variables, including chronic disease status. Less than half of those with a chronic disease use it solely for medical purposes. Full article

Authentic, situated learning experiences which mirror the collaborative nature of healthcare practice are essential in preparing students for their future professions. Feedback literacy may be thought of as the understanding, capacity, and disposition needed to make sense of information and use it to enhance work or learning strategies. This study explored how feedback literacy can be developed through situated, interprofessional peer-to-peer feedback within a community-based paediatric health screening programme. Using an exploratory Action Research qualitative design, the planning activities stage explored current practice, gathering student insights via interviews, reflections, and a workshop to co-design an Interprofessional Feedback Conversation Guide (IPFCG). The IPFCG was piloted, integrating structured feedback tools and protected time for peer exchange, within the community screening activity. Feedback regarding use of the IPFCG contributed to the gathering data stage, which was followed by the evaluation and reflection stage. Evaluation revealed four key themes: value, engagement, optimising relationships, and structuring conversations. Students valued receiving feedback from peers outside their discipline, actively engaged with the process, emphasised the importance of building rapport, and utilised structured dialogue. These findings highlight how authentic, field-based learning can foster feedback literacy, enhancing the development of professional identity. The interprofessional nature of the program reflects the complexity of modern healthcare and demonstrates how curriculum-integrated models of authentic learning can enhance student engagement and workplace readiness. This study contributes to the evolving conversation about embedding authenticity in higher education and offers a practical model for building collaborative communication within situated learning experiences at scale. Full article

Generative Artificial Intelligence (GAI) is rapidly transforming higher education by introducing new mechanisms for supporting the development of advanced cognitive processes and research-related capabilities. This study examines how postgraduate students employ GAI to develop their cognitive and research talent, conceptualized here as higher-order academic skills such as analysis, synthesis, and critical reasoning, across six domains: literature review, theoretical development, research design, data analysis, academic writing, ethical use, and challenges encountered—signaled explicitly rather than listed line by line. We administered a validated multidimensional scale to 214 postgraduate students, and the results indicate a moderate overall use of GAI, with notably high involvement in practices that emphasize ethics and responsibility. Students reported clear cognitive benefits in tasks involving information processing, linguistic refinement, and conceptual clarification while showing caution toward delegating higher-order analytical or theoretical reasoning to AI systems. Key challenges included limited institutional training, concerns about data privacy and academic integrity, and difficulties evaluating the originality and reliability of AI-generated content. Inferential analyses indicated significant differences based on gender, academic level, and general technology proficiency, whereas no differences emerged across age groups, departments, or specializations. Overall, this study demonstrates how GAI can contribute to the development of higher-level cognitive skills and research competencies, with “moderate use” operationalized as consistent but selective engagement across domains, while underscoring the need for structured training, clear guidelines, and teaching approaches that foster the responsible and effective incorporation of AI within postgraduate research. The results highlight practical implications for higher education, including the importance of institutional training programs, governance frameworks for responsible AI use, and pedagogical models that foster critical engagement with GAI. Full article

The pedagogical efficacy of artificial intelligence (AI) technologies in education heavily depends on cultural, technological, and cognitive contexts. Prior studies examined AI-driven learning outcomes without accounting for cultural variability or sufficiently anchoring their analyses in robust theoretical frameworks. The current study discusses the interconnection between AI technologies, learner competencies, and educational outcomes, in addition to the significance of digital and media literacy in secondary foreign language teaching. It employs Hofstede’s cultural dimensions theory, the technology acceptance model, and sociocultural learning theory to examine how AI technologies affect learning outcomes of English as a foreign language among Lebanese high school students. One hundred and eighty high school students in Mount Lebanon were given a 20-item survey using a quantitative research design. The results were analyzed using statistical tests and analyses in SPSS version 27.0.1. The findings indicate that AI technologies significantly enhance student learning outcomes: affective and motivational outcomes (45%), social and collaborative competencies (35%), and English language proficiency (accounting for 43% of variance). Furthermore, these relationships are strongly moderated by digital and media literacy, which increases the beneficial effects of AI on learning outcomes. The findings also show that students’ opinions, engagement, and acceptance of AI-supported language learning are influenced by cultural traits. Full article

Prosocial behavior supports social cohesion and legal order. Drawing on attachment theory and emotional socialization theory, this study examined whether peer attachment promotes prosocial behavior through positive legal emotion, defined as affective identification with and respect for law, and whether social exclusion moderates this pathway. A cross-sectional survey was conducted with 401 Chinese university students. Measures assessed peer attachment, positive legal emotion, social exclusion, and prosocial behavior. Gender and parental education were included as control variables. Correlational and regression analyses were conducted, followed by conditional process modeling using Hayes’ PROCESS Model 60 with 5000 bootstrap resamples to test mediation, moderation, and moderated mediation effects. Peer attachment significantly and positively predicted prosocial behavior. Positive legal emotion partially mediated the relationship between peer attachment and prosocial behavior. Social exclusion significantly moderated the association between peer attachment and positive legal emotion. The indirect effect of peer attachment on prosocial behavior through positive legal emotion became stronger as social exclusion increased. Moderated mediation analyses further confirmed that the indirect effect intensified at higher levels of social exclusion. Positive legal emotion represents an important psychological mechanism linking peer relationships to prosocial outcomes, while social exclusion functions as a key contextual boundary condition. Interventions that strengthen peer support, enhance legal emotional engagement, and reduce social exclusion may promote prosocial and legal socialization among university students and contribute to broader social integration and stability. Full article

Confidence in mathematics is a key factor for academic success, being influenced by emotional, behavioral, and technological aspects. The integration of digital tools, such as GeoGebra, has shown potential to promote engagement and develop mathematical skills. This study investigates how affective and behavioral engagement, confidence in the use of technology, and the perception of GeoGebra use relate to and contribute to explaining the confidence in mathematics of future teachers. The sample comprised 54 undergraduate students in Basic Education from a higher polytechnic institution. Participants engaged in learning activities involving real functions of a real variable using both traditional methods and GeoGebra. Data were analyzed using partial least squares structural equation modeling. The results indicate that behavioral engagement positively influences affective engagement, which, in turn, enhances confidence in mathematics. Confidence in the use of technology also has a positive effect on confidence in mathematics. The perception of GeoGebra use significantly influences behavioral engagement and confidence in the use of technology, but not affective engagement. These findings highlight the importance of the critical integration of digital technologies in mathematics education and emphasize the need to design pedagogical strategies that promote active participation and strengthen future teachers’ confidence in using technological tools. Full article

Academic functioning in university students emerges from the interplay of motivational, self-regulatory, emotional, and contextual processes. The present study examined the network structure linking academic motivation, self-regulated learning, academic engagement, academic burnout, generalized anxiety, self-esteem, and students’ ratings of instruction. Participants were 530 university students from Western Romania (Mage = 28.86, SD = 9.75; 87.5% women). Data were collected through an online cross-sectional survey using validated self-report instruments. A Gaussian Graphical Model was estimated using the EBICglasso procedure to examine the unique associations among the study variables and their relative structural importance within the network. The results indicated a moderately dense psychological network, with academic burnout emerging as the most structurally central node. Intrinsic motivation toward achievement, identified regulation, and performance control were positioned within the adaptive core of the network, whereas burnout, anxiety, amotivation, and low self-esteem clustered within the maladaptive region. Academic engagement occupied an intermediary position linking motivational and self-regulatory processes. Overall, the findings support a systems-oriented interpretation of academic functioning, suggesting that burnout represents a key convergence point in students’ psychological functioning, while self-determined motivation and self-regulated learning may serve as protective processes. These results highlight the value of network analysis for identifying psychologically meaningful intervention targets in higher education. Full article

Classroom humor is an important instructional strategy that enhances teaching effectiveness and improves student engagement. However, its automatic detection remains challenging due to the strong contextual dependency and implicit semantic shifts that characterize humorous expressions in teaching discourse. Conventional pretrained language models capture global semantics but often fail to focus on the subtle humor anchors that trigger incongruity. To address this issue, we propose T-HumorAGSA, a cognitive-inspired classroom teacher humor language detection model. The model employs BERT for contextualized semantic encoding, followed by a Gated Anchor-Guided Self-Attention (AGSA) mechanism that adaptively amplifies anchor-related features responsible for humor generation. A bidirectional gated recurrent unit (BiGRU) layer is further integrated to model long-range temporal dependencies within teaching utterances. T-HumorAGSA is evaluated on five datasets, including SemEval 2021 Task 7-1a, ColBERT, CCL2018, CCL2019 and the self-constructed teacher humor language dataset (T-Humor), demonstrating consistently strong performance. For instance, it achieves 0.9874 F1 on ColBERT and 0.9508 F1 on SemEval 2021 Task 7-1a, both outperforming the best baseline models. On the T-Humor dataset, the model attains a high F1 score of 0.9895, validating its capacity to detect subtle humorous cues in instructional discourse. The results demonstrate that the proposed model delivers excellent performance in classroom humor detection. Full article

English for Academic Purposes (EAP) courses primarily aim to cultivate academic communication, yet English-only norms and exam-oriented histories often discourage bilingual participation. This qualitative study traced Chinese graduate students’ language-belief development over one semester in a graduate EAP course and examined how the instructor mediated that process. Data included two rounds of open-ended surveys in two intact classes (N = 40), two interview rounds and end-of-semester reflections from ten purposively selected focus students (n = 10), and video-recorded classroom observations of 12 lessons. Findings show that the students increasingly legitimized bilingual participation and reframed English learning from test preparation toward academic communication. Beliefs nevertheless remained layered. Many still upheld an English-only ideal, treated English as the default language, and positioned the first language (L1) mainly as support when second language (L2) expression became difficult. Endorsement also exceeded uptake, with L1 use treated as a compensatory fallback rather than a co-equal academic resource. Instructor policy, conceptual framing, and interactional modeling reduced anxiety around bilingual moves and sometimes supported greater willingness to attempt more English, which identifies mechanisms for bilingual-aware EAP pedagogy in monolingual-leaning EFL contexts. Full article