Search Results (168)

Identifying when and which adolescents are at increased risk of developing problematic social media use (PSMU) is critical for effective prevention and early intervention. Previous research has examined risk and protective factors using theory-driven (confirmatory-explanatory) approaches, such as regression models. However, few studies have simultaneously considered personal, peer, and parent characteristics to assess their relative contributions, and none have explored how these factors are structurally interrelated using data-driven (inductive–exploratory) approaches. To address these gaps, this study combines logistic regression and psychological network analysis to examine which personal, parent, and peer factors are most relevant in identifying at-risk/problematic social media use among adolescents. Using three waves of data analyzed cross-sectionally from N = 2441 secondary school students, adolescents were classified as normative (0–1 symptoms) or at-risk/problematic (2+ symptoms) users based on the Social Media Disorder Scale. Logistic regression showed that fear of missing out, impulsivity, depressive symptoms, intensity of meeting with friends, and reactive parental rules uniquely predicted at-risk/problematic use. Psychological network analysis identified self-esteem, attention problems, impulsivity, depressive symptoms, and life satisfaction as central, highly interconnected nodes. These findings show that theory- and data-driven approaches illuminate different aspects of PSMU risk, and that network analysis can generate novel hypotheses about underlying processes. Full article

To make predictions, one can use machine learning and/or knowledge-based approaches. Knowledge-based approaches focus on developing systems with reasoning capabilities to solve application problems. Traditionally, statistical techniques have been used, while more recently, machine learning techniques have been used to make predictions. Both types of techniques are based almost exclusively on the analysis of historical data. This paper proposes a model that combines knowledge engineering and intelligent data analysis, leveraging the causal relationship between a past event and its known consequences. By determining the similarity between a current analogous situation and the past event, the model infers what the consequences of the current situation might be. The main contribution is the combination of various knowledge engineering techniques to improve the prediction outcomes for certain events. The present approach not only relies on analysing historical data but also integrates smart data utilization, the identification of the most similar past event, and the prediction or definition of cause–effect rules based on causal inference. One use case is presented: predicting the percentage of students who are promoted to the next grade with all subjects passed over the four years of middle school. Applying statistical regression techniques, a predicted value of 68.67% was obtained. Applying the proposed model, a value of 62.85% was obtained. The actual value published by the Spanish Department of Education for the 2021–2022 school year was 63.95%. The prediction using statistical techniques deviated 7.3% from the actual value. The proposed method deviated only 1.7% from the actual value. The proposed method improved the prediction compared to the value obtained using statistical techniques. Full article

Background: Health issues among school-age children have emerged as a global public health concern. These conditions often do not occur in isolation but tend to cluster, indicating a widespread issue of multimorbidity among this population. This study examined the prevalence and clustering of multimorbidity among school-aged school students in the Yangzhou district. Methods: A repeated cross-sectional analysis was conducted from 2019 to 2024, including 22,512 students aged 6–18 years. Common diseases, under national key monitoring, including myopia, dental caries, obesity, elevated blood pressure, and growth disorders, were assessed. Multimorbidity patterns were identified using association rule mining (Apriori algorithm) with predefined thresholds (support ≥ 2.0%, confidence ≥ 20.0% and lift > 1). Results: The multimorbidity prevalence among school-age students in the Yangzhou district is 53.95%. The most frequent multimorbidity was found in dental caries and myopia, while the most common ternary pattern was found in obesity, dental caries, and myopia. The following gender differences were observed: boys had a higher multimorbidity prevalence (56.4%) compared to girls (51.2%), with boys more likely to exhibit obesity and dental caries, while girls showed a higher prevalence of myopia-related multimorbidity. By educational stage, primary school students showed a multimorbidity rate of 50.3%, junior high showed a rate of 54.6%, and senior high showed a rate of 57.9%, indicating a rising trend across age groups. Patterns of multimorbidity varied but were interrelated. Conclusions: From 2019 to 2024, the prevalence of multimorbidity among school-aged children in Yangzhou remained relatively high, primarily manifesting as co-occurring myopia and other health issues. Patterns of multimorbidity across gender and educational stage varied but were interrelated. Full article

This article introduces the Third Space of Indian child welfare to theorize how Indigenous nations simultaneously engage and disrupt settler legal systems while building sovereign, care-based alternatives. Drawing from legal analysis, Indigenous political thought, and sociohistorical synthesis, I trace the historical continuity from boarding schools to today’s foster care removals, showing how child welfare operates as a colonial apparatus of family separation. In response, Native nations enact governance through three interrelated strategies: strategic legal engagement, kinship-based care, and tribally controlled family collectives. Building on Bruyneel’s theory of third space sovereignty, Simpson’s nested sovereignty, and Lightfoot’s global Indigenous rights framework, I conceptualize the Third Space as a dynamic field of Indigenous governance that transcends binary settler logics. These practices constitute sovereign abolitionist praxis. They reclaim kinship, resist carceral systems, and build collective futures beyond settler rule. Thus, rather than treating the Indian Child Welfare Act (ICWA) as a federal safeguard, I argue that tribes have repurposed ICWA as a legal and political vehicle for relational governance. This reframing challenges dominant crisis-based narratives and positions Indigenous child welfare as the center of a “global Indigenous politics of care” with implications for theories of sovereignty, family, and abolitionist futures across disciplines, geographies, and social groups. The article concludes by reflecting on the broader implications of the Third Space for other Indigenous and minoritized communities navigating state control and asserting self-determined care. Full article

Climate adaptation in urban environments is often constrained by rigid institutional rules and fragmented governance, which limit inclusive and context-specific planning of public spaces such as schoolyards. This study addresses this challenge by examining how collaborative planning can transform schoolyards, from asphalt-dominated, monofunctional spaces into green, climate-resilient community assets. The research employed the Institutional Analysis and Development framework within a qualitative case study design. Two public schools in the San Cristóbal de los Ángeles neighbourhood of Madrid served as case studies, with data collected through document analysis, participant observation, and interviews with municipal officials, urban planners, educators, and community members. Results indicate that the collaborative planning process reshaped rules in use, expanded the network of actors, and transformed decision-making processes. Existing rules were flexibly reinterpreted to allow new uses of space. Children, teachers, and residents became co-producers of the public space, expanding the governance network, where new deliberative practices emerged that improved coordination across people and organisations. These institutional changes occurred without formal regulatory reform, but with the reinterpretation of the game’s rules by each organisation. Thus, schoolyards can serve as laboratories for institutional innovation and participatory climate adaptation, demonstrating how urban experiments have the potential to catalyse not only physical transformations but also transformations in urban management. Full article

The present study aimed to examine the relationship between multiple risk exposures in family and school settings and the depressive symptoms of Chinese students in early adolescence living in the ethnic regions of Yunnan and Hebei, China, via different multiple risk analytic approaches. A total of 2940 students (47.3% females) in grades 4 to 9 (Mage = 12.08, SD = 2.04) from ethnic minority counties in Yunnan and Hebei participated in the survey. The participants completed the questionnaires and reported family risk, school risk, depressive symptoms, and demographic information. The cumulative risk model and the individual multiple risk models with logistic regression/association rule mining were used to examine the effects of cumulative risk, the relative contributions of individual risks, and combinations of multiple risks. We found that (1) when a cumulative risk model was used, the associations between family cumulative risk and school cumulative risk on depressive symptoms were significant, but the cross-domain interaction effect was not significant. (2) The results of logistic regression indicated that high levels of family conflict, low levels of family cohesion, low levels of teacher support, and low levels of peer support were significantly correlated with a high risk for depression. (3) The results of association rule mining revealed meaningful associations between multiple risk factor combinations and depressive symptoms. In conclusion, the use of association rule mining enhanced the analyses and understanding of the effects of multiple risk exposures. Interpersonal stressors in family and school settings need to be addressed in depression prevention and intervention programs for adolescents. Full article

The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) highlights the need for innovative, engaging educational approaches in secondary education. This study presents the design and classroom implementation of a robotics-based lesson aimed at introducing core AI and ML concepts to ninth-grade students without prior programming experience. The intervention employed two low-cost, 3D-printed robots, each used to illustrate a different aspect of intelligent behavior: (1) rule-based automation, (2) supervised learning using image classification, and (3) reinforcement learning. The lesson was compared with a previous implementation of similar content delivered through software-only activities. Data were collected through classroom observation and student–teacher discussions. The results indicated increased student engagement and enthusiasm in the robotics-based version, as well as improved conceptual understanding. The approach required no specialized hardware or instructor expertise, making it easily adaptable for broader use in school settings. Full article

(1) Background: The increasing use of information and communication technologies (ICT) in educational environments has introduced new challenges related to digital safety and sustainability. Teacher mediation and institutional initiatives are pivotal for preventing and managing Internet-related risks. This study investigates teacher and school mediation strategies for online risk prevention, analysing differences across educational settings and stages in Spain to inform inclusive digital safety practices. (2) Methodology: a quantitative study was conducted using a cross-sectional survey design involving 550 elementary and secondary school teachers from both mainstream and special education schools. (3) Results: Most schools implement intervention plans to mitigate risks associated with students’ Internet use, although the approach to these plans varies according to educational stage and school setting. Teachers employ strategies such as setting classroom rules and supporting students with online challenges, with secondary school teachers and those in mainstream schools tending to adopt more comprehensive or conversation-based prevention strategies. (4) Conclusions: Teachers and schools play a crucial role in ensuring digital safety and sustainability. Future efforts should strengthen digital skills, foster responsible online behaviour, and build inclusive, flexible learning environments according to the differing needs observed across stages and school settings. Full article

This study examines the psychological well-being (PWB) of lower secondary school students in Bangkok’s Secondary Educational Service Area Offices (SESAO) 1 and 2, using data mining techniques to analyze key influencing factors and develop a culturally adapted PWB questionnaire. The research framework is based on six components: autonomy, environmental mastery, personal growth, positive relationships, life purpose, and self-acceptance. Data were collected from 2543 students in the 2023 academic year and analyzed using the Waikato Environment for Knowledge Analysis (WEKA) program and the JRip rule-based classification model. Results indicate that personal growth is the most predictive in the classification performance of PWB, followed by positive relationships and life purpose. A newly developed PWB questionnaire was tested for reliability, with the Supplied Test Set (80:20) method yielding strong performance metrics, including accuracy (90.18%), precision (69.00%), recall (90.90%), and F-measure (78.40%). This study demonstrates data mining’s effectiveness in identifying factors influencing adolescent PWB within the Thai context. The findings provide educators and policymakers with insights for fostering student well-being and contribute to research by offering a validated, culturally relevant assessment tool. Full article

Geometry problem understanding (GPU) is a fundamental task in machine intelligence for problem-solving, requiring more accurate and complete information extraction than general natural language understanding tasks. This paper proposes a knowledge and semantic fusion method to achieve high-quality, interpretable, and scalable GPU. It extracts text-level and knowledge-level entities and relationships from problem texts and transforms them into a semantic knowledge graph. First, a dual-layer semantic-enhanced knowledge ontology model (SGKO) tailored for the geometry domain is constructed. By separating the ontology and data layers and combining the strengths of both the knowledge system type ontology and the semantic network type ontology, it enables bidirectional association between conceptual-level knowledge and object-level textual data. Second, a dynamically generated modular relationship matching template is introduced, which is decomposed into reusable atomic components and dynamically assembled through knowledge base queries, significantly reducing template quantity while enhancing adaptability to complex text structures. Additionally, a state-machine-based semantic information extraction model (IDIM-T) is designed that achieves efficient and interpretable semantic extraction through categorized relationship description types. This is combined with a rule-based method (IDIM-K) to complete knowledge-level entity relationship extraction. To validate the method, a dataset was constructed from authoritative sources, including past middle school exam questions, textbooks, and exercise books, covering unary, binary, and ternary relationships, as well as single-clause, cross-clause, and multi-relationship conjunction expressions. Experiments on 230 problems with complex relational descriptions showed that the proposed method achieved fully accurate two-level relationship parsing for 91.87% of the problems. Compared with four baseline methods (sentence template-based, Bi-LSTM-based, Transformer-based, and $S^2$-based), the method achieved the highest F1 score (0.974) for 1832 relationships, outperforming the highest F1 score (0.900) of the baselines. Full article

Technology-assisted emotion analysis opens new possibilities for the early identification of risk behaviors that may impact the well-being of university students, contributing to the creation of healthier, safer, and more proactive educational environments. This pilot study aimed to design and develop a technological prototype capable of analyzing students’ emotional states and anticipating potential risk situations. A mixed-methods approach was adopted, employing qualitative methods in the ideation, design, and prototyping phases and quantitative methods for laboratory validation to assess the system’s accuracy. Additionally, mapping and meta-analysis techniques were applied and integrated into the chatbot’s responses. As a result, an educational technological innovation was developed, featuring a chatbot structured with a rule-based dialogue tree, complemented by an ontology for knowledge organization and a pre-trained artificial intelligence (AI) model, enhancing the accuracy and contextualization of user interactions. This solution has the potential to benefit the educational community and is also relevant to legislative stakeholders interested in education and student well-being, institutional leaders, academic and well-being coordinators, school counselors, teachers, and students. Full article

The rapid development of technology sets its own rules for adults and children. For younger schoolchildren, acquiring digital skills from primary school will give them the confidence to apply them correctly in school, at university, and in their lives. Schools should be interested in providing the necessary conditions to develop children’s digital skills. Teachers can equip them with the basic skills needed to live successfully in the digital age by teaching them digital literacy skills. They can help children develop their digital skills and move consciously in the digital environment. The development of digital literacy in primary school students and the role of information and communication technologies (ICT) teachers in this development are considered relevant and timely in the article. The study examines the vision and experiences of Kazakhstani primary school computer science teachers in developing students’ digital skills in informatics classes. The article discusses research methods such as questionnaires, interviewing ICT teachers, observation, and participation in computer science lessons to better understand the actual situation in primary schools in the Republic of Kazakhstan. The study’s results will be helpful for schools and are suggested for improving computer science curricula. Full article

Background: The Delis–Kaplan Executive Function System (D-KEFS) Tower Test (TT) is a widely used neuropsychological tool that assesses complex executive functions, including planning, cognitive flexibility, inhibition, switching, and impulsivity—key abilities often impaired in individuals with frontal dysfunction. Aims: There is currently no normative data for the D-KEFS TT in the Greek population. Consequently, it cannot be effectively used to detect executive dysfunction in neurological and psychiatric populations or for research purposes. Methods: The study sample included 249 healthy adults (28.9% male, 71.1% female) aged 20 to 85 years (M = 46.53, SD = 17.41), with educational levels ranging from secondary school graduates to master’s degree holders. Pearson correlation was used to examine the relationship between age and D-KEFS TT performance, while chi-square test assessed the effects of education and gender. Normative data were then derived from raw scores and converted into percentiles. Results: Norms were established for the following D-KEFS TT variables: Total Achievement Score, Total Rule Violations, Move Accuracy Ratio, Mean First Move Time, and Time-per-Move Ratio. Age was the strongest predictor of performance, and normative data were stratified accordingly for the Greek adult population. Conclusions: This study introduces the D-KEFS TT as a neuropsychological assessment tool for Greek adults across different age groups to evaluate complex executive functions throughout the lifespan. Unlike other D-KEFS tests, the TT had not previously been adapted for the Greek population. This study is the first to provide normative data, supporting its use in clinical practice and research. Full article

Background: The Healthy, Hunger-Free Kids Act was passed in 2010 to update nutrition standards in the National School Lunch and Breakfast Programs to be in accordance with evidence-based guidelines. In 2017 and 2020, the United States Department of Agriculture proposed flexibilities to the nutrition standards for milk, whole grains, and sodium. Objective: This study examines the positions used by stakeholders in support for or opposition to the proposed rules. Methods: We conducted a longitudinal qualitative content analysis of public comments posted to the U.S. Federal Register in response to the USDA’s proposed rules in 2017 and 2020. All public comments submitted by individuals and organizations were analyzed (n = 7323, 2017 and n = 2513, 2020). Results: Results indicated three categories of arguments: (1) comments favoring the original law, (2) comments favoring flexibilities, and (3) other. In both comment periods, constituents opposed the implementation of flexibilities, while schools and manufacturers/industry predominately supported them. Academic and advocacy organizations opposed the original proposed change (2017) but relaxed their position in 2020. Conclusions: Any flexibility to the required nutritional standards of school meals has the potential to affect the health trajectory of youth. It is imperative to understand how stakeholders view this issue and inform policy change. Full article

To ensure children’s safe independent mobility on the road, they need to learn basic traffic rules. In this case, traffic rule education in a realistic environment through a play-way method can be a significant learning strategy. This research focuses on the “Traffic Park (TP)”, which provides that opportunity. Specifically, this research examined how elementary school children’s knowledge of traffic rules and behavioral intentions are influenced by their experiences of using and playing in a TP before and after they start school. Children from four different elementary schools (grades 1~6) were surveyed in Toyohashi City, Japan using a web-based questionnaire survey. Structural equation modeling (SEM) was applied to analyze the effect of TP use experience on children’s awareness of traffic rules and behavioral intentions. Three distinct SEM models were tested to measure the effect pattern of children’s TP use experience on their traffic rule awareness and behavioral intentions. The results show that TP use experience before entering school has a statistically significant effect on children’s traffic rule awareness (β = 0.16, p < 0.004; model 1) and behavioral intention improvement (β = 0.09, p < 0.07; model 2). However, TP use experience after entering school was found to have no significant effect. Finally, children’s TP use experience indirectly improves their behavioral intentions (β = 0.74, p < 0.001; model 3) by improving their awareness of traffic rules. Overall, the findings of this study highlight the contribution of TPs in promoting children’s safe independent mobility and fostering the development of sustainable child-friendly cities worldwide. Full article