Search Results (172)

The literature has shown that contextual and individual factors of an emotional and motivational nature are often associated with good academic performance and regular school attendance. The present study aimed to examine indirect associations involving Academic Procrastination and School Climate in the relationship between learning behavior patterns (MO-LH) and school success in a sample of 539 Italian secondary school students (M_age = 16.5). Structural equation modeling was used to test the hypothesized relationships. The results showed that LH was positively associated with Academic Procrastination, whereas MO was negatively associated with it. Academic Procrastination was negatively associated with all dimensions of perceived School Climate (Teacher Support, Peer Connectedness, School Connectedness, Affirming Diversity, Rule Clarity, and Reporting and Seeking Help). School Achievement was positively associated with MO, School Connectedness, and Affirming Diversity, and negatively associated with Peer Connectedness. School Absence was negatively associated with MO. Mediation analyses suggested significant indirect associations between learning behavior patterns and school achievement, with Academic Procrastination and Affirming Diversity emerging as potential mediating variables. Findings highlight the importance of addressing emotional and motivational vulnerabilities and fostering a supportive school climate as potential factors associated with adaptive academic functioning and reduced maladaptive outcomes. Full article

Rapid technological changes and evolving labor market demands have increased the importance of identifying the competencies required for successful workforce participation. This study examines how digital competencies (DC) contribute to job market readiness (JMR) among university students and recent graduates in Saudi Arabia by investigating the mediating role of employability competencies (EC) and the moderating role of labor market dynamism (LMD). Drawing on Human Capital Theory and Career Construction Theory, the study develops an integrated framework explaining how individual competencies and labor market conditions jointly shape workforce preparedness. Data were collected using a stratified random sampling approach from 400 participants (248 final-year university students and 152 recent graduates) recruited from universities in Riyadh, Jeddah, Dammam, Medina, and Yanbu using a structured questionnaire. The hypotheses were tested using regression analysis and Hayes’ PROCESS macro, while confirmatory factor analysis was conducted using AMOS 27. The results indicate that DC positively influences both EC and JMR. EC also positively affected JMR and partially mediated the relationship between DC and JMR. Furthermore, LMD significantly strengthened the positive effect of DC on JMR, suggesting that digital competencies are becoming increasingly valuable in dynamic employment environments. This study contributes to the workforce preparedness literature by providing a more comprehensive explanation of how digital competencies are associated with job market readiness through employability competencies and identifying labor market dynamism as a contextual condition associated with the strength of this relationship. The findings offer implications for universities, policymakers, and students seeking to enhance employability outcomes and align workforce development initiatives with Saudi Arabia’s Vision 2030. Full article

The regular (even-aged) silvicultural system is widely used in the management of pedunculate oak forests to ensure their long-term sustainability and regeneration. This approach maintains an even-aged stand structure by harvesting one tree generation and establishing a new cohort through natural or artificial regeneration. This management simplifies cultivation and exploitation but can have an impact on biodiversity. Research was conducted in the Spačva forest complex in pedunculate oak–common hornbeam stands, including mature forest stands and stands in the seed cut and final cut stages of shelterwood regeneration. This study aimed to assess changes in the floristic composition between the forest and regeneration phases of pedunculate oak stands, and to test whether these changes are associated with shifts in microclimatic conditions, soil microbiological and chemical properties. The study was conducted in the Spačva forest complex across four sites representing regeneration and forest phases, using a total of 20 phytocoenological relevés (10 per phase). Microclimatic data were collected over two years (March 2021–March 2023), while soil chemical, microbiological, and vegetation data were analyzed to compare habitat conditions. Differences between phases were evaluated using Student’s t-tests, and relationships between diagnostic species and habitat variables were assessed using Spearman rank correlation analysis. The regeneration phase exhibited substantially higher plant species richness than the forest phase, with 84 recorded species compared with 44 in forest stands (approximately a 91% increase), corresponding to mean values of 32 and 19 species per relevé, respectively. There were 12 species with a fidelity index determined as diagnostic for the regeneration phase. Microclimatic, pedological, chemical, and biological soil properties were compared between regeneration and forest phases, and their relationships with the occurrence of 12 diagnostic species were examined to identify ecological drivers relevant to successful and sustainable forest restoration. Increased insolation resulting from canopy opening was identified as the primary factor differentiating regeneration from forest stands, promoting higher plant species richness by enhancing light availability and modifying soil chemical conditions. These findings indicate that shelterwood-induced changes in habitat structure can increase floristic diversity without adversely affecting key soil functions, highlighting the importance of integrating vegetation, soil, and microclimatic indicators into adaptive management strategies for the sustainable restoration and long-term resilience of pedunculate oak forests. Full article

Educational success is shaped by students’ social background, making parental support with homework particularly important for younger learners. Beyond instructional help, the emotional quality of this support may influence parent–child interactions and, in turn, students’ outcomes. However, little is known about the emotions that parents experience during homework support or how these emotions relate to parents’ personality traits. Therefore, the present study surveyed N = 903 parents (M_Age = 42.40, SD = 5.60; 87.4% female, predominantly mothers) of sixth-grade students using the newly developed Homework-Related Parental Emotions Scale (PES-Home) to assess parents’ emotions during homework support. In addition, parents reported personality traits of openness and extraversion as well as further individual characteristics (gender, age, marital status, number of children at home, educational attainment, and weekly working hours). The results of the exploratory and confirmatory factor analyses indicate that the PES-Home scales exhibit sound psychometric properties and reliably capture the three emotions of enjoyment, anxiety, and anger. Bivariate correlations and findings of a path model reveal significant links between parents’ emotions and their openness, extraversion, and additional individual characteristics. Specifically, higher educational attainment, more children in the household, and longer working hours are each associated with less favorable emotional experiences during homework support. Our findings highlight that research on parental homework support is still at an early stage and requires further investigation. Full article

Academically resilient students achieve high performance despite socioeconomic disadvantages. Although this population has received increasing attention, little is known about its motivational heterogeneity, a critical gap given the central role of motivation in persistence and success. Guided by Self-Determination Theory (SDT), this study examined motivational profiles among a population of academically resilient 10th-grade students in Italy (N = 15,751). Using a person-centered approach, Latent Profile Analysis (LPA) identified three profiles: a “multifaceted regulation resilient” profile (72%), marked by low amotivation and high levels across regulations; a “moderately amotivated resilient” profile (21%), with higher amotivation and lower levels of regulation; and a “strongly amotivated resilient” profile (7%), characterized by the highest amotivation and the lowest levels of regulation. Auxiliary analyses indicated that the amotivated profiles, particularly the “strongly amotivated resilient” profile, exhibited higher school dropout intentions than the “multifaceted regulation resilient” profile. Overall, although the majority of academically resilient students displayed multiple coexisting forms of regulation, a non-negligible subgroup showed significant motivational vulnerability, with amotivation emerging as a central risk factor. These findings challenge the assumption that academic resilience is sufficient to protect students from motivational disengagement and dropout risk. High academic achievement, in other words, should not be taken to imply the absence of motivational concerns. This highlights the importance of moving beyond a one-size-fits-all approach, recognizing that even within resilient populations, specific subgroups remain motivationally vulnerable and in need of tailored support. Full article

Predicting student performance has emerged as an essential element of contemporary learning assessment, allowing educational organizations to determine problematic students and offer early intellectual assistance. Many machine learning (ML) methodologies prioritize predicted accuracy at the expense of interpretability and practical insights. This paper provides a framework for predicting student performance that is both risk aware and explainable utilizing a chaotic educational competition optimizer (ECO) in conjunction with a support vector classifier (SVC) to overcome existing challenges. The ECO serves as a metaheuristic feature selection technique for selecting the most significant features from a multivariate educational dataset consisting of 1195 students and 29 behavioral, demographic, and academic characteristics. Experimental findings demonstrate that ECO effectively condenses the feature space to 11 essential indications and improves generalization of model while maintaining classification robustness. Utilizing the chosen features, the ECO–SVC model attains a complete classification accuracy of 87.03%, with F1-scores of 0.92, 0.69, and 0.82 for high-, medium-, and low-performance student categories, respectively, surpassing other benchmark ML methods. The proposed framework incorporates explainable artificial intelligence (XAI) to improve transparency by utilizing local explanations and permutation-driven feature significance. The XAI research verifies that institutional support, learner engagement, and previous academic success are the most important contributing factors to predictive results. Notably the ECO functions as a classifier-independent feature selection mechanism; however, the support vector classifier (SVC) is adopted in this study due to its strong generalization capability and effectiveness in exploiting the optimized feature space. The findings are analyzed using a semiotic-linguistic framework, wherein certain qualities are correlated with symbolic, indexical, and temporal educational signs, converting numerical significance into substantive pedagogical insights. Furthermore, an initial academic risk profile strategy is established by utilizing SVC decision confidence and elucidating feature contributors. The consequent risk ratings accurately categorize students into low-, medium-, and high-risk categories, facilitating the detection of at-risk learners beyond mere final score assessment. The proposed risk-aware and explainable ECO–SVC framework enhances learning outcomes assessment by integrating interpretability, high accuracy, and proactive academic reasoning, rendering it suitable for real-life educational decision-support systems. Full article

Background: College students’ well-being is a critical determinant of academic success, and for Native American students, cultural strengths, resilience, and community support are key in fostering persistence in higher education. Alongside these assets, health behaviors are key contributors to psychological well-being (PWB) and academic performance. This study examined how modifiable health behaviors, such as physical activity (PA) and sleep duration, relate to PWB and academic performance among Native American college students. Methods: A secondary data analysis was conducted using a nationally representative sample of Native Americans (N = 1914) from the Spring 2023 American College Health Association-National College Health Assessment (ACHA-NCHA) survey. Independent variables include meeting PA guidelines (≥150 min moderate or ≥75 min vigorous/week) and sleep duration (categorized as poor or good). The Diener Flourishing Scale measured PWB. Academic performance was measured based on self-reported cumulative grade averages. Findings: Biological sex and PA were significantly associated, χ² = 40.60, p < 0.001, with a higher proportion of males meeting PA guidelines. Students with good sleep reported higher PWB than others, F(1, 1817) = 62.08, p < 0.001. Similarly, students who met PA guidelines reported higher PWB, F(1, 1817) = 35.71, p < 0.001. Poor sleep was associated with lower odds of higher academic performance (B = −0.33, p < 0.001). Contrarily, PA was not significant (p = 0.350). PWB was positively associated with academic performance (B = 0.031, p < 0.001). Conclusions: Sleep and PWB are key factors associated with both PWB and academic performance, while PA is associated with PWB. These findings highlight the importance of relevant interventions that promote these factors to support overall well-being, academic success, and retention among Native American college students. 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

This study explores how various factors—including motivation, emotion, demographics, and institutional characteristics—interrelate to shape academic success among Greek university students. Based on Self-Determination Theory (SDT) and Tinto’s model of integration, it fills a gap in research by addressing the specific characteristics of the Greek higher education system. While prior research emphasizes the importance of motivation and integration, few studies have combined these with factors like program alignment, student type, and gender in a structural model. A sample of 284 students, aged 18–28, completed validated Greek versions of the AMS, PASS, and SACQ. Structural Equation Modeling (SEM) was used to assess both the direct and indirect effects on academic success. Key variables included gender, traditional vs. non-traditional student status, first-choice program enrollment, intrinsic and extrinsic motivation, academic and social integration, emotional adjustment, institutional attachment, and procrastination. Gender (female) was the strongest predictor of academic success (β = 0.819), affecting outcomes through intrinsic motivation, emotional adjustment, and procrastination. Academic integration (β = 0.424) and traditional student status (β = 0.300) also significantly predicted GPA. Social integration had an indirect effect through academic engagement. Procrastination (β = −0.228) and emotional maladjustment (β = −0.143) were major obstacles. While selecting a first-choice program affected institutional attachment, it did not directly impact academic performance. Conclusion: Academic success in Greek universities is influenced by a range of personal, motivational, and contextual factors. Improving integration, reducing procrastination, and fostering intrinsic motivation can boost academic outcomes. Interventions should consider gender and student pathways to be more effective. Full article

The present study aimed to examine the inter-relationships among trait-, class-, and study-related boredom, as well as their associations with self-efficacy, self-regulation, critical thinking, academic performance, and engagement among university students. The sample comprised 250 undergraduate psychology students who completed self-report measures assessing academic boredom, critical thinking, self-regulation, academic engagement, and academic performance. Using path analysis, the study revealed that academic boredom was negatively correlated with self-regulation, critical thinking, and self-efficacy. The results further revealed that academic boredom was negatively associated with both academic engagement and performance. However, class-related boredom was negatively associated with engagement but not with performance, whereas study-related boredom was negatively associated with both academic performance and engagement. These findings emphasize the importance of addressing specific types of academic boredom in higher education, as each type appears to play a distinct role in shaping students’ academic experiences and outcomes. The study also highlights the need for interventions that promote self-regulation, critical thinking, and self-efficacy as protective factors to mitigate boredom and enhance academic success. Implications for future research and university policies are discussed. Full article

As test anxiety has evolved conceptually, identifying specific components contributing to educational success is essential. This study is the first to examine how different components of test anxiety are related to university dropout. Hungarian university students were recruited through the university’s website and asked to complete a series of online questionnaires at three different points over two years to monitor test anxiety levels and potential dropout or graduation during this period. Of the 98 students who completed assessments at all time points, by the final measurement, 69 had already either graduated or dropped out of their studies. Test anxiety was measured using the multidimensional TAM-C-SF (Test Anxiety Measure for College Students—Short Form). Study dropout was defined as leaving a program before graduation. Task-irrelevant behaviors—a component of test anxiety that includes restless and avoidance behaviors—were significantly associated with dropout. Higher values of cognitive interference were also significantly associated with a higher likelihood of dropout. Task-irrelevant behaviors and cognitive interference might play a key role in academic persistence among university students. These findings highlight the importance of a multidimensional approach to assessing test anxiety and suggest interventional techniques that may help diminish these factors to support students in succeeding in their studies. Full article

Background/Objectives: Creativity has become an essential skill for children and adolescents to cope with the challenges of contemporary society. Beyond academic success, creativity is closely linked to well-being, social adjustment, and personal development. Schools, therefore, play a crucial role in creating conditions that allow students to explore ideas, express themselves, and develop socioemotional resources. This study aimed to examine how self-perceived creativity relates to educational, socioemotional, and well-being factors in Portuguese students, to identify different creativity profiles, and to explore the main variables that predict creativity. Methods: This cross-sectional study was based on secondary analyses of national data from the project Psychological Health and Well-being|School Observatory. The sample included 3011 students aged between 9 and 20 years (M = 13.62; SD = 2.53), from grades 5 to 12. Data were collected using validated instruments: the OECD Socioemotional Skills Survey (SSES), the Positive Youth Development (PYD) scale, and the WHO-5 Well-Being Index. Analyses included group comparisons, cluster analysis to identify self-perceived creativity profiles, correlation analyses, and multiple regression models. Results: Self-perceived creativity did not differ between boys and girls, but it decreased significantly with higher grade levels. Three profiles were identified: low, medium, and high self-perceived creativity. Students with higher self-perceived creativity reported better well-being, more positive relationships with teachers, a stronger sense of belonging at school, and higher parental educational levels. Self-perceived creativity was positively associated with socioemotional skills such as curiosity, sociability, and optimism, as well as with PYD dimensions and well-being. Negative associations were found with age and test anxiety. Socioemotional variables were the strongest predictors of creativity, explaining 39% of its variance. Conclusions: These results show that creativity is closely connected to students’ socioemotional development. Investing in emotional skills, supportive relationships, and positive school environments may be a powerful way to foster creativity and promote healthier, more balanced development. This has important implications for educational practice and policy. Full article

Student performance is an important factor for any education process to succeed; as a result, early detection of students at risk is critical for enabling timely and effective educational interventions. However, most educational datasets are complex and do not have a stable number of features. As a result, in this paper, we propose a new algorithm called MOHHO-NSGA-III, which is a multi-objective feature-selection framework that jointly optimizes classification performance, feature subset compactness, and prediction stability with cross-validation folds. The algorithm combines Harris Hawks Optimization (HHO) to obtain a good balance between exploration and exploitation, with NSGA-III to preserve solution diversity along the Pareto front. Moreover, we control the diversity management strategy to figure out a new solution to overcome the issue, thereby reducing the premature convergence status. We validated the algorithm on Portuguese and Mathematics datasets obtained from the UCI Student Performance repository. Selected features were evaluated with five classifiers (k-NN, Decision Tree, Naive Bayes, SVM, LDA) through 10-fold cross-validation repeated over 21 independent runs. MOHHO-NSGA-III consistently selected 12 out of 30 features (60% reduction) while achieving 4.5% higher average accuracy than the full feature set (Wilcoxon test; $p<0.01$ across all classifiers). The most frequently selected features were past failures, absences, and family support aligning with educational research on student success factors. This suggests the proposed algorithm produces not just accurate but also interpretable models suitable for deployment in institutional early warning systems. Full article

The primary objective of this study is to examine factors that influence Mobile Learning adoption and effectiveness in the case of higher education, underpinned by the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. The study was conducted at some of the top Saudi universities. A survey method was used in this study, and the questionnaire includes 12 factors, which include enjoyment, hedonic motivation, and performance expectancy, among others, whose relationships with behavioral intention towards using m-learning and in turn, its actual use, were investigated. Additionally, two mediating factors, namely technostress and exhaustion, were included to examine the effects of students’ engagement with m-learning. The questionnaire was distributed to 500 undergraduate students, and 264 replied. Based on the findings, the traditional UTAUT2 factors had significant effects on students’ intention and use behaviors, which were mediated by technostress and exhaustion. The findings indicated that the effective management of mediators is important to improve student engagement in m-learning and that m-learning strategies can be developed through the information provided. This study validated the UTAUT2 model in the case of m-learning, with a 70% success rate in predicting IT adoption, which exceeded other models. Through the study findings, educators and policymakers can find ways to optimize m-learning platforms. Furthermore, it is recommended that focus is placed on the long-term effects of technostress and exhaustion on the students’ adoption of and engagement with m-learning. Full article

As education undergoes digital transformation, ChatGPT-4 has emerged as one of the most visible tools of generative artificial intelligence. While widely discussed, its impact on student satisfaction and learning outcomes in higher education remains underexplored. This study investigates the factors that shape art and design students’ satisfaction when using ChatGPT to support coursework. Unlike previous research focusing on ChatGPT adoption behavior, this study extends the Information Systems Success Model (ISSM) to the context of art and design education. Drawing on 435 valid survey responses, we employed a mixed-methods approach. Partial Least Squares Structural Equation Modeling (PLS-SEM) was first applied to examine how system quality, compatibility, personal innovativeness, and perceived usefulness influence satisfaction directly and through mediating mechanisms. To complement this, fuzzy-set Qualitative Comparative Analysis (fsQCA) was used to identify multiple combinations of conditions that lead to high satisfaction. The findings show that compatibility, perceived usefulness, and personal innovativeness significantly enhance satisfaction, with path coefficients of 0.378, 0.342, and 0.155, respectively. Importance–Performance Map Analysis (IPMA) further highlights personal innovativeness and system quality as critical drivers. By providing both theoretical and practical insights, this study contributes to the growing body of research on generative AI in art and design education and informs the design of courses and digital learning tools. Full article