Search Results (50)

School dropout is a global issue that compromises individual and societal well-being. Researchers in psychology emphasize that dropout often results from a prolonged erosion of bonds between individuals, schools, and society, especially in socioeconomically disadvantaged contexts. School trust, described as the “connective tissue” within the school system, fosters psychological well-being and is associated with self-esteem, self-efficacy, life satisfaction, and reduced depression. This study aimed to explore the interaction of various relational constructs related to school life, which could be used to improve student well-being and reduce the risk of dropout. A total of 645 high school students from impoverished and high-crime neighborhoods in Naples were involved in the cross-sectional study, investigating the role that school trust plays in relation to positive teaching, self-efficacy, and the sense of belonging. The results indicate that positive teaching significantly enhances the sense of school belonging through the mediating role of students’ trust in teachers. These findings highlight the crucial role of trust as a mediator in strengthening student–school relationships. Schools should prioritize fostering trust by promoting teacher transparency, consistency, and care. Such efforts can enhance students’ sense of belonging, ultimately mitigating dropout risk and restoring their connection with education. This systemic approach is especially vital in contexts with significant socioeconomic challenges. Full article

Sexual violence among school-aged girls is a global health problem. Research has shown that school-aged girls experience high rates of sexual violence that often go unreported. In Kenya, one in three girls experiences sexual violence before the age of 18. Sexual violence against girls can prevent them from safely attending school and cause health issues that affect school performance. This qualitative study explored community and environmental factors associated with sexual violence against school-aged girls in Kitui County, Kenya. A total of 25 in-depth interviews were conducted with key stakeholders from Kitui South Sub County. The stakeholders were from six sectors, including the police, health, education, community, religious, and criminal justice sectors. The data were analyzed using conventional content analysis to gain an understanding of the stakeholder’s perspectives and knowledge relating to sexual violence against school-aged girls. Stakeholders frequently identified the criminal justice system, culture and traditional beliefs, and threats to well-being as barriers to reporting sexual violence offenses. Girls who experience sexual violence often contend with shame from the community, and the effects of stigma include loss of resources, additional violence, poorer marriage prospects, unplanned pregnancies, school dropouts, or abandonment. Perpetrators often threatened or bribed the families of girls who experienced sexual violence with gifts or monetary incentives to prevent them from reporting the crime to local authorities, while the criminal justice system itself presents numerous challenges for victims. The reporting of sexual violence could be increased by focusing on intervention strategies that challenge attitudes, norms, and behaviors rooted in gender inequality. By addressing the underlying causes of stigma and inequality, we can create a safer environment for school-aged girls to report sexual violence and seek justice. Full article

The phenomenon of student dropout in higher education presents significant challenges for students, higher education institutions, governments, and society. The present study focuses on the dropout rates within the engineering programmes at one school of engineering in Mexico. This study uses a quantitative approach with a non-experimental cross-sectional design. Exploratory, descriptive, and correlational analyses of historical data from the University Information and Administration Integral System were performed. A logistic regression model was applied to assess the influence of various demographic, academic, and socioeconomic factors on the likelihood of student dropout. The results show some predictive variables, namely, Gender, Displaced students from home, High school GPA, and Mathematical skills. In conclusion, the group of students identified as the most likely to drop out comprised males who were studying very far away from home, who studied in a private high school in a general programme (not technological), and who presented lower grades in math. Since most dropouts were identified in the first two semesters, students who perform poorly in these semesters and have the former characteristics could benefit from special attention. Full article

Community schools represent a transformative approach to addressing systemic inequities in public education by integrating academic, social, and health services to create equitable learning environments. This study investigated the long-term impact of community school programming at an urban elementary school on middle school academic outcomes and college readiness indicators. Utilizing a quasi-experimental design with rigorous inclusion criteria and propensity score matching, the researchers minimized the bias from baseline group differences to enhance the internal validity. The key findings indicate that students who attended the community school demonstrated significant increases in grade point average over time and were less likely to exhibit high school dropout risk factors compared to a demographically matched comparison group of students who did not attend a community school. A higher proportion of the community school students met college readiness benchmarks, underscoring the sustained impact of community school programming. These results align with the existing literature on the potential of community schools to mitigate academic disparities and highlight the importance of integrating holistic support into educational strategies. By demonstrating a robust methodological approach, this study contributes valuable evidence to guide policymakers and practitioners in scaling and optimizing community school models to advance educational equity and excellence. Full article

It is estimated that one in three students drop out of university by the end of the first year of study. Dropping out of university has significant consequences, not only for the student but also for the university and for society as a whole. A total of 1.154 Italian freshmen were involved in this study and were divided based on their intention to dropout from university. The intention to dropout was assessed using five questions, and motivation was assessed through the Academic Motivation Scale. Differences in socio-demographic factors, extra-curriculum activities, academic characteristics, and academic motivation between freshmen with low and high dropout risks were assessed for highlighting potential intervention for limiting dropout rates. The majority of the freshmen were female, from low-income families, had attended high school, and lived out of town; the most represented field of study was health professions. The results indicate that the variables increasing the likelihood of belonging to the high dropout risk group are as follows: unsatisfactory relationships with lecturers/professors and fellow students, low income, amotivation, and extrinsic motivation. This study underlines the importance of adopting new teaching approaches that include spaces and time dedicated to fostering relationships, supporting academic success, and promoting the psychosocial well-being of students. Full article

High student dropout rates are a critical issue in Somaliland, significantly impeding educational progress and socioeconomic development. This study leveraged data from the 2022 National Education Accessibility Survey (NEAS) to predict student dropout rates using supervised machine learning techniques. Various algorithms, including logistic regression (LR), probit regression (PR), naïve Bayes (NB), decision tree (DT), random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN), were employed to analyze the survey data. The analysis revealed school dropout rate of 12.67%. Key predictors of dropout included student’s grade, age, school type, household income, and type of housing. Logistic regression and probit regression models highlighted age and student’s grade as critical predictors, while naïve Bayes and random forest models underscored the significance of household income and housing type. Among the models, random forest demonstrated the highest accuracy at 95.00%, indicating its effectiveness in predicting dropout rates. The findings from this study provide valuable insights for educational policymakers and stakeholders in Somaliland. By identifying and understanding the key factors influencing dropout rates, targeted interventions can be designed to enhance student retention and improve educational outcomes. The dominant role of demographic and educational factors, particularly age and student’s grade, underscores the necessity for focused strategies to reduce dropout rates and promote inclusive education in Somaliland. Full article

Background/Objectives: Premature birth can lead to socio-emotional, behavioural and executive problems that impact quality of life and school performance in the long term. The aim of this pilot study was to evaluate the feasibility and efficacy of a 12-week computerised training called Vis-à-vis to enhance these competencies in a cohort of very preterm (VPT) children aged 6 to 9. Methods: This pilot randomised controlled trial included 45 children born before 32 gestational weeks. Socio-emotional, behavioural and executive competencies were evaluated at three time points using computerised tasks, neuropsychological tests and questionnaires. Results: Among the eligible VPT children, 20% (n = 45) accepted to be part of the study, and 40% (n = 18) dropped out. Finally, 60% (n = 27) of the enrolled participants completed the study. Results showed a significant improvement in emotion knowledge and recognition immediately after the completion of the training. Conclusions: Overall, our results indicate that the implementation of this type of computerised training is feasible, but the overall compliance is unsatisfactory given the high dropout rate. Nevertheless, the positive effect of the training on emotion recognition encourages further exploration of these kinds of interventions to prevent adverse consequences in children born too soon. Full article

Transitioning from high school to university can be a challenging time for students, associated with uncertainty and stress, in part resulting from the vast number of subjects to choose from. Research has shown positive associations between intrinsic motivation and student well-being. Considering the detrimental roles that students’ stress and possibly dysfunctional coping strategies can play regarding general well-being, we investigate relationships between these constructs. Motivation for enrollment in a study program was analyzed in n = 201 first- and higher-semester students with regard to different facets of motivation. Part of the freshmen sample (n = 40) completed an additional follow-up survey in their second semester, expanding on stress and coping strategies. Cross-sectional results showed different patterns of intercorrelation among the motivational facets, but no significant differences between first- and higher-semester students. Longitudinally, only motivation based on social influences decreased over the course of the first semester. Motivation did not prove to be a suitable predictor for retrospectively judged stress during the first semester, but intrinsic motivation, especially, showed encouraging connections to some coping strategies. The findings can be used to improve student well-being and reduce dropout rates, as well as to design suitable marketing strategies for universities. Full article

This study was conducted to examine the effect of psychoeducation on attitudes toward violence and risky behaviors among refugee adolescents. This was a randomized controlled experimental study conducted with refugee adolescents (n = 101) studying in a high school in southern Turkey. After psychoeducation, it was determined that there was a significant decrease in the prevalence of antisocial behaviors, alcohol use, suicidal thoughts, unhealthy eating habits, and school dropout thoughts among adolescents according to the subdimensions of the risky behavior scale. Psychoeducation was found to be effective in reducing attitudes toward violence and preventing risky behaviors among refugee adolescents. Full article

The dropout rate in underdeveloped and emerging countries is a pressing social issue, as highlighted by studies conducted by The Organization for Economic Co-operation and Development. This study compares five feature selection techniques to address this challenge and improve the automatic detection of dropout risk. The methodological design involves three distinct phases: data preparation, feature selection, and model evaluation utilizing machine learning algorithms. The results demonstrate that (1) the top features identified by feature selection techniques, i.e., those constructed through feature engineering, proved to be among the most effective in classifying student dropout; (2) the F-score of the best model increased by 5% with feature selection techniques; and (3) depending on the type of feature selection, the performance of the machine learning algorithm can vary, potentially increasing or decreasing based on the sensitivity of features with higher noise. At the same time, metaheuristic algorithms demonstrated significant precision improvements, but there was a risk of increasing errors and reducing recall. Full article

Machine learning (ML) has become integral in educational decision-making through technologies such as learning analytics and educational data mining. However, the adoption of machine learning-driven tools without scrutiny risks perpetuating biases. Despite ongoing efforts to tackle fairness issues, their application to educational datasets remains limited. To address the mentioned gap in the literature, this research evaluates the effectiveness of four bias mitigation techniques in an educational dataset aiming at predicting students’ dropout rate. The overarching research question is: “How effective are the techniques of reweighting, resampling, and Reject Option-based Classification (ROC) pivoting in mitigating the predictive bias associated with high school dropout rates in the HSLS:09 dataset?" The effectiveness of these techniques was assessed based on performance metrics including false positive rate (FPR), accuracy, and F1 score. The study focused on the biological sex of students as the protected attribute. The reweighting technique was found to be ineffective, showing results identical to the baseline condition. Both uniform and preferential resampling techniques significantly reduced predictive bias, especially in the FPR metric but at the cost of reduced accuracy and F1 scores. The ROC pivot technique marginally reduced predictive bias while maintaining the original performance of the classifier, emerging as the optimal method for the HSLS:09 dataset. This research extends the understanding of bias mitigation in educational contexts, demonstrating practical applications of various techniques and providing insights for educators and policymakers. By focusing on an educational dataset, it contributes novel insights beyond the commonly studied datasets, highlighting the importance of context-specific approaches in bias mitigation. Full article

Despite promising outcomes in higher education, the widespread adoption of learning analytics remains elusive in various educational settings, with primary and secondary schools displaying considerable reluctance to embrace these tools. This hesitancy poses a significant obstacle, particularly given the prevalence of educational technology and the abundance of data generated in these environments. In contrast to higher education institutions that readily integrate learning analytics tools into their educational governance, high schools often harbor skepticism regarding the tools’ impact and returns. To overcome these challenges, this work aims to harness learning analytics to address critical areas, such as school dropout rates, the need to foster student collaboration, improving argumentation and writing skills, and the need to enhance computational thinking across all age groups. The goal is to empower teachers and decision makers with learning analytics tools that will equip them to identify learners in vulnerable or exceptional situations, enabling educational authorities to take suitable actions that are aligned with students’ needs; this could potentially involve adapting learning processes and organizational structures to meet the needs of students. This work also seeks to evaluate the impact of such analytics tools on education within a multi-dimensional and scalable domain, ranging from individual learners to teachers and principals, and extending to broader governing bodies. The primary objective is articulated through the development of a user-friendly AI-based dashboard for learning. This prototype aims to provide robust support for teachers and principals who are dedicated to enhancing the education they provide within the intricate and multifaceted social domain of the school. Full article

The progress of technology has played a crucial role in enhancing various fields such as education. Universities in Saudi Arabia offer free education to students and follow specific admission policies. These policies usually focus on features and scores such as the high school grade point average, general aptitude test, and achievement test. The main issue with current admission policies is that they do not fit with all majors, which results in high rates of failure, dropouts, and transfer. Another issue is that all mentioned features and scores are cumulatively calculated, which obscures some details. Therefore, this study aims to explore admission criteria used in Saudi Arabian universities and the factors that influence students’ choice of major. First, using data mining techniques, the research analyzes the relationships and similarities between the university’s grade point average and the other student admission features. The study proposes a new Jaccard model that includes modified Jaccard and approximated modified Jaccard techniques to match the specifications of students’ data records. It also uses data distribution analysis and correlation coefficient analysis to understand the relationships between admission features and student performance. The investigation shows that relationships vary from one major to another. Such variations emphasize the weakness of the generalization of the current procedures since they are not applicable to all majors. Additionally, the analysis highlights the importance of hidden details such as high school course grades. Second, this study employs machine learning models to incorporate additional features, such as high school course grades, to find suitable majors for students. The K-nearest neighbor, decision tree, and support vector machine algorithms were used to classify students into appropriate majors. This process significantly improves the enrolment of students in majors that align with their skills and interests. The results of the experimental simulation indicate that the K-nearest neighbor algorithm achieves the highest accuracy rate of 100%, while the decision tree algorithm’s accuracy rate is 81% and the support vector machine algorithm’s accuracy rate is 75%. This encourages the idea of using machine learning models to find a suitable major for applicants. Full article

School dropout is a risky behaviour that is a threat to well-being in adolescence. This study aimed to analyse school satisfaction and self-efficacy in school activities in a sample of adolescents attending secondary school in an Italian region at high risk of school dropout. The objective was to investigate whether differences exist among students on the basis of school dropout intention, gender, and career choices. Another aim was to identify, among the students’ satisfaction variables, the main psychosocial predictors of dropout intention. Students (N = 1340) attending secondary schools in Sardinia completed Soresi’s questionnaires on life satisfaction and self-efficacy. The data were analysed with a multivariate analysis of variance and logistic regression analysis. The results indicated that students intending to drop out of school scored lower on satisfaction with perceived support and satisfaction with peer and teacher relationships than their peers not at risk of dropping out. The logistic regression analysis showed that the most significant predictors of dropout intention were academic performance, satisfaction with the school experience, satisfaction in the relationships with teachers and with family members, and satisfaction with perceived support (26.9% of model variance). The results of this research thus indicate which areas could be addressed through prevention to improve well-being conditions in education. Full article

The COVID-19 pandemic caused a reduction in vaccination coverage for all age groups, especially in non-infant age. The main objective of the present study is to evaluate the effectiveness of an online intervention conducted among adolescents during the COVID-19 pandemic in increasing knowledge and positive attitudes toward vaccinations. The study, which took place online from March to May 2021, involved 267 students from six lower secondary schools in Palermo city (Italy); they filled out the questionnaire before and after the intervention. The questionnaire was based on the protection motivation theory (PMT), which estimates the improvement in vaccination-related knowledge and attitudes. The pre- and post-intervention comparison showed a significant increase in the perception of the disease severity: strongly agree pre-intervention n = 150 (58.6%) and post-intervention n = 173 (67.6%, p < 0.001), rated on a five-point Likert scale. In a multivariate analysis, the factor associated with the improvement in the score after the intervention was the school dropout index (low vs. very high dropout index OR 4.5; p < 0.03). The educational intervention was more effective in schools with lower early school leaving rates, an indirect index of socio-economic status. The topic of vaccination has caught the adolescents’ attention, it is, therefore, important that interventions tackling teenagers are tailored to reduce their emotional tension about the perception of adverse effects and improve vaccination coverage. Full article