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Using longitudinal data of children in Nepal, this study examines the relationships between dropouts, grade repetition and the causes of dropout, such as entrance age, to derive concrete recommendations to improve the basic education completion rates in Nepal, one of the targets of Sustainable Development Goal 4 (SDG4).

The data for this study were drawn from school records kept in the target schools, home visit surveys and interviews with principals and teachers. After comparing the repetition experience and the frequency of repetitions between graduates and dropouts, the study analysed the dropout patterns of individual children. The study then examined the effects of repetition and entrance age on children’s schooling.

The results identified a large group of children who dropped out of school without any repetition, with only 15.9% of the children repeating a grade immediately before dropping out. The findings indicated that harsh family environments, being overage and low educational expectations were factors in sudden dropout.

Collecting longitudinal data is difficult in developing countries, and few studies use such data. The question of access to education has been re-ignited by the COVID-19 pandemic; this study used longitudinal data to thoroughly analyse children’s dropout patterns and provide specific recommendations that will contribute to the achievement of SDG4.

Adolescents with harmful sexual behaviors (AHSB) who drop out of treatment are more likely to continue offending than are those who complete treatment; therefore, it is important to identify factors that heighten the risk of dropout, so they can be detected early. The purpose of this paper is to present the predictors of treatment dropout derived from a community sample of AHSB in New Zealand.

Pretreatment data on 100 males (aged 12–16) in community-based treatment for harmful sexual behavior were analyzed. Data on 50 adolescents who dropped out were matched by age and ethnicity to 50 adolescents who completed treatment. Pretreatment variables were identified using the Estimate of Risk of Adolescent Sexual Offence Recidivism. The degree to which these variables influenced treatment dropout was tested using logistic regression.

Compared to those who completed treatment, adolescents who dropped out were more likely to have a prior history of personal victimization, to deny or minimize their behavior, to have been mandated to attend treatment and to have engaged in noncontact offences.

Screening for a prior history of personal victimization, denial or minimization, mandated treatment and noncontact offences may facilitate the prediction of dropout risk more confidently. Addressing these pretreatment risk variables has the potential to improve treatment completion rates.

To the best of the authors’ knowledge, this paper is the first to highlight treatment dropout predictors in a New Zealand community sample.

This study aims to analyze to what extent distance education is feasible and efficient with the limited technological infrastructure in Egypt. The study answers this question from the perspective of households' preparedness level regarding possessing information and communication technologies (ICTs). In addition, it assesses whether the pattern of students' ICT ownership is influenced by disability- and socioeconomic-based inequality in education and whether the pattern of ICT ownership exacerbates such biases.

A three-stage probit model with double sample selection (PMDSS) was applied to estimate the factors likely to influence ICT possession, considering the selection process for school enrollment and education continuation. The authors utilized nationally representative data from the Egypt Labor Market Panel Survey 2018.

About 40% of students aged 12–25 did not have ICTs. Most socioeconomically poor households, particularly those living in Upper Egypt, were the least likely to obtain ICTs and rely on distance education. In addition, female students, particularly those with disabilities, had the lowest chance of benefitting from distance learning.

The persistent structural deprivation of school enrollment and educational progression has led to the positive selection of well-off children in education, which is extended to ICT possession and internet use. Without addressing these structural biases, the study suggests that distance education will likely exacerbate educational inequalities.

The study analyzed the extent to which Egyptian families were prepared in 2018 regarding ICT possessions for distance education for their children, particularly those with disabilities. Furthermore, it investigated whether access to distance learning was influenced by disability- and socioeconomic-based inequalities in education.

First-generation students (FGS) experience numerous challenges during their studies because of their background attributes and lack of social capital required to navigate institutions of higher learning. The purpose of this study was to investigate the influence of perceived social support and a sense of belonging on academic persistence among FGS at a distance e-learning institution in South Africa.

A cross-sectional quantitative research approach was employed. The sample comprised 240 undergraduate students. While multiple linear regression analysis was used to assess the influence of perceived social support and sense of belonging on academic persistence, mediation analysis was used to test for the mediating effect of sense of belonging on the relationship between perceived social support and academic persistence.

The results revealed that while perceived family support, perceived friendship support and perceived support from others did not significantly predict academic persistence, a sense of belonging significantly predicted academic persistence. The results also demonstrated that a sense of belonging significantly mediated the relationship between perceived family support and academic persistence. Further, the sense of belonging significantly mediated the relationship between perceived support from others and academic persistence.

The results of the study imply that distance learning institutions should strengthen student support mechanisms and institute steps to create learning environments that engender belongingness to enable students to persist academically and reach their academic goals.

This study outlines practical strategies that distance learning institutions could use to enhance support and bolster belongingness among students to help them complete their studies.

Social avoidance disorders can be disruptive to the individual as they begin to over-manage their lives, at times avoiding social and work commitments. A potential therapeutic approach is exposure therapy and a virtual reality (VR) treatment approach, gameChange VR, has been developed. This provides an opportunity to test their fears in virtual environments. This study aims to investigate the feasibility of using this VR approach to treat people with social avoidance problems within a secure mental health setting.

Nine participants completed the gameChange VR sessions. Participants received 30-minute VR sessions for up to six weeks using selected scenarios at five levels of difficulty. Patients’ comments and behaviours were noted and clinical staff also provided feedback on their observations of patients’ use and experience with VR.

Overall, patients felt that the gameChange VR helped them to build their confidence and reduce their overall anxiety as they became more comfortable with the equipment. Both patients and staff generally found the intervention easy to use and the staff reported an overall high level of engagement among the participants. The key issues raised largely related to technical and safety issues.

While a previous study was carried out using gameChange VR with an outpatient cohort, this was the first using an inpatient group in a secure mental health setting. It has demonstrated that the intervention is viable in this setting, although further studies are required to identify the specific patient population that would benefit optimally from the therapy.

This study aims to assess the prevalence of burnout among undergraduate medical and health science students and to identify the factors associated with its development.

A cross-sectional study was conducted involving students from Ras Al Khaimah Medical and Health Science University. The Maslach Burnout Inventory-Student Survey was used to evaluate three subscales of burnout. Ethical considerations were met, and informed written consent was obtained from participants. Bivariate and multiple logistic regression analyses were conducted to examine the relationship between demographic, personal and school environment factors and burnout.

Out of 453 students surveyed, the majority were females (74.8%) aged 18–20 years (60%). Significant levels of burnout were reported, with emotional exhaustion, depersonalization and reduced personal accomplishment affecting over half of the participants (69%). Factors such as being in the clinical years, long-distance commuting challenges and involuntary college selection were identified as predictors of burnout.

While the study provides valuable insights, limitations may include the specific context of the university and potential biases in self-reported data.

The findings underscore the importance of raising awareness, maintaining work-life balance and implementing coping strategies to support student well-being and foster a positive learning environment. Suggestions include time management and seeking guidance from mentors or mental health professionals.

This study contributes to the understanding of burnout among medical and health science students, especially within the unique context of the United Arab Emirates, given the convergence of students from diverse nationalities and cultural backgrounds.

Through the review of several journal articles on the adoption of information and communication technologies (ICTs) and how it impacts students’ motivation to continue with their studies or to drop out of their academic program, this study aims to review the literature on the impact of ICTs on student motivation at a university.

This paper is based on a systematic literature review steered by the PRISMA guidelines. This paper uses both Durban University of Technology subscription-based and publicly available papers. The research articles examined were published between 2018 and 2023 in Scopus, Web of Science and ScienceDirect.

Reviewed literature bespeaks that ICTs can increase student motivation by enhancing interactive, engaging and individualized learning. Digital technologies that engage students and offer a more engaging learning environment include instructional apps, online simulations and multimedia content. Using ICTs may be useful in lowering university dropout rates.

The systematic review yielded valuable insights for both academic research and real-world applications in education regarding the Durban University of Technology. The study offers a comprehensive analysis of the nexus between ICTs and student motivation.

This article explores the education crisis in Cameroon, focusing on the personal experiences of teachers and the strategies they employ to reduce school dropout during the transitional phase from primary to secondary education.

Based on a teacher training programme, 31 interviews with primary and secondary school teachers were conducted. An exploratory research design with an inductive thematic approach was used to identify themes and establish code categories. This design allowed for flexibility in investigating contextual crisis factors by eliciting interpersonal stories and exploring the dynamics of each case.

Three types of dropout prevention strategies – either standalone or mixed – are identified, depending on network, opportunity or service factors. Network-based strategies are considered the most effective and popular, as school dropout is largely influenced by students’ social backgrounds, and teachers remain key players in community life. Lies and fictional narratives, through opportunity- or service-based strategies, create dishonesty and unrealistic expectations regarding final education outcomes. Each type of strategy has significant limitations due to the highly fragmented education system in the country.

The article reveals that school dropout prevention in Cameroon is hampered significantly by education disorganisation. Teachers’ individual practices are insufficient to convey the actual value of education and reverse dropout decisions made after careful consideration. A national pedagogy plan is needed to ensure continuity.

This study aimed to use enrollment information including demographic, family background and financial status, which can be gathered before the first semester starts, to construct early prediction models (EPMs) and extract crucial factors associated with first-year student dropout probability.

The real-world samples comprised the enrolled records of 2,412 first-year students of a private university (UNI) in Taiwan. This work utilized decision trees (DT), multilayer perceptron (MLP) and logistic regression (LR) algorithms for constructing EPMs; under-sampling, random oversampling and synthetic minority over sampling technique (SMOTE) methods for solving data imbalance problems; accuracy, precision, recall, F1-score, receiver operator characteristic (ROC) curve and area under ROC curve (AUC) for evaluating constructed EPMs.

DT outperformed MLP and LR with accuracy (97.59%), precision (98%), recall (97%), F1_score (97%), and ROC-AUC (98%). The top-ranking factors comprised “student loan,” “dad occupations,” “mom educational level,” “department,” “mom occupations,” “admission type,” “school fee waiver” and “main sources of living.”

This work only used enrollment information to identify dropout students and crucial factors associated with dropout probability as soon as students enter universities. The extracted rules could be utilized to enhance student retention.

Although first-year student dropouts have gained non-stop attention from researchers in educational practices and theories worldwide, diverse previous studies utilized while-and/or post-semester factors, and/or questionnaires for predicting. These methods failed to offer universities early warning systems (EWS) and/or assist them in providing in-time assistance to dropouts, who face economic difficulties. This work provided universities with an EWS and extracted rules for early dropout prevention and intervention.

Many studies have examined pre-diploma students' backgrounds and academic performance with results showing that some did not achieve the expected level of competence. This study aims to examine the relationship between students’ demographic characteristics and their academic achievement at the pre-diploma level using machine learning.

Secondary data analysis was used in this study, which involved collecting information about 1,052 pre-diploma students enrolled at Universiti Teknologi MARA (UiTM) Pahang Branch between 2017 and 2021. The research procedure was divided into two parts: data collecting and pre-processing, and building the machine learning algorithm, pre-training and testing.

Gender, family income, region and achievement in the national secondary school examination (Sijil Pelajaran Malaysia [SPM]) predict academic performance. Female students were 1.2 times more likely to succeed academically. Central region students performed better with a value of 1.26. M40-income students were more likely to excel with an odds ratio of 2.809. Students who excelled in SPM English and Mathematics had a better likelihood of succeeding in higher education.

This research was limited to pre-diploma students from UiTM Pahang Branch. For better generalizability of the results, future research should include pre-diploma students from other UiTM branches that offer this programme.

This study is expected to offer insights for policymakers, particularly, the Ministry of Higher Education, in developing a comprehensive policy to improve the tertiary education system by focusing on the fourth Sustainable Development Goal.

These pre-diploma students were found to originate mainly from low- or middle-income families; hence, the programme may help them acquire better jobs and improve their standard of living. Most students enrolling on the pre-diploma performed below excellent at the secondary school level and were therefore given the opportunity to continue studying at a higher level.

This predictive model contributes to guidelines on the minimum requirements for pre-diploma students to gain admission into higher education institutions by ensuring the efficient distribution of resources and equal access to higher education among all communities.