Dropout Patterns Research Articles

Socioeconomic Status as a Predictor of School Dropout in High School Students

School dropout among high school students is a complex problem that impacts communities around the globe. This article analyzes the relationship between socioeconomic status and school dropout among high school students. The methodology used was quantitative, with a hypothetical-deductive approach and a non-experimental design. A population of 270 students was identified, from which a representative sample of 200 students was selected from a public school offering extraordinary education. Two specific questionnaires were designed: the first addresses the social factors that may influence students' educational experience, and the second focuses on identifying the causes and patterns of school dropout. The results show a direct connection between the variables; applying the Pseudo R-squared, the Nagelkerke value indicates a high incidence value of socioeconomic level in school dropout, highlighting the importance of improving the conditions of these students to avoid school dropout. In conclusion, school dropout is significantly influenced by socioeconomic level highlighting the need to address this level affecting vulnerable youth to prevent school dropout.

대학 재학생들의 중도탈락 패턴 분석 및 이탈 예측: H 대학교를 중심으로

This study was conducted to analyze dropout patterns of H University students and construct a prediction model for churning of students. Keeping a high enrollment rate of students in a local university is very important for its sustainability and survival, which forced us to develop a good explainable predictive model for dropout probabilities of each student. We applied a mixed effect logistic regression model in order to predict a dropout probability based on 11 explanatory variables such as admission type of processes, first semester grades, total admission scores, category of high schools, and departments of application, etc. The constructed model tells us that department of application is a significant variable as a random effect on which fixed effects for total admission scores and first semester grades should be interpreted differently. The main finding is that in each department the proposed model was able to discriminate a high-probabilistic dropout student although they have showed a good performance in total admission scores and first semester grades. It is highly expected that our model can be effectively applied to manage the dropout rates and enhance the enrollment rates of the students.

Internet-based therapist-supported interpersonal psychotherapy for depression: A randomized controlled trial

BackgroundDepression is a common disorder for which there are several treatments options including different psychological treatments. The aim of this study was to investigate the effects of internet-based interpersonal psychotherapy (IPT) for symptoms of depression in randomized controlled trial. MethodsFollowing recruitment via advertisement a total of 113 participants with mild to moderate symptoms of depression were included and randomized to either a ten-week internet-based IPT with weekly therapist guidance or a waitlist control condition. The primary outcome was symptoms of depression measured weekly with the Montgomery Åsberg Depression Rating Scale (MADRS-S) and at pre- and post-treatment assessment with the Beck's Depression Inventory (BDI-II). Secondary outcomes were self-rated quality of life and symptoms of generalized anxiety disorder. We also measured therapeutic alliance and treatment credibility. Outcomes were evaluated with a latent growth curve model (for MADRS-S) and robust linear regression models (for the other measures). The trial was conducted during the Covid-19 pandemic in the spring of 2021. ResultsSignificant differences favoring the treatment group were found on three of the four outcomes: BDI-II, quality of life ratings, and ratings of generalized anxiety. Between-group effect sizes for these outcomes were moderate (BDI-II, quality of life) or small (generalized anxiety). The latent growth curve model did not indicate a significant difference on the weekly MADRS-S ratings. Exploratory analyses did not show an association between therapeutic alliance, treatment credibility and outcome. LimitationsMissing data at post-treatment was high in the treatment group (37 %), though the missingness was not significantly related to observed ratings at pre-treatment or estimated trajectories during the treatment. Few participants completed all modules. The Covid-19 pandemic situation may have affected both effects and dropout rates. ConclusionsInternet-based IPT can lead to significant improvements, though the reductions in symptoms of depression were not consistent across the two measures used. Completion rates and dropout patterns suggest a need for improved acceptability. Trial registrationThe trial was preregistered at Clinicaltrials.gov (Identifier: NCT04721678). Registered January 2021.https://clinicaltrials.gov/study/NCT04721678

Predictors and Patterns of Dropout From Psychiatric Treatment Among Trauma-Affected Refugees: A Large Data Pool Analysis.

Dropout from mental health treatment is a substantial hindrance to relevant and effective treatment. Despite the high prevalence of PTSD among refugees, research into their treatment dropout has received limited attention. This study aimed to identify patterns and predictors of treatment dropout versus completion through different treatment stages. The sample included 940 patients with a refugee background undergoing outpatient treatment for PTSD in Denmark. All patients were offered 10 medical doctor sessions and 16-20 psychotherapy sessions. Dropout was analysed in three stages: (1) during the first six MD sessions, (2) during the first eight psychotherapy sessions upon completion of Stage 1, and (3) during psychotherapy sessions 9 to 16. A stepwise multiple regression analysis was conducted for each stage to identify predictors of stage-specific dropout. Counter to expectations, both early dropout and full completion were associated with better symptom outcomes, relative to late-treatment dropout. Key predictors varied by stage, with younger age predicting early dropout, whereas chronic pain and poor Danish proficiency predicted late dropout. Female gender and a clearly articulated motivation for active participation were predictors for full treatment completion. Practical advice is suggested to accommodate at-risk patients and to re-evaluate patient engagement after familiarisation with treatment.

Unveiling Dropout Patterns: A Python-Powered Analysis of Student Attrition in School Education

Student dropout rates pose a significant challenge in Gujarat, India, affecting both societal welfare and economic development. This study aims to diagnose and predict student dropout patterns using data analytics and machine learning (ML) algorithms. Extensive datasets from Gujarat's educational authorities are preprocessed with Python libraries such as Pandas and NumPy, ensuring quality and consistency. Exploratory data analysis (EDA) uncovers insights and trends, while feature engineering extracts relevant characteristics for ML models. The study implements ML algorithms Random Forest (RF), Decision Trees (DT), Logistic Regression (LR), K-Nearest Neighbors (KNN), AdaBoost, XGBoost, Support Vector Machine(SVM) to construct predictive models. The findings reveal critical factors influencing dropout rates and assess model performance using model accuracy. This research provides novel insights into student dropout rates in Gujarat, offering a comprehensive, data-driven approach to address educational attrition and guide targeted intervention strategies and policy recommendations for improving educational outcomes in the region.

Analysing the dropout patterns of individual children in Nepal: relationship between school dropout and grade repetition/entrance age

PurposeUsing 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).Design/methodology/approachThe 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.FindingsThe 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.Originality/valueCollecting 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.

Higher education retention in Ireland and Scotland: the role of admissions policies

AbstractThis article provides new insights into the role played by higher education (HE) selection policies in influencing student retention by exploring drop-out patterns in Ireland and Scotland. The Irish and Scottish HE systems differ in relation to the emphasis placed on grades and subjects studied at school and to the degree of autonomy enjoyed by HE institutions in the student admissions process. We investigate whether these system differences matter for student retention using administrative data from national student records and logistic regression modelling. The findings show that in Ireland, HE students have higher chances of dropping out than in Scotland and there are inequalities in these chances among students from different social backgrounds. Moreover, the association between subject matching (but also school grades) and the chances of dropping-out is stronger in Ireland than in Scotland. We conclude that the tighter student selection criteria in Scotland improve retention and reduce social inequalities in drop-out rates. Moreover, admission criteria are found to be important not only to explain between-country differences in drop-out rates but also within-country differences among different HE institutions.

Perception on Student Dropout in Secondary Education: An Insight from University Students and Staff in Battambang Province, Cambodia

This study primarily aims to gather initial data on the quantity of four universities, Battambang province in Cambodia, with a secondary goal of characterizing student dropout patterns. The collected data was intended to inform the Cambodian government’s recent initiatives to decrease dropout rates and increase numbers of enrollment. Data was gathered from three universities and an upper secondary school, using a sample size of 120 participants who completed a questionnaire. The data was then analyzed using Microsoft Excel and the R program. The results were utilized to investigate the reasons behind student dropouts in grades 10, 11, and 12, and to devise strategies to prevent such occurrences. The study’s findings highlighted that: The correlation circle, which represents the impact of all factors contributing to student dropout, showed significant contributions from factors accounting for 10% (0.1) to 40% (0.4) of the total variances. There are varying perceptions regarding student dropout. This study helps us comprehend the key factors influencing dropout rates, which include domestic violence (DoVio), student health issues (Unhe), school violence (SchVio), parental attitudes (PaAtt), and immigration (Imm), which are still prevalent issues.

Teenage Pregnancy and Education: An Analysis of Dropout Rates in Selected Public Schools in Kabwe District, Zambia

This study used a mixed-methods case descriptive design to investigate the impact of teenage pregnancy on dropout rates in public schools in Kabwe District, Zambia. The study aimed to determine the prevalence of teenage pregnancy and its effects on dropout patterns, identify socioeconomic and cultural factors contributing to educational attrition among pregnant teenagers, assess long-term consequences of teenage pregnancy on educational trajectories, and propose evidence-based recommendations. Data collection methods included interviews, questionnaires, and document analysis. Key findings were that guidance teachers and parents can significantly reduce teenage pregnancy, but more joint efforts are needed. Detrimental impacts on schooling included dropout, delayed completion, lack of concentration, and psychological consequences. It is crucial for all stakeholders to work collaboratively in supporting young women's education and empowerment. The study provides insights into these complex issues in Zambia and recommends comprehensive multi-sectoral approaches to address teenage pregnancy and promote sustainable education.

Case series: Superficial plexus en face may aid distinction of retinal nerve fiber layer loss from diabetic retinal ischemia versus glaucoma.

This study aimed to demonstrate that the pattern and degree of capillary bed dropout in early glaucoma appear different on OCT-A superficial plexus en-face slabs compared with retinal ischemia. RNFL loss associated with retinal ischemia in diabetic patients may be explained and accounted for by overlying the RNFL deviation map on a superficial plexus en-face montage. Three middle-aged White men with diabetes mellitus showed cup-to-disc ratios of approximately 0.7 and RNFL and ganglion thinning. Each patient had several Cirrus OCT and OCT-A scans taken of the posterior pole. The OCT-A en-face images demonstrated specific patterns of superficial capillary dropout. The appearance of superficial plexus capillary dropout in one case of glaucoma is contrasted against two cases of retinal ischemia. Early glaucoma appears to be associated with incomplete capillary bed dropout that extends from macular regions to the disc in a wedge- or arc-shaped pattern. Diabetic retinal ischemia appears to be associated with well-defined patchy and polygonal pockets of complete capillary bed obliteration that may not extend back to the disc. If an RNFL deviation map is superimposed over the superficial plexus en-face montage, areas of RNFL loss may correlate with and thus be well accounted for by areas of retinal ischemia in cases with RNFL thinning likely from ischemia. This approach may supplement inspection of OCT B-scans for focal retinal thinning when trying to differentiate RNFL and ganglion cell loss from retinal ischemia versus glaucoma in patients with diabetes. Formal research studies are needed to validate our observations and proposed use of OCT-A together with OCT in these patients.

Application of the Learning Vector Quantization Algorithm for Classification of Students with the Potential to Drop Out

Universities, as providers of academic education services, are required to provide an optimal educational process for students to produce a generation of quality human beings. Student learning success is seen as the success of universities in implementing the higher education process. One of the problems universities face in maintaining the quality of education is student dropout. The high dropout rate in universities can impact accreditation assessments. As a result, it will affect the level of public trust. The number of dropouts in higher education can be minimized from an early age by analyzing the factors that cause student dropouts using data on students who graduated and those who dropped out. This data can be used to determine student dropout patterns by classifying them using the artificial neural network learning vector quantization (LVQ) approach. The data used in this research was 4053, consisting of 3840 graduate student data and 213 dropout student data. This data is considered unbalanced, an unbalanced dataset can cause errors because the model tends to classify the majority class with a high classification and pays less attention to the minority class. So, it is necessary to apply oversampling techniques to overcome this problem. The research results show that the application of the LVQ method to unbalanced data produces an accuracy value of 95.53%, a precision value of 100%, a recall value of 15.02% and an f1-score of 0.26, while the application of the LVQ method to data that has undergone resampling resulting in an accuracy value of 94.66%, a precision value of 92.22%, a recall value of 97.55%, and an f1-score value of 0.95. The LVQ method can be used to classify dropout students with excellent results.

Lessons learned from the student dropout patterns on COVID‐19 pandemic: An analysis supported by machine learning

AbstractDuring the COVID‐19 pandemic, the challenges associated with the transition from face‐to‐face to emergency remote education increased concerns about student dropout. Aligned with this concern, this study investigates the impact of the pandemic on the dropout patterns of 3371 undergraduate students from a Brazilian institution. Using data mining and machine learning techniques, we developed predictive dropout models based on student data preceding and succeeding the onset of the pandemic. Through the interpretation and comparison of these models and with the support of statistical and graphical analyses, we identify that the patterns persistently indicate that young students in their initial semesters, characterized by lower income, academic performance, and interaction, remain most susceptible to dropping out. Despite the pandemic leading to an enhanced predictive capability of data regarding student interaction within the virtual learning environment, our analysis revealed a lack of significant variation in dropout patterns. Institutionally, this indicates that a considerable number of dropouts likely encountered challenges in adapting to higher education, both before and throughout the pandemic. Practitioner notesWhat is already known about this topic The challenges posed by emergency remote learning, implemented during the COVID‐19 pandemic, may exacerbate the dropout problem and change the patterns involved in this phenomenon. Despite being widely used to identify dropout profiles and/or predict at‐risk students, data mining and machine learning techniques have been little explored in the investigation of changes associated with the pandemic context. What this paper adds We employ data mining and machine learning techniques to construct predictive and interpretable dropout models for the pre‐ and during‐pandemic contexts of a Brazilian institution. Comparing these models, we investigate the impacts of the pandemic on dropout patterns. The pandemic and the shift to emergency remote learning have an enhanced predictive capability of data regarding student interaction within the virtual learning environment. Throughout the pandemic, there was limited variation observed in dropout patterns, consistently highlighting young students in their initial semesters with lower income, academic performance and levels of interaction. Implications for practice and/or policy This study urges the inclusion of interactional student data in future dropout prediction research, capitalizing on the enhanced predictive power attained through the widespread adoption of virtual learning environments. Institutionally, the dropout patterns from before and during the pandemic suggest that students may be facing difficulties in adapting to higher education. In addition to the need to intensify preventive actions, this work indicates the need to conduct a study specifically targeting first‐semester students to understand their needs better and redesign preventive policies.

Prediction of Early Dropouts in Patient Remote Monitoring Programs

The analysis of medical data is a significant opportunity worldwide for national health systems to reduce costs and at the same time improve healthcare. The utilization of these technologies is done in the context of monitoring health issues, counting health goals, as well as for recording medical data. In such a context, early detection of users at risk of lower compliance rates and patterns of use of a health monitoring application suggesting a risk of abandonment is an invaluable opportunity to implement tailored intervention strategies aimed at recovering and avoiding abandonment thoughts. This study aims to identify patterns of early dropout in users of an application for mobile intervention, having access to a database of users who have experienced the impact of a digital monitoring application to improve their quality of life for at least 6 months. At the experimental stage, many different approaches for early dropout prediction were implemented with a different set of features. Specifically, the current study proposes a methodology using the Neighborhood Cleaning Rule and a specific classification algorithm based on the Stacked Generalization learning method to predict the early abandonment of users of the health monitoring application. The results showed that the proposed algorithm was able to predict the early dropout of users from the application with an accuracy of 97.6%, making it reliable enough to be used as an early warning system.

2b or not 2b? 2bRAD is an effective alternative to ddRAD for phylogenomics.

Restriction-site-associated DNA sequencing (RADseq) has become an accessible way to obtain genome-wide data in the form of single-nucleotide polymorphisms (SNPs) for phylogenetic inference. Nonetheless, how differences in RADseq methods influence phylogenetic estimation is poorly understood because most comparisons have largely relied on conceptual predictions rather than empirical tests. We examine how differences in ddRAD and 2bRAD data influence phylogenetic estimation in two non-model frog groups. We compare the impact of method choice on phylogenetic information, missing data, and allelic dropout, considering different sequencing depths. Given that researchers must balance input (funding, time) with output (amount and quality of data), we also provide comparisons of laboratory effort, computational time, monetary costs, and the repeatability of library preparation and sequencing. Both 2bRAD and ddRAD methods estimated well-supported trees, even at low sequencing depths, and had comparable amounts of missing data, patterns of allelic dropout, and phylogenetic signal. Compared to ddRAD, 2bRAD produced more repeatable datasets, had simpler laboratory protocols, and had an overall faster bioinformatics assembly. However, many fewer parsimony-informative sites per SNP were obtained from 2bRAD data when using native pipelines, highlighting a need for further investigation into the effects of each pipeline on resulting datasets. Our study underscores the importance of comparing RADseq methods, such as expected results and theoretical performance using empirical datasets, before undertaking costly experiments.

The healthy moms and babies app to prevent postpartum depression: analysis of user-profiles and dropout cases.

Perinatal depression affects mothers, babies and society. Preventive interventions are needed, but face barriers to access. E-health interventions could be an effective and accessible option. To date, few studies have attempted to understand the use of mobile health (m-health) applications and why they are not more widely used. This study aims to understand the demographic characteristics of enrolled participants and examine dropout patterns through the Healthy Moms and Babies app. A longitudinal study was conducted with a sample of 511 women recruited between 2020 and 2022. Data were collected from the app, including sociodemographic information, the participant's progress through the modules of the app, and the permissions granted to use the app. Out of the 511 women who completed the initial form to initiate participation, 279 downloaded the app and completed the evaluation. Results indicated that granting permission to be notified about the module's availability is related to an increase in the use of the first modules. This study shows the importance of establishing follow-ups in the use of mobile apps during the perinatal period.

Educational Environments and Secondary School Outcomes Among Students Who Are D/deaf and Hard of Hearing in Special Education.

The purpose of this study is to provide updated national estimates on the annual number, educational environments, and secondary school outcomes of students who are D/deaf and hard of hearing (D/HH) receiving special education (SpEd) and related services in the United States. We performed a retrospective cross-sectional descriptive analysis of Individuals with Disabilities Education Act, Part B, Section 618 data from 2012 to 2018. Participants included students 6-21 years old in SpEd with "hearing impairment" reported as their primary disability. The general population of students in secondary school served as a comparator, via Current Population Survey data. We described the annual number of students (a) overall, (b) by educational environment, and (c) by reason for exiting SpEd, including the proportion graduating from and dropping out of secondary school. We described variation over time. The median annual number of students was 67,655, with minimal variation by year. The proportion in general education (GenEd) for ≥ 80% of the day increased by 4.2% over 6 years from 57.8% to 62.0%, whereas the proportions in GenEd for < 40% and 40%-79% of the day decreased by 1.6% and 1.3%, respectively. Proportions in the remainder of the environments changed < 1.0% each. Of exiters, 86.8% of students graduated, whereas 3.9% dropped out, compared to a dropout rate of 5.0% in the general population. From 2012 to 2018, students who are D/HH receiving SpEd in the United States have spent increasingly more time in GenEd, most graduated from high school, and few dropped out, with dropout patterns appearing similar to the general population.

Deep Neural Network DNN Applied to the Analysis of Student Dropout

This paper reports the design of a deep neural network (DNN) focused to predict patterns of student dropout at the UPTC University of Colombia. We apply specialized artificial intelligence (AI) algorithms for the implementation of the DNN specifically were using a learning machine for classification and clustering task. Additionally, a dataset containing 17 attributes of 3000 academically active students was structured; the data set has been prepared to be trained as an entry to the neural network. The final result of the research reveals a predictive model trained through the DNN validated by several quality metrics that demonstrate reliability and accuracy of the results accomplished through the model.

Money, a Drain of Educational Opportunity: A Microregional Study of School Dropouts in Mpigi, Uganda

This paper sheds light on the causes of school dropouts, a major challenge facing UPE attainment in public primary schools of Uganda. We offer microregional analysis of school dropout patterns in the Mpigi district, and show a number of distinct patterns of school dropouts in Mpigi that can be differentiated from studies based on national data. While the Universal Primary Education program covered tuition fees, additional costs for education, such as meals, school transport, and uniforms, function as hurdles for longer school years. This is an obstacle regardless of parental awareness about education’s importance and how wealthy the parents think they are. Such findings directly conflict with existing knowledge that higher household income results in longer school years. The previous understanding of girl dropouts in Sub-Saharan Africa and its patriarchal context offer little explanations on the dropout pattern of Mpigi, as it is more related to the economic cost of education and the high demand for male agricultural labor. For a multicultural society such as Uganda, we highlight the importance of microlevel regional study in educational research.

Grit was not associated to dropout in Dutch Marine recruits

ABSTRACT Approximately half of all recruits drop out of Marine recruit training. Identifying associated and predisposing factors for dropout would be helpful to understand dropout patterns and induce preventive strategies. Grit has been suggested to be a predictor of who is likely to succeed and who is not. We aimed to investigate the association between baseline grit scores and dropout of Marine recruit training in the Netherlands Armed Forces. We performed an exploratory study using data of three platoons Marine recruit training of the Royal Netherlands Marine Corps. Individual grit levels were measured using the NL-Grit scale, including two subscales. The primary outcome of this study was successful completion or dropout of Marine recruit training. Data were available from 270 recruits, of whom 119 (44%) dropped out of training. The odds ratio for dropout were 1.01 (95% CI 0.84–1.21, p = .917) and 1.07 (95% CI 0.89–1.29, p = .481) per standard deviation increase of consistency of interests and perseverance of effort, respectively. Our study did not confirm the proposed association between baseline grit levels and dropout of Marine recruit training in Dutch Marine recruits using the NL-Grit scale.

A Joint Model for Unbalanced Nested Repeated Measures with Informative Drop-Out Applied to Ambulatory Blood Pressure Monitoring Data.

This study proposes a Bayesian joint model with extended random effects structure that incorporates nested repeated measures and provides simultaneous inference on treatment effects over time and drop-out patterns. The proposed model includes flexible splines to characterize the circadian variation inherent in blood pressure sequences, and we assess the effectiveness of an intervention to resolve pediatric obstructive sleep apnea. We demonstrate that the proposed model and its conventional two-stage counterpart provide similar estimates of nighttime blood pressure but estimates on the mean evolution of daytime blood pressure are discrepant. Our simulation studies tailored to the motivating data suggest reasonable estimation and coverage probabilities for both fixed and random effects. Computational challenges of model implementation are discussed.