Higher Risk Of Dropout Research Articles

Resources, norms, and dropout intentions of migrant students in Germany: the role of social networks and social capital

ABSTRACT This study focuses exclusively on migrant students in German higher education and examines the relationship between their social networks, social capital, and dropout intention. We identify two different forms of social capital – resources and norms – that students derive from their networks, and are related to their educational outcomes. These two forms of social capital also correspond with the two well-established arguments for explaining immigrants’ educational outcomes: (1) immigration optimism and (2) social background disadvantages. Using data from the German National Education Panel Study, we use Lin (2001) concepts of ‘capital deficit’ and ‘return deficit’ to examine the differences in access and utilization of social capital between native and migrant students, further differentiated by whether one or both parents were born abroad. Using linear and logistic regressions we find that the networks of migrant students are characterized by high norms and aspirations, which have a shielding role for dropout. Further, students with both parents born abroad are at a higher risk of dropout than mixed background students. We specifically focus on factors that are unique to migrant students going beyond the well-established social background explanations for migration-related disadvantages.

DEVELOPMENT OF AN EXPERIMENTAL WEB PLATFORM FOR COACHING ACTIVITIES IN UNIVERSITIES

The classic educational process organized in universities becomes quite rigid in the current pandemic context, which claims especially online activities, with students who increasingly feel the need to be guided and helped. In this respect, the students have to be supported through tutoring and mentoring activities, dedicated mainly to 1 st -year students with a high risk of dropout. The tutoring, coaching, and mentoring activities take time and the related effort for the tutor is quite high. On the other hand, the need to benefit from such activities is certain, requiring the identification of proper ways for students to have access to counseling activities, in which the effort of the counselors is not overwhelming. A viable environment would be the setting-up and the maintaining of a dedicated web platform, specially designed for such purposes. This platform facilitates coaching activities for a larger number of users, compared to face-to-face meetings. Also, the students can be counseled on topics that do not directly involve the educational process but are associated with it. This paper aims to summarize the current theoretical perspectives concerning online coaching in higher education, as a support strategy for students who experience difficulties, to prevent early school leaving, highlighting the characteristics and effective methodological approach related to this process

Mental Health Applications for Primary and Secondary Prevention of Common Mental Disorders: Attitudes of German Employees.

Background: Web-based and mobile mental health applications for the prevention and treatment of common mental disorders (CMDs) are on the rise. Under certain circumstances they have proved to be effective for a range of conditions (e.g., depression).Objective: There is not sufficient evidence regarding the benefits and barriers especially for mobile phone apps and for programs in the field of primary prevention. Studies on the acceptance of potential users of mental health apps yielded mixed outcomes. In a large survey we investigated the attitudes of employees toward mental health apps and various traditional mental health services. Our main research question in this contribution focuses on the acceptance of apps compared to other measures and the moderating influence of individual characteristics.Methods: The standardized survey was completed by members of an online access panel with different job types. A set of 33 self-developed items, including three questions on e-health, captured the perceived relevance of prevention at the (A) occupational, (B) individual, and (C) societal level. On the basis of an exploratory factor analysis, mean scores for mapping seven (sub-)dimensions were constructed and compared using the Wilcoxon test. The influence of potential predictors was analyzed in linear regression models.Results: The data of 610 respondents were analyzed (response rate 75%). Support from mental health applications was rated significantly less important compared to all other dimensions at the levels (A) to (C). Respondents were more likely to use mental health apps if they felt literate with electronic devices, perceived a high relevance of work-related demands as causal factors for CMDs, stated they would be ashamed of having a CMD, and would be willing to begin psychotherapy if recommended.Discussion and Conclusions: The results confirm the critical attitudes of potential mental health app users found in other studies. Since users with a negative attitude toward e-health might have a higher risk for dropout and non-adherence as well as lower intervention effects, well-designed educational strategies should be carried out beforehand.

From the Editor’s Desk...

From the Editor’s Desk...

Somato-Functional Profile of Children at Risk of Educational and Social Exclusion in Western Romania

This paper aims to identify the somatic and functional profile of middle-school students from three areas of Romania (South-West, West and North-West). A group of 364 students (173 girls and 191 boys) aged between 10 and 16 years (12.75 ± 1.2) from 6 different schools in Mehedinti, Caras-Severin and Bistrita-Nasaud counties took part in this study. The target group category includes Roma ethnic students, students from disorganised families and students at high risk of early school dropout, from both urban and rural areas. The calculated statistical indicators were: arithmetic mean, standard deviation, coefficient of variation and Pearson coefficient. The anthropometric measurements made to identify the somatic and functional profile of students were height, weight, BMI, arm span and Ruffier test. The results show an upward trend in the height of boys aged between 10 (1.44 m) and 16 years (1.67 m), the maximum value recorded for girls aged 14 years being 1.59 m. The highest value of body weight was identified in boys aged 15 years (58.91 kg) and girls aged 14 years (52.63 kg). The highest value for arm span was found for 15-years-old boys (167 cm) and 14-years-old girls (158.83 cm). BMI values for both boys and girls were between 17.22 and 21.52 points. In Ruffier test, the scores obtained by students ranged between 9.95 and 13.4. This research is part of the “Sustainable social and education integration through sport activities” project (PNP001).

Factors associated with early and later dropout from methadone maintenance treatment in specialist addiction clinics: a six-year cohort study using proportional hazards frailty models for recurrent treatment episodes

BackgroundRetention in methadone maintenance treatment (MMT) is associated with reduced illicit drug use, criminal activity, and mortality; however, many clients move in and out of MMT. This study aims to identify determinants of time to dropout of MMT across multiple treatment episodes in specialist addiction services in Ireland. MethodsCohort study of persons attending specialist addiction clinics between 2010 and 2015. MMT episodes were periods of continuous treatment if there were no interruptions to treatment lasting > 7days. Proportional hazards frailty models were used to assess factors associated with time to dropout from recurrent MMT episodes at 3 (90 days) and 12 months (91-365 days). MMT episodes were right- censored at time of death, transfer to prison or primary care, and study end. ResultsA total of 2,035 individuals experienced 4,969 MMT episodes, with 2,724 dropout events during the six-year follow-up. Factors associated with dropout at 3 months included low dose methadone (<60 mg/day) (HR = 1.49, 95% CI 1.29-1.73) and previous dropout (HR = 1.65, 95% CI 1.41-1.92). Adherence was protective (HR = 0.91, 95% CI 0.90-0.92). Dropout at 12 months was associated with low dose methadone (HR = 1.44, 95% CI 1.23-1.68), previous dropout (HR = 1.37, 95% CI 1.16-1.61), males (HR 1.26, 95% CI 1.06-1.50), benzodiazepines (HR = 1.22, 95% CI 1.03-1.45) and number of comorbidities (HR = 1.12, 95% CI 1.05-1.20); adherence was protective (HR = 0.86, 95% CI 0.84-0.87). ConclusionsClients with a previous history of treatment dropout and those on low dose methadone should be identified as high risk for both early and later dropout. Inversely, adherence to treatment, not missing methadone doses, is protective.

Why patients stop using their home telehealth technologies over time: Predictors of discontinuation in Veterans with heart failure

BackgroundDaily use of home telehealth (HT) technologies decreases over time. Barriers to continued use are unclear. PurposeTo examine predictors of drop-out from HT in Veterans with heart failure. MethodsData for Veterans with heart failure enrolled in the Veterans Affairs HT Program were analyzed using a mixed effects Cox regression model to determine risk of dropping-out over a 1-year period. FindingsOlder (hazard ratio [HR] 1.01), sicker (prior hospital readmission [HR 1.39]), higher probability of hospital admission/death [HR 1.23], functional impairments [1.14]) and white Veterans (compared to black; HR 1.41) had higher risk of drop-out in HT Programs. Users of VA's online patient portal (HR 0.90) had lower risk of drop-out. DiscussionOlder and sicker patients are at most risk of stopping HT use, yet use of a patient portal shows promise in improving continued use. Interventions targeting patients at high risk for HT discontinuation are needed to promote ongoing engagement.

A novel waitlist dropout score for hepatocellular carcinoma – identifying a threshold that predicts worse post-transplant survival

It has been suggested that patients with hepatocellular carcinoma (HCC) at high risk of wait-list dropout would have done poorly after liver transplantation (LT) because of tumour aggressiveness. To test this hypothesis, we analysed risk of wait-list dropout among patients with HCC in long-wait regions (LWRs) to create a dropout risk score, and applied this score in short (SWRs) and mid-wait regions (MWRs) to evaluate post-LT outcomes. We sought to identify a threshold in dropout risk that predicts worse post-LT outcome. Using the United Network for Organ Sharing database, including all patients with T2 HCC receiving priority listing from 2010 to 2014, a dropout risk score was created from a developmental cohort of 2,092 patients in LWRs, and tested in a validation cohort of 1,735 patients in SWRs and 2,894 patients in MWRs. On multivariable analysis, 1 tumour (3.1-5 cm) or 2-3 tumours, alpha-fetoprotein (AFP) >20 ng/ml, and increasing Child-Pugh and model for end-stage liver disease-sodium scores significantly predicted wait-list dropout. A dropout risk score using these 4 variables (C-statistic 0.74) was able to stratify 1-year cumulative incidence of dropout from 7.1% with a score ≤7 to 39.5% with a score >23. Patients with a dropout risk score >30 had 5-year post-LT survival of 60.1% vs. 71.8% for those with a score ≤30 (p= 0.004). There were no significant differences in post-LT survival below this threshold. This study provided evidence that patients with HCC with the highest dropout risk have aggressive tumour biology that would also result in poor post-LT outcomes when transplanted quickly. Below this threshold risk score of ≤30, priority status for organ allocation could be stratified based on the predicted risks of wait-list dropout without significant differences in post-LT survival. Prioritising patients with hepatocellular carcinoma for liver transplant based on risk of wait-list dropout has been considered but may lead to inferior post-transplant survival. In this study of nearly 7,000 patients, we created a threshold dropout risk score based on tumour and liver-related factors beyond which patients with hepatocellular carcinoma will likely have poor post-liver transplant outcomes (60% at 5 years). For patients below this risk score threshold, priority status could be stratified based on the predicted risk of wait-list dropout without compromising post-transplant survival.

Including adolescent migrants in school through VET approach: evidence from a pilot action in Italy

This paper investigates the effectiveness of a small experimental pilot addressed to foreign adolescents with low language skills and high dropout risk in lower secondary school in Turin, North-West Italy. The intervention aims at reducing Early School Leaving while supporting standard school curricula. In order to facilitate students’ re-motivation and their attachment to the school environment, the pilot proposes an original VET approach that alternates standard school activities to innovative vocational training. In particular, treated students substitute 290 h VET courses for 10 standard school weeks along a scholastic year. First of all, the analysis aims at evaluating effects on Early School Leaving. Then, since the pilot requires a long period in VET away from school, the study evaluates effects on educational outcomes. Oltre I Muri is a very small pilot experiment, involving seven schools and offering 11 VET positions to migrant students. We investigated the effectiveness of the program by an experimental counterfactual approach: participants have been randomly assigned to a treated and a control group with similar characteristics. They are adolescent migrants attending the 2nd year of lower secondary school and manifesting very high drop-out risk. Treated students attend the alternating VET/school pilot, while control students attend standard school classes. Randomization inference allows assessing average treatment effects, but the very small sample size does not guarantee statistical significance at standard levels. Results anyhow suggests some effectiveness of the project in contrasting Early School Leaving. In particular, while some non-treated students dropped out from school during the pilot, all treated students regularly completed their scholastic year. This can be interpreted as a 20% increase in school attendance, with an 80% confidence level assessed by Montecarlo randomization. Finally, extreme value bound techniques applied to test scores suggest that treated students do not prejudice their learning by attending VET instead of standard school classes during the pilot. On the contrary, their school attachment is reinforced.

Processi autoregolativi e feedback nell’apprendimento online

The e-learning training, both within most traditional formal education contexts and in the latest version of MOOCs, is growing. However, the limits are also highlighted: high risk of drop-out, high variability in the quality of education and learning outcomes. The commitment to further qualify e-learning training implies a redefinition of educational and evaluation models and tools that consider aspects of cognitive, metacognitive and motivational nature. Specifically it seems interesting to study the self-regulation processes which many have highlighted as one of the main success factors in the study and which we must consider as particularly critical when the regulation of learning processes is completely or almost at the expense of the subject, as typically happens in e-learning contexts. It is therefore interesting to develop tools and strategies that help students to reflect and become aware of those dimensions that are basis of self-regulating behaviour. In this way also Social Learning Analytics perspective, through the implementation of feedback tools, offers interesting solutions to improve levels of awareness and trust of students in e-learning environments.

Precision education with statistical learning and deep learning: a case study in Taiwan

The low birth rate in Taiwan has led to a severe challenge for many universities to enroll a sufficient number of students. Consequently, a large number of students have been admitted to universities regardless of whether they have an aptitude for academic studies. Early diagnosis of students with a high dropout risk enables interventions to be provided early on, which can help these students to complete their studies, graduate, and enhance their future competitiveness in the workplace. Effective prelearning interventions are necessary, therefore students’ learning backgrounds should be thoroughly examined. This study investigated how big data and artificial intelligence can be used to help universities to more precisely understand student backgrounds, according to which corresponding interventions can be provided. For this study, 3552 students from a university in Taiwan were sampled. A statistical learning method and a machine learning method based on deep neural networks were used to predict their probability of dropping out. The results revealed that student academic performance (regarding the dynamics of class ranking percentage), student loan applications, the number of absences from school, and the number of alerted subjects successfully predicted whether or not students would drop out of university with an accuracy rate of 68% when the statistical learning method was employed, and 77% for the deep learning method, in the case of giving first priority to the high sensitivity in predicting dropouts. However, when the specificity metric was preferred, then the two approaches both reached more than 80% accuracy rates. These results may enable the university to provide interventions to students for assisting course selection and enhancing their competencies based on their aptitudes, potentially reducing the dropout rate and facilitating adaptive learning, thereby achieving a win-win situation for both the university and the students. This research offers a feasible direction for using artificial intelligence applications on the basis of a university’s institutional research database.

Do Student Characteristics Affecting School Dropout Risk Differ from One School to Another?

This study assessed the predictive power of variables at school-level and student-level on the risk of school dropout within the framework of the Ecological Theory. A predictive correlational research model was used, and the participants were selected by employing a two-stage sampling procedure. Participants were 1.851 students from 30 different high schools in the central districts of Ankara studying during the 2016/17 school year. According to the findings of two-level hierarchical linear regression analysis; the average of the highest school at the risk of school dropout is almost twice that of the lowest school. The effect of the environmental risk perception on school dropout in schools with high dropout is higher than schools where the risk of dropout is low. Academic achievement, environmental security, and non-antisocial behaviors reduce the likelihood of early school leaving at the schools with a higher risk of dropout than lower-ones. The probability of dropout of upper grades in schools comprising student clubs and students with high academic achievement in schools with High School Entrance Exam success is less. It was also concluded that the effects of gender, class repetition, having a sibling who left school early, teacher participation, punishment, absenteeism and motivation may have been mediated by other variables in the regression equation.

The Effect of Confidence on Dropout Rate and Outcomes in Online Cognitive Bias Modification

Cognitive bias modification training for interpretation bias (CBM-I) trains less threat-oriented interpretation patterns using basic learning principles and can be delivered completely online. Thus, CBM-I may increase accessibility of treatment options for anxiety problems. However, online interventions are often affected by pronounced dropout rates, and findings regarding the effectiveness of CBM-I, especially when delivered online, are mixed. Therefore, it is important to identify further predictors of dropout rate and intervention outcomes. The current study uses data from an exclusively online, multi-session CBM-I feasibility trial to investigate the effects of perceived confidence in the likely helpfulness of a CBM-I program (rated at baseline) on subsequent dropout rate, and change in interpretation bias and anxiety symptoms for N = 665 individuals high in trait anxiety. Results showed that higher baseline confidence ratings predicted lower dropout rate, as expected. Partially in line with hypotheses, there was some evidence that higher confidence ratings at baseline predicted greater changes on some interpretation bias measures, but results were not consistent across all measures and fit indices. Inconsistent with hypotheses, confidence did not predict change in anxiety symptoms. Possible explanations for why the nonsignificant findings may have occurred, as well as implications of confidence in online interventions being an early predictor of high risk for dropout, are discussed.

The role of social support in school adjustment during Secondary Education.

During secondary education, a stage with a high risk of failure and school dropout, social support is an important contextual variable for the prevention of school maladjustment. The aim of this study is to examine a theoretical model of the explanatory capacity of social support in terms of school adjustment, understood as school engagement and perceived academic performance. Participants were 1,468 students (51% girls; 49% boys) from the Basque Country, aged between 12 and 17 (M=14.03, SD=1.36). The study had an ex post facto cross-sectional design. The measurement instruments used were: TCMS -teacher support subscale, AFA-R -family support and peer support subscales, SEM -School Engagement Measure, and EBAE-10 - perceived academic performance subscale. Various different structural models were tested. The first-choice model was one in which social support predicts school engagement with perceived academic performance as a mediating variable: together, both variables predict 73% of school engagement. The strongest effect was that of teacher support, followed by family support, whereas friends were not found to have any direct effect on school adjustment variables. Teachers and families should strive to offer social support to students as a means of strengthening perceived academic self-efficacy and school engagement.

Predicting Dropouts From an Electronic Health Platform for Lifestyle Interventions: Analysis of Methods and Predictors.

BackgroundThe increasing prevalence and economic impact of chronic diseases challenge health care systems globally. Digital solutions can potentially improve efficiency and quality of care, but these initiatives struggle with nonusage attrition. Machine learning methods have been proven to predict dropouts in other settings but lack implementation in health care.ObjectiveThis study aimed to gain insight into the causes of attrition for patients in an electronic health (eHealth) intervention for chronic lifestyle diseases and evaluate if attrition can be predicted and consequently prevented. We aimed to build predictive models that can identify patients in a digital lifestyle intervention at high risk of dropout by analyzing several predictor variables applied in different models and to further assess the possibilities and impact of implementing such models into an eHealth platform.MethodsData from 2684 patients using an eHealth platform were iteratively analyzed using logistic regression, decision trees, and random forest models. The dataset was split into a 79.99% (2147/2684) training and cross-validation set and a 20.0% (537/2684) holdout test set. Trends in activity patterns were analyzed to assess engagement over time. Development and implementation were performed iteratively with health coaches.ResultsPatients in the test dataset were classified as dropouts with an 89% precision using a random forest model and 11 predictor variables. The most significant predictors were the provider of the intervention, 2 weeks inactivity, and the number of advices received from the health coach. Engagement in the platform dropped significantly leading up to the time of dropout.ConclusionsDropouts from eHealth lifestyle interventions can be predicted using various data mining methods. This can support health coaches in preventing attrition by receiving proactive warnings. The best performing predictive model was found to be the random forest.

The different patterns of the dropout according to the level and the field of education

Background and aims Our research is a part of a complex dropout research, whose main aim is to examine the time changes, social and regional inequalities, and constitutional differentiation of student dropout, appearing on a large scale and causing individual and institutional loss. As a part of this, the aim of our research on one hand is to identify the training fields and training levels with high dropout risk, furthermore to draw the patterns of dropout based on the available countrywide data. Methods We analyzed the database of the Hungarian Higher Education Informational System for our research, especially considering the students starting their training, based on final exam. We examined the most important features of advancements in studies, furthermore the occurring recoils. Considering our data, the measure of dropout remarkable differs based on the training fields and schedule, training, finances and some demographically background variable as well, taking the measure of dropout and its reasons into account. During our analysis, we separated three main types of the dropout’s reasons: financial reasons, study inefficiency, and dropout caused by other reasons. Results In this study, we examine these types and their characteristics in three different training levels: Bachelor degree programs, undivided, long-cycle Master courses, and short-cycle higher education vocational training. Discussion Almost one third of the students end up joining the group of dropout in all kind of training type, especially critical period is from the second to the fifth semester. The most endangered are males, correspondent students, fee-paying students, participants of higher education vocational training, furthermore the students of science and students of IT fields.

Treatment Engagement and Outcomes of Mindfulness-Based Cognitive Therapy for Veterans with Psychiatric Disorders.

Objectives: The aim of this study was to evaluate utilization and outcomes of mindfulness-based cognitive therapy (MBCT) provided to veterans with psychiatric disorders.Design: Retrospective chart review.Settings: Veterans Administration Medical Center (VAMC).Subjects: Ninety-eight veterans with psychiatric illness who were enrolled in an MBCT class between May of 2012 and January of 2016. Subjects were predominately white (95%), male (81%), and >50 years old (74%). The most common psychiatric conditions were any mood disorder (82%) and post-traumatic stress disorder (54%).Intervention: Eight-week MBCT class.Outcome measures: Session attendance and pre- to postintervention changes in numbers of emergency department (ED) visits and psychiatric hospitalizations.Results: The average number of sessions attended was 4.87 of 8 and only 16% were present for all sessions. Veteran demographic variables did not predict the number of MBCT sessions attended. However, both greater numbers of pre-MBCT ED visits (p = 0.004) and psychiatric admissions (p = 0.031) were associated with attending fewer sessions. Among patients who experienced at least one pre- or post-treatment psychiatric admission in the 2 years pre- or postintervention (N = 26, 27%), there was a significant reduction in psychiatric admissions from pre to post (p = 0.002). There was no significant change in ED visits (p = 0.535).Conclusions: MBCT may be challenging to implement for veterans with psychiatric illness in, at least some, outpatient VAMC settings due to a high attrition rate. Possible mediation approaches include development of methods to screen for high dropout risk and/or development of shorter mindfulness-based interventions (MBIs) and/or coupling MBIs with pleasurable activities. The finding of a significant decrease in psychiatric hospitalizations from pre- to post-MBCT suggests that prospective studies are warranted utilizing MBCT for veterans at high risk for psychiatric hospitalization.

Parenting practices during middle adolescence and high school dropout

IntroductionDespite inconclusive findings, educational researchers have long considered adequate parenting practices instrumental in preventing high school dropout among adolescents. The present short-term retrospective study focuses on parenting practices during middle adolescence when dropout typically occurs. MethodsThe culturally diverse, high-risk sample of Canadian adolescents (N = 108; Mage = 16.0 years) from low-income neighborhoods included very recent dropouts and matched still-in-school students. A global score reflecting the quality of parenting practices during the period preceding dropout (or comparable period) was derived from adolescents’ answers to a well-established structured interview protocol. Transcripts of interviews were also used to identify the potentially disruptive challenges (e.g., parental incarceration) that families faced. ResultsResults show a robust relationship between current parenting practices and dropout that was not moderated by challenging family circumstances or immigration history. Descriptive findings indicate that extreme and relatively rare cases of parental neglect were associated with a high dropout risk, but that most dropouts lived in families where communication and supervision, although not entirely absent, were minimal. ConclusionOffering systematic support to parents of middle adolescents could help to prevent dropout in high-risk communities.

School-Based Group Mentoring and Academic Outcomes in Vulnerable High-School Students

This study examined the association between participation in a school-based group-mentoring program (Project Arrive) and academic outcomes in a group of ninth-grade students who had been identified as at high risk of high-school dropout ( n = 239). Comparison participants were ninth-grade students with similar levels of risk ( n = 980). Using inverse probability of treatment weighting (IPTW) with a propensity score to reduce selection bias, and a partially clustered model to account for nonindependence of data in program students, we found that Project Arrive students earned more credits by the end of 9th grade and 10th grade; and reported increased instructional time by the end of 9th grade, than comparison students.

Aberrant personality tendencies and academic success throughout engineering education.

In a longitudinal field study, we investigated the predictive associations between six aberrant personality tendencies (schizotypal, avoidant, borderline, antisocial, narcissistic, obsessive-compulsive) and academic success of science, technology, engineering, and mathematics students. Bachelor students of Industrial Engineering at a Dutch technical university (N=432, Mage =18.45; 87.3% male) filled out the NEO-PI-R and aberrant tendencies were operationalized by the five-factor model (FFM) compound technique. Indicators of academic achievement (grades) and persistence (credit points earned per year, re-enrollment, study duration) were made available by the academic office. Validities across the 3years of the study program consistently support the role of two aberrant tendencies: Individuals with high antisocial tendency reached lower academic achievement, took longer to finish their study, and had a higher risk of dropout. The obsessive-compulsive tendency was associated with higher grade-point average, faster study progress, and higher retention rates and effects were still visible while controlling for known predictors (high school grades, Conscientiousness). Contrary to our expectations, we found no evidence for inverted U-shaped relationships. We used the compound technique for aberrant tendencies based on the FFM in the academic context and our findings support the importance of personality-based psychopathological tendencies for academic success.