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1
Wolman, Clara, Robert Bruininks, and Martha L. Thurlow. "Dropouts and Dropout Programs." Remedial and Special Education 10, no. 5 (September 1989): 6–20. http://dx.doi.org/10.1177/074193258901000504.
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Burton, Damon, and Rainer Martens. "Pinned by Their Own Goals: An Exploratory Investigation into Why Kids Drop out of Wrestling." Journal of Sport Psychology 8, no. 3 (September 1986): 183–97. http://dx.doi.org/10.1123/jsp.8.3.183.
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Previous research concludes that athletes drop out of sport because of conflicts of interest, but these findings cannot clarify whether dropouts find other activities more appealing or turn to new activities because sport fails to meet their achievement needs. This investigation assessed dropout motives by testing explanations derived from Nicholls' (1984) motivational model and comparing them with traditional dropout questionnaire responses. Wrestling coaches, participants, participants' parents, dropouts, and dropouts' parents completed a 23-item dropout inventory; and participants and dropouts responded to questionnaire items testing Nicholls' task choice predictions. Dropout inventory responses confirmed previous conflict-of-interest findings. In data testing Nicholls' model, participants demonstrated significantly higher perceived ability, better won-loss records, more functional attributions, and more positive expectancies, and valued wrestling success more than dropouts did. These findings supported predictions that wrestlers change activities when continued participation threatens their perceived ability. Disagreement between the conclusions concerning why young athletes drop out of wrestling drawn from the conflict-of-interest explanation and from Nicholls' perceived ability model are discussed, and suggestions for reducing dropout rates are offered.
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Janosz, Michel, Marc LeBlanc, and Bernard Boulerice. "Consommation de psychotropes et délinquance : de bons prédicteurs de l’abandon scolaire ?" Criminologie 31, no. 1 (September 1, 2005): 87–107. http://dx.doi.org/10.7202/017413ar.
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Although empirical links between deviant behavior and school dropout have been extensively demonstrated, the specific influence of drug use and delinquency on school dropout is still not clear and varies across studies. One reason for this lack of consistency may rests upon the way samples of dropouts have been analysed. Recently, Janosz, Le Blanc, Boulerice and Tremblay (1996) constructed and validated a typology of school dropout highlithing the social and psychological diversity of this population. Using a longitudinal sample of adolescents (N=791), we analyzed the predictive relationships of family rebelliousness, drug use and delinquency on school dropout. The results showed an important variability in the predictive relationships according to the type of dropouts. The necessity of considering the psychosocial heterogeneity of dropouts when conducting such studies is discussed.
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Pusztai, Gabriella, Hajnalka Fényes, Fruzsina Szigeti, and Katalin Pallay. "Dropped-out Students and the Decision to Drop-out in Hungary." Central European Journal of Educational Research 1, no. 1 (October 1, 2019): 31–40. http://dx.doi.org/10.37441/cejer/2019/1/1/3341.
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The interpretation of the phenomenon of student dropout, which represents a waste of a relatively large proportion of human and material capital in the social, individual and institutional domain, is impossible without examining dropout students. In this study, we analysed the DEPART 2018 database, which contains data from 605 Hungarian dropout students. We tried to identify higher education dropout scenarios and pointed out that higher educational dropout is a complex phenomenon. Based on the students' reports on their interpretation and evaluation of their dropouts, four student clusters were created. We compared the groups with their socio-cultural background and their decision to drop out from higher education, and their assessment of that decision. The most important result of the study is that it identifies a new group in addition to the international dropout types, and provides a detailed picture that calls our attention to the diversity of dropout groups, thereby moving beyond the over-generalised image of the dropout student.
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Huang, Li-xun, and Yong Fang. "Convergence Analysis of Wireless Remote Iterative Learning Control Systems with Dropout Compensation." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/609284.
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The wireless remote iterative learning control (ILC) system with random data dropouts is considered. The data dropout is viewed as a binary switching sequence which obeys the Bernoulli distribution. In order to eliminate the effect of data dropouts on the convergence property of output error, the signal at the same time with the lost one but in the last iteration is used to compensate the data dropout at the actuator. With the dropout compensation, the convergence property of output error is analyzed by studying the element values of system transition matrix. Finally, some simulation results are given to illustrate the validity of the proposed method.
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Hammack, Floyd Morgan. "Large School Systems' Dropout Reports: An Analysis of Definitions, Procedures, and Findings." Teachers College Record: The Voice of Scholarship in Education 87, no. 3 (March 1986): 324–41. http://dx.doi.org/10.1177/016146818608700303.
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One basic problem for both researchers and policymakers is obtaining accurate information about dropouts. In this article, Floyd Hammack examines school district reports on the dropout problem in Boston, Los Angeles, Miami, New York City, San Diego, and Chicago. Citing the great diversity in the processes for the classification of students as dropouts, he raises important concerns about the comparability of dropout rates between districts.
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Andrea King-Dominguez, Andrea, Luis Amestica-Rivas, Victor Ramirez Gonzalez, and Francisco Ganga Contreras. "Student dropout, the economic cost for Chilean universities." Universidad Ciencia y Tecnología 27, no. 118 (February 27, 2023): 17–28. http://dx.doi.org/10.47460/uct.v27i118.683.
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Student dropout continues to be a significant challenge in higher education. Studies have shown that it has economic impacts on institutions. However, the cost associated with student dropout needs to be better understood. This research focuses on first-year dropouts in 51 Chilean universities, intending to analyze the costs generated by student dropouts in all their careers, allowing to project in an average horizon the resources that would no longer be received as a result of dropouts. It is descriptive research with a documentary analysis technique. Among the most outstanding results, it is shown that the institutions would lose around USD 1,169,853,410 and that private universities are the ones that register a higher cost associated with first-year university dropouts. On the other hand, it is statistically proven that for those universities that register a higher number of enrollments, the cost associated with dropout will be higher.
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Nurmalitasari, Zalizah Awang Long, and Mohammad Faizuddin Mohd Noor. "Factors Influencing Dropout Students in Higher Education." Education Research International 2023 (February 8, 2023): 1–13. http://dx.doi.org/10.1155/2023/7704142.
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Dropout students are a severe problem in higher education (HE) in many countries. Student dropout has a tremendous negative impact not only on individuals but also on universities and socioeconomic. Consequently, preventing educational dropouts is a considerable challenge for HE’s institutions. Therefore, knowing the factors influencing student dropout is an essential first step in preventing students from dropping out. This study uses a mix of qualitative and quantitative approaches. To determine what variables affect student dropout, we use a qualitative approach, after which the variables found will be validated by the public and stakeholders using a quantitative approach. Then, the next step is to classify variables using a quantitative approach. This study observes dropout students at private universities in Central Java, Indonesia. The findings reveal that personal economic factors, academic satisfaction, academic performance, and family economics are the most influential. The results of this paper are significant for universities in Indonesia, especially Central Java, to overcome the problem of student dropouts, so that they are more precise in making decisions. In addition, the results of this study are also helpful for further research as a basis for predicting students dropping out of university.
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Radovanovic, Sandro, Boris Delibasic, and Milija Suknovic. "Predicting dropout in online learning environments." Computer Science and Information Systems, no. 00 (2020): 53. http://dx.doi.org/10.2298/csis200920053r.
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Online learning environments became popular in recent years. Due to high attrition rates, the problem of student dropouts became of immense importance for course designers, and course makers. In this paper, we utilized lasso and ridge logistic regression to create a prediction model for dropout on the Open University database. We investigated how early dropout can be predicted, and why dropouts occur. To answer the first question, we created models for eight different time frames, ranging from the beginning of the course to the mid-term. There are two results based on two definitions of dropout. Results show that at the beginning AUC of the prediction model is 0.549 and 0.661 and rises to 0.681 and 0.869 at mid-term. By analyzing logistic regression coefficients, we showed that at the beginning of the course demographic features of the student and course description features are the most important variables for dropout prediction, while later student activity gains more importance.
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Hernández-Rodríguez, Juan-Carlos, Cristina García-Muñoz, Juan Ortiz-Álvarez, Francesc Saigí-Rubió, Julián Conejo-Mir, and Jose-Juan Pereyra-Rodriguez. "Dropout Rate in Digital Health Interventions for the Prevention of Skin Cancer: Systematic Review, Meta-analysis, and Metaregression." Journal of Medical Internet Research 24, no. 12 (December 9, 2022): e42397. http://dx.doi.org/10.2196/42397.
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Background Digital strategies are innovative approaches to the prevention of skin cancer, but the attrition following this kind of intervention needs to be analyzed. Objective The aim of this paper is to assess the dropouts from studies focused on digital strategies for the prevention of skin cancer. Methods We conducted this systematic review with meta-analyses and metaregression according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statements. Search terms for skin cancer, digital strategies, and prevention were combined to search PubMed, Scopus, Web of Science, CINAHL, and Cochrane Library from inception until July 2022. Randomized clinical trials that reported dropouts of participants and compared digital strategies with other interventions to prevent skin cancer in healthy or disease-free participants were included. Two independent reviewers extracted data for analysis. The Revised Cochrane Collaboration Bias tool was employed. We calculated the pooled dropout rate of participants through a meta-analysis of proportions and examined whether dropout was more or less frequent in digital interventions against comparators via an odds ratio (OR) meta-analysis. Data were pooled using a random-effects model. Subgroup meta-analyses were conducted in a meta-analysis of proportions and OR meta-analysis to assess the dropout events when data were sorted by digital interventions or control comparator. A univariate metaregression based on a random-effects model assessed possible moderators of dropout. Participants’ dropout rates as pooled proportions were calculated for all groups combined, and the digital and comparator groups separately. OR>1 indicated higher dropouts for digital-based interventions. Metaregressions were performed for age, sex, length of intervention, and sample size. Results A total of 17 studies were included. The overall pooled dropout rate was 9.5% (95% CI 5.0-17.5). The subgroup meta-analysis of proportions revealed a dropout rate of 11.6% for digital strategies (95% CI 6.8-19.0) and 10.0% for comparators (95% CI 5.5-17.7). A trend of higher dropout rates for digital strategies was observed in the overall (OR 1.16, 95% CI 0.98-1.36) and subgroup OR meta-analysis, but no significant differences were found between the groups. None of the covariates moderated the effect size in the univariate metaregression. Conclusions Digital strategies had a higher dropout rate compared to other prevention interventions, but the difference was not significant. Standardization is needed regarding reporting the number of and reasons for dropouts. Trial Registration International Prospective Register of Systematic Reviews (PROSPERO) CRD42022329669; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=329669
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Fetler, Mark. "School Dropout Rates, Academic Performance, Size, and Poverty: Correlates of Educational Reform." Educational Evaluation and Policy Analysis 11, no. 2 (June 1989): 109–16. http://dx.doi.org/10.3102/01623737011002109.
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Educational policy makers have questioned the effects of school reforms on at-risk students. Are higher academic standards associated with greater numbers of dropouts? This study examines school average dropout rates for 2 consecutive years in conjunction with percentage covered by Aid to Families With Dependent Children (AFDC), total enrollment, achievement, and academic course enrollments for all of California’s regular public high schools. Both AFDC percentage and total enrollment were associated with higher dropout rates. Higher achievement was associated with lower dropout rates even after statistically controlling AFDC percentage and total enrollment. The year-to-year stability of school dropout rates and their correlations with the other study variables were assessed. The findings suggest that school effectiveness measures that result in higher student achievement may also enhance the effectiveness of dropout treatment programs.
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Roy, Ram Rekha, and Bishnu Prasad Sharma. "Economic Cost of Absentee and Dropout Students in Public Schools of Nepal." Economic Journal of Nepal 42, no. 3-4 (December 31, 2019): 1–11. http://dx.doi.org/10.3126/ejon.v42i3-4.36029.
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High absenteeism and dropout rates in school are considered one of the major stumbling blocks in achieving educational goals in developing countries. Absentees and dropouts impose a large resource cost on the educational system and the society. The study examines the economic cost of absentee and dropout students in public schools of Nepal. Using data from public schools in a rural municipality in Nepal, the study estimates the unit cost of absentee and dropout students by level. Cost of human resource, stationery, utility and fixed costs of building and furniture were the components for estimating unit costs. The findings reveal that the unit cost of students was equivalent to U.S. $295, 130 and 143 for primary, lower-secondary, and secondary levels respectively, and an average of U.S. $189 overall. The resource loss due to dropout was around 28, 12, and 11 percent of the total resource spent for primary, lower secondary and secondary levels respectively with an average of around 16 percent. The combined resource loss from dropouts and absentees were found to be 39, 23 and 20 percent for the different levels. On average, 26 percent of the public-school resource was lost due to dropouts and absentees. The main causes of dropout and absenteeism were the lack of interest, motivation of parents and the children to attend the school. The study suggests the need for more comprehensive and in depth study for effective policy formulation to address this problem.
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Khan, Aiyaz Ahmad, and Abdul Samad. "A Study of Factors Affecting Boys’ Dropout." RESEARCH REVIEW International Journal of Multidisciplinary 7, no. 8 (August 17, 2022): 27–35. http://dx.doi.org/10.31305/rrijm.2022.v07.i08.005.
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School dropout is a major hindrance in attaining the goals of education in India. From time to time several policy pronouncements have been made, but it’s not yielding the desired outcome. Over a decade, the dropout rates for both boys and girls are reflecting a decreasing trend. Several reports reveal that more boys are now dropping out of schools, mainly at the primary level. As per a Unified District Information System for Education Plus (UDISE+) report, the rate of dropout for boys in primary classes was 1.7 percent and that of girls was 1.2 percent. Similarly, the rate of dropout for boys in secondary classes was 18.3 percent which is higher than that of girls with 16.3 percent (TOI, Jul 2, 2021). In this context, attempts have been made to study the patterns of boys’ dropouts and to explore the nature of relationships among different familial and institutional factors with dropouts. This paper is based on the empirical study carried out in one of the most backward districts of West Bengal. A questionnaire on the school dropout factors having 24 items was designed and administered among 93 respondents. The items of the questionnaire were identified through a review of the extant literature. Frequency distribution and ordinal logistic regression were used to analyse the data. Results indicate that key determinants of dropouts are expiry of breadwinner, household works, personal health problems, lack of interest and failure in examinations in studies.
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Kyei, Kwabena A. "Dropout in High Schools students–Modeling a Case Study in the Vhembe District, Limpopo Province, South Africa." Journal of Education and Vocational Research 5, no. 2 (June 30, 2014): 65–72. http://dx.doi.org/10.22610/jevr.v5i2.154.
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South Africa experiences a significant amount of dropouts of high school learners every year. Only about 45% of learners who start primary school in a given year write matriculation examinations after 10 years of schooling. A high dropout rate deprives the country of well-informed and competent future leaders. This study looks at the factors that underlie dropout in high schools in the Vhembe district. A random sample of 5 villages was selected from 25 villages which have high schools. A census of high school dropouts in these 5 villages was conducted. About 660 dropouts were obtained and interviewed. Applying regression modeling, this study shows that poor performance, attitude, punishment and pregnancy are the factors affecting dropout in Vhembe, and recommends that educators encourage learners to develop positive attitude to school work and also give extra assistance to under-performing learners, and sex education be given with seriousness to reduce unwanted pregnancies.
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Lam, Wing-Shun, Parvez N. Guzdar, and Rajarshi Roy. "Effect of Spontaneous Emission Noise and Modulation on Semiconductor Lasers Near Threshold with Optical Feedback." International Journal of Modern Physics B 17, no. 22n24 (September 30, 2003): 4123–38. http://dx.doi.org/10.1142/s021797920302209x.
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The dynamical behavior of power dropouts in a semiconductor laser with optical feedback, pumped near threshold current, is strongly influenced by quantum noise. This is clearly demonstrated by experiments with modulations on the pumping current or the feedback strength. For the cases without modulation and with only current modulation, the dropouts occur randomly. However the feedback strength modulation locks the dropout events periodically. By numerically modeling these three cases using the Lang–Kobayashi equations with a stochastic term to take into account spontaneous emission noise, it is shown that the observed behavior of the dropouts can be readily reproduced for all three cases. Noise plays a signifcant role in explaining the observed dropout events. A simple explanation of the observed dropout phenomenon is presented, based on the adiabatic motion of the ellipse formed by the steady state solutions of the rate equations due to slow time modulations of the injection current or the feedback strength.
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Rõõm, Marili, Marina Lepp, and Piret Luik. "Dropout Time and Learners’ Performance in Computer Programming MOOCs." Education Sciences 11, no. 10 (October 14, 2021): 643. http://dx.doi.org/10.3390/educsci11100643.
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One of the problems regarding MOOCs (Massive Open Online Courses) is the high dropout rate. Although dropout periods have been studied, there is still a lack of understanding of how dropout differs for MOOCs with different levels of difficulty. A quantitative study was conducted to determine the periods with the highest dropouts in computer programming MOOCs and the performance of the dropouts on the course before dropping out. Four occurrences of three MOOCs, with different durations, difficulty of the topic, and the degree of supportive methods, were included. The results showed that dropout was highest at the beginning of all studied courses. Learners also dropped out before the project. In the easier and shorter courses, most dropouts were successful until they quit the course. In longer and more difficult courses, learners mainly dropped out in the week they started due to experiencing problems with the course activities. It is suggested to recommend that learners take courses at a level that suits them if their current course is too easy or difficult and encourage learners to use course resources for help. It would be a good idea to provide learners with example topics to assist them in starting with a project.
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Song, Zihan, Sang-Ha Sung, Do-Myung Park, and Byung-Kwon Park. "All-Year Dropout Prediction Modeling and Analysis for University Students." Applied Sciences 13, no. 2 (January 14, 2023): 1143. http://dx.doi.org/10.3390/app13021143.
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The core of dropout prediction lies in the selection of predictive models and feature tables. Machine learning models have been shown to predict student dropouts accurately. Because students may drop out of school in any semester, the student history data recorded in the academic management system would have a different length. The different length of student history data poses a challenge for generating feature tables. Most current studies predict student dropouts in the first academic year and therefore avoid discussing this issue. The central assumption of these studies is that more than 50% of dropouts will leave school in the first academic year. However, in our study, we found the distribution of dropouts is evenly distributed in all academic years based on the dataset from a Korean university. This result suggests that Korean students’ data characteristics included in our dataset may differ from those of other developed countries. More specifically, the result that dropouts are evenly distributed throughout the academic years indicates the importance of a dropout prediction for the students in any academic year. Based on this, we explore the universal feature tables applicable to dropout prediction for university students in any academic year. We design several feature tables and compare the performance of six machine learning models on these feature tables. We find that the mean value-based feature table exhibits better generalization, and the model based on the gradient boosting technique performs better than other models. This result reveals the importance of students’ historical information in predicting dropout.
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Silva, Fernanda Cristina da, Thiago Luiz De Oliveira Cabral, and Andressa Sasaki Vasques Pacheco. "Evasão ou permanência? Modelos preditivos para a gestão do Ensino Superior." education policy analysis archives 28 (October 19, 2020): 149. http://dx.doi.org/10.14507/epaa.28.5387.
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This research aimed to propose statistical predictive models for the dropout management in undergraduate courses of a Brazilian higher education institution. For this, we conducted an applied study in four undergraduate e-learning courses at a Brazilian public university. We collected the data of 2,991 students from the university’s institutional systems and we used the binary logistic regression method. In the end, we conclude that for different courses, different variables can influence the dropout phenomenon, as well as the same variable can generate different effects in different realities. In addition, the statistical predictive models developed allowed the inference “dropout” or “permanence” for active students at the time of data collection. In partial assessment of the accuracy of the models, we identified that 9 out of 10 dropouts that occurred were previously identified by the models developed. This highlights the potential for using predictive models to the student dropout management, providing a basis for reviewing educational policies and management by identifying the variables that influence student dropout and permanence, as well as by early identification of students at risk of dropout.
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Warren, Robert Warren, and Krista N. Jenkins. "High School Exit Examinations and High School Dropout in Texas and Florida, 1971–2000." Sociology of Education 78, no. 2 (April 2005): 122–43. http://dx.doi.org/10.1177/003804070507800202.
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In this article, the authors ask whether state high school exit examinations are associated with high school dropout rates and racial/ethnic and socioeconomic inequalities in high school dropout rates in Florida and Texas. Using data from the 1968–2000 October Current Population Surveys, they first consider the 1971–2000 graduating classes and use a measure of whether students left school without obtaining any high school credential as the outcome variable. They next consider the 1991–2000 graduating classes and use a measure that classifies students who obtained general equivalency diplomas as dropouts as the outcome variable. In neither case did the authors find evidence that state high school exit examinations are independently associated with higher dropout rates or greater inequalities in dropout rates.
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Löbig, Anna, Peter Ehnold, and Torsten Schlesinger. "„Da hatte ich einfach kein Interesse mehr daran.“ Analyse der Verläufe von Vereinsmitgliedschaftskarrieren jugendlicher Fußballer unter besonderer Berücksichtigung des Dropouts." Sport und Gesellschaft 17, no. 1 (April 28, 2020): 35–68. http://dx.doi.org/10.1515/sug-2020-0003.
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ZusammenfassungAusgehend von erhöhten Dropout-Quoten, welche mit negativen Konsequenzen für Verbände, Vereine oder Mitglieder einhergehen können, steht die Analyse typischer Vereinskarrieren jugendlicher Fußballer unter besonderer Berücksichtigung des Dropouts im Fokus des Beitrags. Ergänzend zu bisherigen, meist querschnittlich angelegten Studien wird dabei eine lebenslaufbezogene Perspektive eingenommen. Jugendliche Fußballer (n=15) aus verschiedenen Vereinen (n=4) wurden mit Hilfe des biografischen Mappings befragt. Dabei zeigen sich typische Verläufe in Abhängigkeit von der subjektiven Relevanz des Vereinswechsels (Vereins-Dropout) sowie des Fußball-Dropouts: „Vereins-Dropouts mit stabiler Fußballmitgliedschaft“ (Typ 1), „Plötzliche Fußball-Dropouts mit oder ohne vorherige Vereinswechsel“ (Typ 2), „Temporäre Fußball-Dropouts mit anschließendem Vereinswechsel“ (Typ 3) und „Instabile Fußballmitgliedschaftskarrieren“ (Typ 4). Diese Verlaufstypologien erweitern die bis-herige Befundlage um eine prozessuale/verlaufsbezogene Perspektive und eröffnen dadurch alternative Steuerungsoptionen im Umgang mit Dropouts.
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Liu, Chunping, Rong Xiong, Jianxin Xu, and Jun Wu. "On Iterative Learning Control for Remote Control Systems with Packet Losses." Journal of Applied Mathematics 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/245372.
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The problem of iterative learning control (ILC) is considered for a class of time-varying systems with random packet dropouts. It is assumed that an ILC scheme is implemented via a remote control system and that packet dropout occurs during the packet transmission between the ILC controller and the actuator of remote plant. The packet dropout is viewed as a binary switching sequence which is subject to the Bernoulli distribution. In order to eliminate the effect of packet dropouts on the convergence property of output error, the hold-input scheme is adopted to compensate the packet dropout at the actuator. It is shown that the hold-input scheme with average ILC can achieve asymptotical convergence along the iteration axis for discrete time-varying linear system. Numerical examples are provided to show the effectiveness of the proposed method.
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Amare, Meseret Yihun, and Stanislava Simonova. "Global challenges of students dropout: A prediction model development using machine learning algorithms on higher education datasets." SHS Web of Conferences 129 (2021): 09001. http://dx.doi.org/10.1051/shsconf/202112909001.
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Research background: In this era of globalization, data growth in research and educational communities have shown an increase in analysis accuracy, benefits dropout detection, academic status prediction, and trend analysis. However, the analysis accuracy is low when the quality of educational data is incomplete. Moreover, the current approaches on dropout prediction cannot utilize available sources. Purpose of the article: This article aims to develop a prediction model for students’ dropout prediction using machine learning techniques. Methods: The study used machine learning methods to identify early dropouts of students during their study. The performance of different machine learning methods was evaluated using accuracy, precision, support, and f-score methods. The algorithm that best suits the datasets for these performance measurements was used to create the best prediction model. Findings & value added: This study contributes to tackling the current global challenges of student dropouts from their study. The developed prediction model allows higher education institutions to target students who are likely to dropout and intervene timely to improve retention rates and quality of education. It can also help the institutions to plan resources in advance for the coming academic semester and allocate it appropriately. Generally, the learning analytics prediction model would allow higher education institutions to target students who are likely to dropout and intervene timely to improve retention rates and quality of education.
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Nesello, Pietro Felice Tomazini, Olga Tairova, Maria Tairova, Lucas Graciolli, Allan Baroni, Eduardo Comparsi, and Thiago De Marchi. "Treatment of the Aged Patients at a Large Cardiac Rehabilitation Center in the Southern Brazil and Some Aspects of Their Dropout from the Therapeutic Programs." Open Access Macedonian Journal of Medical Sciences 4, no. 4 (November 25, 2016): 654–60. http://dx.doi.org/10.3889/oamjms.2016.125.
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AIM: This paper aims to assess the dropout rate in different age groups through the example of the large cardiac rehabilitation centre affiliated with the Institute of Sports Medicine, University of Caxias do Sul.MATERIAL AND METHODS: A historic cohort study comprising the following groups: Non-Old < 65 (n = 141); Young-Old 65-74 (n = 128); and Middle-Old 75-84 years old (n = 57). The exercise program lasted 48 sessions and dropout was defined as attendance of 50% of sessions or less. Logistic binominal regression was performed to assess the risk of dropout. For all analyses, a two-tailed P value of < 0.05 was used.RESULTS: The total dropout rate was 38.6%. The Young-Old and Middle-Old groups showed lower dropouts compared to Non-Old patients (p = 0.01). Young-Old has 96% less risk for dropout compared to Non-Old group (adjusted odds ratios = 1.96 [1.16–3.29]). Furthermore, patients underwent the Coronary Artery Bypass Graft showed a lower rate of dropout (p = 0.001). The absence of CABG involved three times more risk of dropout (p = 0.001).CONCLUSION: The Non-Old and the Middle-Old patients showed higher dropout rates compared to Young-Old. To ensure the best possible rehabilitation and to improve patients´ participation in CR, these programs should be adjusted to the needs of patients in terms of their age.
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Nicoletti, Maria do Carmo, and Osvaldo Luiz de Oliveira. "A Machine Learning-Based Computational System Proposal Aiming at Higher Education Dropout Prediction." Higher Education Studies 10, no. 4 (September 10, 2020): 12. http://dx.doi.org/10.5539/hes.v10n4p12.
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In the literature related to higher education, the concept of dropout has been approached from several perspectives and, over the years, its definition has been influenced by the use of diversified semantic interpretations. In a general higher education environment dropout can be broadly characterized as the act of a student engaged in a course leaving the educational institution without finishing the course. This paper describes the proposal of the architecture of a computational system, PDE (Predicting Dropout Events), based on machine learning (ML) algorithms and specifically designed for predicting dropout events in a higher level educational environment. PDE’s main subsystem implements a group of instance-based learning (IBL) algorithms which, taking into account a particular university-course environment, and based on log files containing descriptions of previous dropouts events, is capable to predict when a student already engaged in the course, is prone to dropout, so preventive measures could be quickly implemented.
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Uy, Phitsamay. "Asian American Dropouts: A Case Study of Vietnamese and Chinese High School Students in a New England Urban School District." AAPI Nexus Journal: Policy, Practice, and Community 7, no. 1 (2009): 83–104. http://dx.doi.org/10.36650/nexus7.1_83-104_uy.
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In the world of K–12 education, the growing numbers of dropouts are a major concern. This article examines the dropout rates of Chinese and Vietnamese high school students. Using logistic regression analysis, this article examines the influence of ethnicity, gender, and socioeconomic status (SES) on dropout rates. The distinct contribution of this analysis lies within the intraethnic comparisons within the Asian American student population and its use of longitudinal data. The results of the study support existing research that gender and SES are related to dropout rates. Moreover, an interesting interaction between ethnicity and SES exists.
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McNamara, James F., and Maryanne McNamara. "Toward an Accurate Description of the Student Dropout Program." International Journal of Educational Reform 3, no. 4 (October 1994): 500–503. http://dx.doi.org/10.1177/105678799400300416.
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The intent of this article was to demonstrate that reporting only student dropout rates can be extremely misleading. Precisely how this common reporting practice is misleading was demonstrated first by using a hypothetical case study, and then by using a real data case study based on student dropout information provided for the 1988–1989 school year in the Bryan ISD. The solution to this problem was straightforward. School district reports and newspaper articles on school dropouts should present both student dropout rates and student dropout counts in a single graphic representation or data table. This reporting practice would substantially reduce opportunities for anyone in the school community to reach incorrect conclusions or to advance unwarranted stereotypic statements about the school district and any racial group of students residing in the district.
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Qi, Jing, Yang Zhou, Zicen Zhao, and Shuilin Jin. "SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data." PLOS Computational Biology 17, no. 6 (June 17, 2021): e1009118. http://dx.doi.org/10.1371/journal.pcbi.1009118.
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The single-cell RNA sequencing (scRNA-seq) technologies obtain gene expression at single-cell resolution and provide a tool for exploring cell heterogeneity and cell types. As the low amount of extracted mRNA copies per cell, scRNA-seq data exhibit a large number of dropouts, which hinders the downstream analysis of the scRNA-seq data. We propose a statistical method, SDImpute (Single-cell RNA-seq Dropout Imputation), to implement block imputation for dropout events in scRNA-seq data. SDImpute automatically identifies the dropout events based on the gene expression levels and the variations of gene expression across similar cells and similar genes, and it implements block imputation for dropouts by utilizing gene expression unaffected by dropouts from similar cells. In the experiments, the results of the simulated datasets and real datasets suggest that SDImpute is an effective tool to recover the data and preserve the heterogeneity of gene expression across cells. Compared with the state-of-the-art imputation methods, SDImpute improves the accuracy of the downstream analysis including clustering, visualization, and differential expression analysis.
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Buzulukova, Natalia, Jerry Goldstein, Mei-Ching Fok, Alex Glocer, Phil Valek, David McComas, Haje Korth, and Brian Anderson. "Magnetosphere dynamics during the 14 November 2012 storm inferred from TWINS, AMPERE, Van Allen Probes, and BATS-R-US–CRCM." Annales Geophysicae 36, no. 1 (January 25, 2018): 107–24. http://dx.doi.org/10.5194/angeo-36-107-2018.
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Abstract. During the 14 November 2012 geomagnetic storm, the Van Allen Probes spacecraft observed a number of sharp decreases (“dropouts”) in particle fluxes for ions and electrons of different energies. In this paper, we investigate the global magnetosphere dynamics and magnetosphere–ionosphere (M–I) coupling during the dropout events using multipoint measurements by Van Allen Probes, TWINS, and AMPERE together with the output of the two-way coupled global BATS-R-US–CRCM model. We find different behavior for two pairs of dropouts. For one pair, the same pattern was repeated: (1) weak nightside Region 1 and 2 Birkeland currents before and during the dropout; (2) intensification of Region 2 currents after the dropout; and (3) a particle injection detected by TWINS after the dropout. The model predicted similar behavior of Birkeland currents. TWINS low-altitude emissions demonstrated high variability during these intervals, indicating high geomagnetic activity in the near-Earth tail region. For the second pair of dropouts, the structure of both Birkeland currents and ENA emissions was relatively stable. The model also showed quasi-stationary behavior of Birkeland currents and simulated ENA emissions with gradual ring current buildup. We confirm that the first pair of dropouts was caused by large-scale motions of the OCB (open–closed boundary) during substorm activity. We show the new result that this OCB motion was associated with global changes in Birkeland (M–I coupling) currents and strong modulation of low-altitude ion precipitation. The second pair of dropouts is the result of smaller OCB disturbances not related to magnetospheric substorms. The local observations of the first pair of dropouts result from a global magnetospheric reconfiguration, which is manifested by ion injections and enhanced ion precipitation detected by TWINS and changes in the structure of Birkeland currents detected by AMPERE. This study demonstrates that multipoint measurements along with the global model results enable the reconstruction of a more complete system-level picture of the dropout events and provides insight into M–I coupling aspects that have not previously been investigated. Keywords. Magnetospheric physics (magnetosphere–ionosphere interactions; magnetospheric configuration and dynamics); space plasma physics (numerical simulation studies)
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Liu, Lei, Yuhao Luo, Xu Shen, Mingzhai Sun, and Bin Li. "$\beta$ -Dropout: A Unified Dropout." IEEE Access 7 (2019): 36140–53. http://dx.doi.org/10.1109/access.2019.2904881.
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Griswold, Cortland K. "Properties of Samples With Segregating Polymerase Chain Reaction (PCR) Dropout Mutations Within a Species." Evolutionary Bioinformatics 15 (January 2019): 117693431988361. http://dx.doi.org/10.1177/1176934319883612.
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In polymerase chain reaction (PCR)-based DNA sequencing studies, there is the possibility that mutations at the binding sites of primers result in no primer binding and therefore no amplification. In this article, we call such mutations PCR dropouts and present a coalescent-based theory of the distribution of segregating PCR dropout mutations within a species. We show that dropout mutations typically occur along branch sections that are at or near the base of a coalescent tree, if at all. Given that a dropout mutation occurs along a branch section near the base of a tree, there is a good chance that it causes the alleles of a large fraction of a species to go unamplified, which distorts the tree shape. Expected coalescence times and distributions of pairwise sequence differences in the presence of PCR dropout mutations are derived under the assumptions of both neutrality and background selection. These expectations differ from when PCR dropout mutations are absent and may form the basis of inferential approaches to detect the presence of dropout mutations, as well as the development of unbiased estimators of statistics associated with population-level genetic variation.
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Venkatesan, Raghul Gandhi, and Bagavandas Mappillairaju. "Detection of hotspots of school dropouts in India: A spatial clustering approach." PLOS ONE 18, no. 1 (January 17, 2023): e0280034. http://dx.doi.org/10.1371/journal.pone.0280034.
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School dropout is a significant concern universally. This paper investigates the incorporation of spatial dependency in estimating the topographical effect of school dropout rates in India. This study utilizes the secondary data on primary, upper primary, and secondary school dropout rates of the different districts of India available at the Unified District Information System for Education plus (UDISE+) for the year 2020 to contemplate the impact of these dropouts from one region to different regions in molding with promotion rate and repetition rate. The Global Moran’s I, Univariate and Bivariate Local Indicators of Spatial Association, and spatial models are utilized to investigate the geographical variability and to find the possible relationship between dropout rates and the school-level factors at the district level. The outcomes provide clear spatial clustering and precisely highlight the hot zone dropout regions with high repetition and low promotion rates. Based on this study’s results, educational administrators can make evidence-based decisions to reduce dropout rates in hot zones of various regions of India. Furthermore, futuristic studies focusing on linking spatial hot zones with causal factors will add consistent data in assisting policymakers in taking necessary measures to develop a sound education management system.
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Cramer, Holger, Heidemarie Haller, Gustav Dobos, and Romy Lauche. "A Systematic Review and Meta-Analysis Estimating the Expected Dropout Rates in Randomized Controlled Trials on Yoga Interventions." Evidence-Based Complementary and Alternative Medicine 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/5859729.
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A reasonable estimation of expected dropout rates is vital for adequate sample size calculations in randomized controlled trials (RCTs). Underestimating expected dropouts rates increases the risk of false negative results while overestimating rates results in overly large sample sizes, raising both ethical and economic issues. To estimate expected dropout rates in RCTs on yoga interventions, MEDLINE/PubMed, Scopus, IndMED, and the Cochrane Library were searched through February 2014; a total of 168 RCTs were meta-analyzed. Overall dropout rate was 11.42% (95% confidence interval [CI] = 10.11%, 12.73%) in the yoga groups; rates were comparable in usual care and psychological control groups and were slightly higher in exercise control groups (rate = 14.53%; 95% CI = 11.56%, 17.50%; odds ratio = 0.82; 95% CI = 0.68, 0.98;p=0.03). For RCTs with durations above 12 weeks, dropout rates in yoga groups increased to 15.23% (95% CI = 11.79%, 18.68%). The upper border of 95% CIs for dropout rates commonly was below 20% regardless of study origin, health condition, gender, age groups, and intervention characteristics; however, it exceeded 40% for studies on HIV patients or heterogeneous age groups. In conclusion, dropout rates can be expected to be less than 15 to 20% for most RCTs on yoga interventions. Yet dropout rates beyond 40% are possible depending on the participants’ sociodemographic and health condition.
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ERTEKİN, Gülşah. "AN INVESTIGATION OF HIGH SCHOOL DROUPOUT INTERVENSION PROGRAMS." SOCIAL SCIENCE DEVELOPMENT JOURNAL 7, no. 31 (May 15, 2022): 62–70. http://dx.doi.org/10.31567/ssd.612.
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Preventing high school dropout is a serious problem that many countries has to face nowadays.To continue education young people is quite important both themselves and for the welfare of society. Have a quality of education background is enhance individuals’ self confidence and improve quality of life as a result it also effects welfare of the society. However, dropouts have economic insufficiency and also they don’t have health ınsurance so they can’t provide their families.. For this reason goverments developing school based prevention programs for support dropout students. Thi studies selection criteria of intervention programs is studying with high school students and including counseling applications.. In literature review; dropout prevention programs gathered under the following headings; academic support, individual and group counselling sessions, vocational counseling, early warning systems and parent involvement. Relating to the literatür review it is observed that effective intervension programs should have a strong bonding with students, teachesr and parents. Additionally, long terms intervension programs which are continue throughout the year will be more effective. Keywords: Dropout, Dropout Intervension Programs , High School Students,
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Sarnquist, Clea, Jake Sinclair, Benjamin Omondi Mboya, Nickson Langat, Lee Paiva, Bonnie Halpern-Felsher, Neville H. Golden, Yvonne A. Maldonado, and Michael T. Baiocchi. "Evidence That Classroom-Based Behavioral Interventions Reduce Pregnancy-Related School Dropout Among Nairobi Adolescents." Health Education & Behavior 44, no. 2 (July 11, 2016): 297–303. http://dx.doi.org/10.1177/1090198116657777.
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Purpose. To evaluate the effect of behavioral, empowerment-focused interventions on the incidence of pregnancy-related school dropout among girls in Nairobi’s informal settlements. Method. Retrospective data on pregnancy-related school dropout from two cohorts were analyzed using a matched-pairs quasi-experimental design. The primary outcome was the change in the number of school dropouts due to pregnancy from 1 year before to 1 year after the interventions. Results. Annual incidence of school dropout due to pregnancy decreased by 46% in the intervention schools (from 3.9% at baseline to 2.1% at follow-up), whereas the comparison schools remained essentially unchanged ( p < .029). Sensitivity analysis shows that the findings are robust to small levels of unobserved bias. Conclusions. Results suggest that these behavioral interventions significantly reduced the number of school dropouts due to pregnancy. As there are limited promising studies on behavioral interventions that decrease adolescent pregnancy in low-income settings, this intervention may be an important addition to this toolkit.
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Patel, Rachana, Ajay K. Singh, Murari Chandra, Tina Khanna, and Sunil Mehra. "Is Mother’s Education or Household Poverty a Better Predictor for Girl’s School Dropout? Evidence from Aggregated Community Effects in Rural India." Education Research International 2018 (September 16, 2018): 1–9. http://dx.doi.org/10.1155/2018/6509815.
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Neighbourhoods and families are key social institutions instilled with development of early life course of adolescents. Limited research in India has examined exposure to community along with socioeconomic conditions for school-dropouts of girls. Multidimensional benefits of female education are increasingly recognized in recent years; hence, it becomes imperative to study the community components of school discontinuation particularly in rural area. This study aims to analyse effect of mother’s education, community poverty, and gender equitable attitudes associated with a girl’s school dropout. This study uses baseline survey data from an intervention program from poor-performing districts of India. Collinearity among individual predictors validated by variance inflation factor proceeded step-wise binary-logistic regression. Result suggests that communities with high concentrations of poverty and mother illiteracy are disadvantages with low gender perception and high levels of dropouts. Result suggests that girls living in poor communities are three times more likely to dropout and 70% more if belonged to communities with lower mother literacy. Girls who scored low on Gender Equitable Scale had less probability to dropout. The study concludes that girls from poor communities with low levels of mother literacy and non-egalitarian gender attitudes are at higher risk of school dropout.
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Dilmé, Francesc, and Fei Li. "Dynamic Signaling with Dropout Risk." American Economic Journal: Microeconomics 8, no. 1 (February 1, 2016): 57–82. http://dx.doi.org/10.1257/mic.20120112.
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We study the role of dropout risk in dynamic signaling. A seller privately knows the quality of an indivisible good and decides when to trade. In each period, he may draw a dropout shock that forces him to trade immediately. To avoid costly delay, the seller with a low-quality good voluntarily pools with early dropouts, implying that the expected quality of the good increases over time. We characterize the time-varying equilibrium trading dynamics. It is demonstrated that the maximum equilibrium delay of trade is decreasing in the initial belief that the good is of high quality. (JEL C73, D82, D83)
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Chinyabuuma, Mwisha, Amika A. Wardana, and Muhammad Abbas. "Causes of School Dropouts Among Congolese Muslim Refugees in Katwe, Kampala, Uganda." Islam Realitas: Journal of Islamic & Social Studies 6, no. 1 (June 30, 2020): 29. http://dx.doi.org/10.30983/islam_realitas.v6i1.3233.
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<p>Schools dropout is being a noticeable problem in Eastern Africa. The school dropout fundamentally affected the future of children because it usually leads them to early marriage, drug use, hard work labor, and prostitution as the school dropout is being a serious issue in mostly Eastern Africa. This study is aimed at investigating factors contributing to the school dropout among Congolese Muslim refugee in Katwe, a village in Kampala. Both qualitative and quantitative approach was used in this study in order to find out how language barriers and early marriages causes dropouts among Congolese Muslim refugees in Katwe; to examine how hidden costs at school causes dropouts among Congolese Muslim refugees in Katwe; and to determine the relationship between the causes of school dropout and the actual school dropout. The results of the current research show that 30% of respondents agree that language barriers are one of the most prevalent factors causes of school dropout in Katwe. The instable school fees and earlier marriage also were found as the contributed cause of school dropout. Alternative hypothesis that stated “there is a relationship between causes of school dropout and actual dropout among Congolese Muslim refugees in Katwe, Kampala, Uganda was adopted.</p><p class="StyleAuthorBold"><em>Saat ini, putus sekolah merupakan salah satu permasalahan pelajar di wilayah Afrika Timur. Umumnya, pelajar yang putus sekolah adalah mereka yang miskin dan/atau yatim piatu serta tidak memiliki wali. Putus sekolah secara fundamental mempengaruhi masa depan anak karena situasi ini cenderung mendorong anak ke pernikahan dini, penggunaan obat-obatan terlarang, pekerjaan kasar, dan prostitusi. Berangkat dari fakta bahwa banyak pengungsi perkotaan Kongo yang putus sekolah, kami menyelidiki penyebab kondisi itu. Khususnya di Katwe, sebuah desa di Kampala, hambatan bahasa dan absennya biaya sekolah yang tetap di sekolah-sekolah terdekat diasumsikan sebagai penyebab utama kondisi putus sekolah para siswa pengungsi perkotaan Kongo di Katwe. Penelitian ini bertujuan untuk; mengetahui bagaimana hambatan bahasa dan pernikahan dini menyebabkan putus sekolah di antara pengungsi perkotaan Kongo di Katwe; untuk menjelaskan bagaimana biaya tersembunyi di sekolah menyebabkan putus sekolah di antara pengungsi perkotaan Kongo di Katwe; dan untuk menentukan hubungan antara penyebab putus sekolah di Katwe dengan putus sekolah yang sebenarnya. Penelitian ini menggunakan pendekatan kualitatif dan kuantitatif. Hasil penelitian ini menunjukkan sebesar 30% responden setuju bahwa hambatan bahasa adalah penyebab utama putus sekolah bagi pengungsi perkotaan Kongo di desa Katwe. Sebagai alternatif, penelitian ini mengadopsi hipotesis yang menyatakan bahwa “ada hubungan antara penyebab putus sekolah dan putus sekolah yang sebenarnya di antara pengungsi perkotaan Kongo di Katwe, Kampala, Uganda.</em></p>
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Ke, Zhiwei, Zhiwei Wen, Weicheng Xie, Yi Wang, and Linlin Shen. "Group-Wise Dynamic Dropout Based on Latent Semantic Variations." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11229–36. http://dx.doi.org/10.1609/aaai.v34i07.6782.
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Dropout regularization has been widely used in various deep neural networks to combat overfitting. It works by training a network to be more robust on information-degraded data points for better generalization. Conventional dropout and variants are often applied to individual hidden units in a layer to break up co-adaptations of feature detectors. In this paper, we propose an adaptive dropout to reduce the co-adaptations in a group-wise manner by coarse semantic information to improve feature discriminability. In particular, we showed that adjusting the dropout probability based on local feature densities can not only improve the classification performance significantly but also enhance the network robustness against adversarial examples in some cases. The proposed approach was evaluated in comparison with the baseline and several state-of-the-art adaptive dropouts over four public datasets of Fashion-MNIST, CIFAR-10, CIFAR-100 and SVHN.
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Rani, Ety, and Wardatul Akmam. "Incidence of Early Marriage Induced Girl Dropouts at Secondary Level: A Study of Bharsho Village in Naogaon District." Indonesian Journal of Innovation and Applied Sciences (IJIAS) 2, no. 2 (June 22, 2022): 132–42. http://dx.doi.org/10.47540/ijias.v2i2.463.
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In this study, efforts have been made to explore the incidence of early marriage-induced dropout among girls at the secondary level of education. This study has three objectives. There are: to find different factors that affected girls’ dropout; to investigate the extent to which child marriage functioned as a reason for girls’ dropout; To relate the findings of the study to Maslow’s theory of hierarchy of needs. Social survey and case study methods have been applied for data collection. Data have been collected from 131 respondents with informed consent. The research area of this study was Bharsho village under the district of Naogaon in Bangladesh. In this study, the socio-economic condition of respondents’ parents and father-in-law was poor. A majority of the respondents’ main reason for dropping out was early marriage. Early marriage was a very significant factor that affects girls’ dropouts. This study shows a strong relationship between Maslow’s hierarchy of needs, early marriage, and girls’ dropout.
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Chen, Chong, Changjing Wu, Linjie Wu, Xiaochen Wang, Minghua Deng, and Ruibin Xi. "scRMD: imputation for single cell RNA-seq data via robust matrix decomposition." Bioinformatics 36, no. 10 (March 2, 2020): 3156–61. http://dx.doi.org/10.1093/bioinformatics/btaa139.
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Abstract Motivation Single cell RNA-sequencing (scRNA-seq) technology enables whole transcriptome profiling at single cell resolution and holds great promises in many biological and medical applications. Nevertheless, scRNA-seq often fails to capture expressed genes, leading to the prominent dropout problem. These dropouts cause many problems in down-stream analysis, such as significant increase of noises, power loss in differential expression analysis and obscuring of gene-to-gene or cell-to-cell relationship. Imputation of these dropout values can be beneficial in scRNA-seq data analysis. Results In this article, we model the dropout imputation problem as robust matrix decomposition. This model has minimal assumptions and allows us to develop a computational efficient imputation method called scRMD. Extensive data analysis shows that scRMD can accurately recover the dropout values and help to improve downstream analysis such as differential expression analysis and clustering analysis. Availability and implementation The R package scRMD is available at https://github.com/XiDsLab/scRMD. Supplementary information Supplementary data are available at Bioinformatics online.
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Parreño, Samuel John. "School dropouts in the Philippines: causes, changes and statistics." Sapienza: International Journal of Interdisciplinary Studies 4, no. 1 (January 17, 2022): e23002. http://dx.doi.org/10.51798/sijis.v4i1.552.
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School dropout is a major problem as it has negative impacts that result in high social costs. A learner can be called a dropout when the learner fails to enroll in school despite having reached the mandatory school age. This paper aims to identify the root causes of school dropouts in the Philippines, specifically in all regions for the years 2008 and 2013. The secondary data on the proportion of population 6 to 24 years old who are not attending school from the website of the Philippine Statistics Authority were used. The data were analyzed using R version 4.2.1. This paper employed descriptive statistics, namely frequency and percentage to identify the root causes of dropping out in the Philippines when grouped according to region and year. Percentage change (increase and decrease) were explored to track changes in the causes of school dropouts. The findings indicated that the high cost of education, and student employment or when the student is seeking employment were the root causes of dropouts in the Philippines for the years 2008 and 2013, respectively. Based on the findings, it is recommended that the Philippine government, through the Department of Education, should prioritize programs that will address the root causes of the school dropouts with the goal of decreasing the dropout rate in general.
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Alghamdi, Saeed M., Ahmed M. Al Rajah, Yousef S. Aldabayan, Abdulelah M. Aldhahir, Jaber S. Alqahtani, and Abdulaziz A. Alzahrani. "Chronic Obstructive Pulmonary Disease Patients’ Acceptance in E-Health Clinical Trials." International Journal of Environmental Research and Public Health 18, no. 10 (May 14, 2021): 5230. http://dx.doi.org/10.3390/ijerph18105230.
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Introduction: Telehealth (TH) interventions with Chronic Obstructive Pulmonary Disease (COPD) management were introduced in the literature more than 20 years ago with different labeling, but there was no summary for the overall acceptance and dropout rates as well as associated variables. Objective: This review aims to summarize the acceptance and dropout rates used in TH interventions and identify to what extent clinical settings, sociodemographic factors, and intervention factors might impact the overall acceptance and completion rates of TH interventions. Methods: We conducted a systematic search up to April 2021 on CINAHL, PubMed, MEDLINE (Ovid), Cochrane, Web of Sciences, and Embase to retrieve randomized and non-randomized control trials that provide TH interventions alone or accompanied with other interventions to individuals with COPD. Results: Twenty-seven studies met the inclusion criteria. Overall, the unweighted average of acceptance and dropout rates for all included studies were 80% and 19%, respectively. A meta-analysis on the pooled difference between the acceptance rates and dropout rates (weighted by the sample size) revealed a significant difference in acceptance and dropout rates among all TH interventions 51% (95% CI 49% to 52; p < 0.001) and 63% (95% CI 60% to 67; p < 0.001), respectively. Analysis revealed that acceptance and dropout rates can be impacted by trial-related, sociodemographic, and intervention-related variables. The most common reasons for dropouts were technical difficulties (33%), followed by complicated system (31%). Conclusions: Current TH COPD interventions have a pooled acceptance rate of 51%, but this is accompanied by a high dropout rate of 63%. Acceptance and dropout levels in TH clinical trials can be affected by sociodemographic and intervention-related factors. This knowledge enlightens designs for well-accepted future TH clinical trials. PROSPERO registration number CRD4201707854.
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Askeland, Ingunn Rangul, and Trond Heir. "Early Dropout in Men Voluntarily Undergoing Treatment for Intimate Partner Violence in Norway." Violence and Victims 28, no. 5 (2013): 822–31. http://dx.doi.org/10.1891/0886-6708.vv-d-12-00137.
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This study investigated psychotherapy dropout rates and predictors of dropping out of therapy early. We enrolled 1,166 men voluntarily admitted to psychotherapy for violent behavior against a female partner. In total, 315 (23.8%) subjects dropped out of therapy within the first 3 sessions. Dropouts were significantly younger, more likely to have a non-Norwegian ethnic background, less likely to have received previous mental health care, and were more likely to have been treated by a student therapist. Among those treated by a student therapist, unemployment was strongly associated with early dropout. Unadjusted and adjusted results were similar. Clients and therapists may benefit by matching unemployed men with experienced therapists. Finally, the high dropout rates among ethnically non-Norwegian clients points to a need for special treatment approaches for this subgroup.
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Garcia-Zanabria, Germain, Daniel A. Gutierrez-Pachas, Guillermo Camara-Chavez, Jorge Poco, and Erick Gomez-Nieto. "SDA-Vis: A Visualization System for Student Dropout Analysis Based on Counterfactual Exploration." Applied Sciences 12, no. 12 (June 7, 2022): 5785. http://dx.doi.org/10.3390/app12125785.
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High and persistent dropout rates represent one of the biggest challenges for improving the efficiency of the educational system, particularly in underdeveloped countries. A range of features influence college dropouts, with some belonging to the educational field and others to non-educational fields. Understanding the interplay of these variables to identify a student as a potential dropout could help decision makers interpret the situation and decide what they should do next to reduce student dropout rates based on corrective actions. This paper presents SDA-Vis, a visualization system that supports counterfactual explanations for student dropout dynamics, considering various academic, social, and economic variables. In contrast to conventional systems, our approach provides information about feature-perturbed versions of a student using counterfactual explanations. SDA-Vis comprises a set of linked views that allow users to identify variables alteration to chance predefined students situations. This involves perturbing the variables of a dropout student to achieve synthetic non-dropout students. SDA-Vis has been developed under the guidance and supervision of domain experts, in line with some analytical objectives. We demonstrate the usefulness of SDA-Vis through case studies run in collaboration with domain experts, using a real data set from a Latin American university. The analysis reveals the effectiveness of SDA-Vis in identifying students at risk of dropping out and proposes corrective actions, even for particular cases that have not been shown to be at risk with the traditional tools that experts use.
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Seidler, Zac E., Michael J. Wilson, David Kealy, John L. Oliffe, John S. Ogrodniczuk, and Simon M. Rice. "Men’s Dropout From Mental Health Services: Results From a Survey of Australian Men Across the Life Span." American Journal of Men's Health 15, no. 3 (May 2021): 155798832110147. http://dx.doi.org/10.1177/15579883211014776.
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While increasing numbers of Australian men are accessing mental health services, the sustainability of their therapy engagement varies significantly, with many men being lost to follow-up. The current study investigated dropout rates in a large community-based male sample to highlight the reasons for, and potential predictors of, men dropping out of mental health care services. Data were drawn from an online survey of 1907 Australian men (aged 16–85; M = 44.1 years) reflecting on their broad experiences in mental health therapy. Participants responded to bespoke items assessing their past dropout experience and reasons for dropping out, the odds of which were modeled in relation to demographics and predictors (e.g., therapist engagement strategies, alignment to traditional masculinity and pre-therapy feelings of optimism, shame, and emasculation). The overall dropout rate from therapy was 44.8% ( n = 855), of which 26.6% ( n = 120) accessed therapy once and did not return. The most common reasons for dropout were lack of connection with the therapist (54.9%) and the sense that therapy lacked progress (20.2%). Younger age, unemployment, self-reported identification with traditional masculinity, the presence of specific therapist engagement strategies, and whether therapy made participants feel emasculated all predicted dropout. Current depressive symptoms and suicidality were also higher amongst dropouts. Therapists should aim to have an honest discussion with all clients about the importance of therapy fit, including the real likelihood of dropout, in order to ensure this does not deter future engagement with professional services.
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Sari, Eka Yulia, Kusrini Kusrini, and Andi Sunyoto. "Analisis Akurasi Jaringan Syaraf Tiruan Dengan Backpropagation Untuk Prediksi Mahasiswa Dropout." Creative Information Technology Journal 6, no. 2 (February 24, 2021): 85. http://dx.doi.org/10.24076/citec.2019v6i2.235.
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Universitas ABC yogyakarta selalu melakukan evaluasi kinerja mahasiswa guna mengetahui pencapaian pada masing-masing mahasiswa.Mahasiswa yang melampaui masa studi dan tidak melakukan perpanjangan akan dikenakan sanki berupa dropout.Kasus dropout tersebut dapat diminimalisir dengan pendeteksian secara dini terhadap mahasiswa yang beresiko dropout. Pendeteksian dapat dilakukan dengan memanfaatkan tumpukan data untuk memprediksi dropout mahasiswa. Pada penelitian ini bertujuan untuk memprediksi mahasiswa yang berpotensi dropout dengan masa studi maksimal yang harus diselesaikan pada jenjang Sarjana dengan mengimplementasikan Metode Backpropagation. Data yang digunakan dalam penelitian ini adalah data akademik prodi S1 Informatika Universitas ABC pada tahun 2016-2019 denganjumlah dataset sebanyak 129.Tujuan penelitian ini untuk mengukur analisis prediksi dropoutdengan percobaan penggunaan beberapa arsitektur jaringan. Hasil yang diperoleh dari modelyang diusulkan yaitu model arsitektur 12-5-2 merupakan model arsitektur terbaik yangdidapatkan. Learning rate terbaik sebesar 0,4 dengan momentum terbaik sebesar 0,95. Akurasi yang diperoleh dari prediksi mahasiswa dropout dengan arsitektur, learning rate, dan momentum terbaik sebesar 98,2%.ABC University of Yogyakarta always evaluates student performance in order to find out the achievements of each student. Students who have exceeded the study period and not extended would be subject to sanctions in the form of a dropout. The dropout case can be minimized by early detection of students who are at risk of dropout. Detection can be done by utilizing a pile of data to predict student dropouts. In this study aims to predict students who have the potential to drop out with a maximum study period that must be completed at the Undergraduate level by implementing the Backpropagation Method. The data used in this study are academic data of S1 University Informatics Study Program of ABC University in 2016-2019 with the number of datasets as much as 129. The purpose of this study is to measure the dropout prediction analysis with the experiments of using several network architectures. The results obtained from the proposed model, namely architectural models 12-5-2, are the best architectural models obtained. The best learning rate is 0.4 with the best momentum of 0.95. The accuracy obtained from the prediction of dropout students is 98.2%.
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Brorson, Hanne H., Espen Ajo Arnevik, and Kim Rand. "Predicting Dropout from Inpatient Substance Use Disorder Treatment: A Prospective Validation Study of the OQ-Analyst." Substance Abuse: Research and Treatment 13 (January 2019): 117822181986618. http://dx.doi.org/10.1177/1178221819866181.
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Background and Aims: There is an urgent need for tools allowing therapists to identify patients at risk of dropout. The OQ-Analyst, an increasingly popular computer-based system, is used to track patient progress and predict dropout. However, we have been unable to find empirical documentation regarding the ability of OQ-Analyst to predict dropout. The aim of the present study was to perform the first direct test of the ability of the OQ-Analyst to predict dropout. Design: Patients were consecutively enlisted in a naturalistic, prospective, longitudinal clinical trial. As interventions based on feedback from the OQ-Analyst could alter the outcome and potentially render the prediction wrong, feedback was withheld from patients and therapists. Setting: The study was carried out during 2011–2013 in an inpatient substance use disorder clinic in Oslo, Norway. Participants: Patients aged 18 to 28 years who met criteria for a principal diagnosis of mental or behavioural disorder due to psychoactive substance use (ICD 10; F10.2–F19.2). Measurements: Red signal (predictions of high risk) from the Norwegian version of the OQ-Analyst were compared with dropouts identified using patient medical records as the standard for predictive accuracy. Findings: A total of 40 patients completed 647 OQ assessments resulting in 46 red signals. There were 27 observed dropouts, only one of which followed after a red signal. Patients indicated by the OQ-Analyst as being at high risk of dropping out were no more likely to do so than those indicated as being at low risk. Random intercept logistic regression predicting dropout from a red signal was statistically nonsignificant. Bayes factor supports no association. Conclusions: The study does not support the predictive ability of the OQ-Analyst for the present patient population. In the absence of empirical evidence of predictive ability, it may be better not to assume such ability.
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Saputra, Awang, and Muhamad Fauzi. "Integration of Education Planning Funds Base on Philanthropy: Drop Out School Mitigation." International Journal of Humanities, Management and Social Science 5, no. 1 (June 26, 2022): 8–17. http://dx.doi.org/10.36079/lamintang.ij-humass-0501.333.
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The increasing number of dropouts during the Covid-19 pandemic needs to find an alternative solution by planning for philanthropy-based education financing. This paper aims to discuss education financing planning and to know the role of philanthropy in rescuing dropout students. The research uses a qualitative method with a literature study approach with content analysis techniques. Educational institutions must plan education financing in the budget work plan by seeking alternative funding assistance for students dropping out of school due to economic problems. Mitigation of dropout students is carried out by identifying students and parents, providing counselling services, collaborating with donor agencies, continuing formal or non-formal schools and finding donors or sponsors. The role of philanthropy in Indonesia in education has provided positive support, equality and sustainability of the quality of human development, including dropout students. The research contribution provides an overview for educational institutions to find and prepare special budgets to overcome school financial difficulties and overcome students who leave due to not being able to carry out their financial obligations during the COVID-19 pandemic. The implication is that education financing planning efforts in mitigating dropouts are integrated into philanthropic institutions.
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Yukselturk, Erman, Serhat Ozekes, and Yalın Kılıç Türel. "Predicting Dropout Student: An Application of Data Mining Methods in an Online Education Program." European Journal of Open, Distance and E-Learning 17, no. 1 (July 1, 2014): 118–33. http://dx.doi.org/10.2478/eurodl-2014-0008.
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Abstract This study examined the prediction of dropouts through data mining approaches in an online program. The subject of the study was selected from a total of 189 students who registered to the online Information Technologies Certificate Program in 2007-2009. The data was collected through online questionnaires (Demographic Survey, Online Technologies Self-Efficacy Scale, Readiness for Online Learning Questionnaire, Locus of Control Scale, and Prior Knowledge Questionnaire). The collected data included 10 variables, which were gender, age, educational level, previous online experience, occupation, self efficacy, readiness, prior knowledge, locus of control, and the dropout status as the class label (dropout/not). In order to classify dropout students, four data mining approaches were applied based on k-Nearest Neighbour (k-NN), Decision Tree (DT), Naive Bayes (NB) and Neural Network (NN). These methods were trained and tested using 10-fold cross validation. The detection sensitivities of 3-NN, DT, NN and NB classifiers were 87%, 79.7%, 76.8% and 73.9% respectively. Also, using Genetic Algorithm (GA) based feature selection method, online technologies self-efficacy, online learning readiness, and previous online experience were found as the most important factors in predicting the dropouts.
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Anderson, Jordan A., Shaelyn M. Harris, Erica L. Nelson, Ingrid Teuber, and Andrew Futterman. "DIMINISHED COMPLEXITY OF RELIGIOUS BELIEF AMONG DROPOUTS FROM A LONGITUDINAL STUDY OF COPING." Innovation in Aging 3, Supplement_1 (November 2019): S458. http://dx.doi.org/10.1093/geroni/igz038.1714.
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Abstract Selective attrition is a common problem in longitudinal studies of older adults. Dropout is due to many factors, but frequently health concerns figure prominently as a reason for attrition. In light of previous work that suggests health problems reduce complexity of religious and other social involvements, the current study examines complexity of religiousness among dropouts and continuers in a longitudinal study of religion and health in later life. A random sample of 287 older adults living in Worcester, MA was assessed at two times of measurement 12 months apart using interview-based measures of religious orientation (Batson, Schoenrade, and Ventis, 1993) and health (OARS). Of the 287 who began the study, 72 dropped out and were not available to be assessed at 12 month assessment. Using Mplus, a three-factor model of Ends, Means, and Quest orientations demonstrated a good fit to the data in both dropout and continuer subsamples (e.g., CFI’s equaled .959 and .966, respectively). Diminished correlations between Ends, Means, and Quest orientations in the dropout vs. continuer subsample suggests diminished complexity of religious orientation among dropouts. Dropouts were more seriously ill, had higher levels of functional impairment, and demonstrated lower SES, suggesting increased vulnerability and fewer resources may have been the reason for dropping from the study as well as one possible cause of their diminished religious complexity.