Dropout In Higher Education Research Articles

Dropout in Rural Higher Education: A Systematic Review

Student dropout in higher education has been of great interest to the academic community, state and social actors over the last three decades, due to the various effects that this event has on the student, the family, higher education institutions, and the state itself. It is recognised that dropout at this level of education is extremely complex due to its multi-causality which is expressed in the existing relationship in its explanatory variables associated with the students, their socioeconomic and academic conditions, as well as the characteristics of the educational institutions. Thus, the aim of this article was to identify the individual, socioeconomic, academic, and institutional explanatory variables involved in student dropout in rural populations, based on a synthesis of the evidence available in the SCOPUS database. In order to achieve it, a mixed systematic review was defined under the PRISMA 2020 method. The analysis was approached in two stages; the first concerned the identification of the documents and the conformation of the sample, where 21 documents were distinguished for effectively dealing with dropout in rural higher education; and the second corresponded to the procedures defined for the development of the bibliometric analysis and synthesis of the information found in the documents. The results showed the distribution of studies by country, years of publication, the categorisation of the documents in SCOPUS, their classification by type and the methodologies used in the development of the studies analysed, as well as the variables that have been addressed in previous research. In this way, it is concluded that the results of the studies are not generalisable, either because of the size of the sample or because of the marked social asymmetries that exist in some countries, which can make the findings lack significance; on the other hand, the interest in research on variables associated with individual and academic determinants to explain rural student dropout is highlighted. In addition, some future research lines which can be addressed as a complement to the current view of the dropout event in rural higher education were identified.

Identifying Students at Risk to Academic Dropout in Higher Education

A main goal of the university institution should be to reduce the desertion of its students, in fact, the dropout rate constitutes a basic indicator in the accreditation processes of university centers. Thus, evaluating the cognitive functions and learning skills of students with an increased risk of academic failure can be useful for the adoption of strategies for preventing and reducing school dropout. In this research, cognitive functions and learning skills in 284 university students were evaluated. Academic performance predictors were identified, and conglomerates analysis was carried out to establish groups according to those variables. The stability and validity of the conglomerates were tested with discriminant analyzes and comparison tests. The variables associated significantly to academic performance were: attention, intelligence, motivation, metacognition and affective components. The conglomerate analysis suggested a three-group solution: (1) students with cognitive skills of moderate to high, but deficient learning strategies; (2) students with cognitive and learning capabilities of moderate to high; (3) students with cognitive functions low and moderate learning capacity. Students from groups 1 and 3 showed worse academic performance; 83.3% of students at risk of desertion belonged to such groups. Two groups of students have been identified with the highest risk of academic failure: those with poor cognitive capacity and those with bad learning skills.

Individual Achievement, Person-Major Fit, or Social Expectations: Why Do Students Switch Majors in German Higher Education?

While a large body of research addresses both subject choice and student dropout in higher education, much less is known about switching the initially chosen major. Therefore, we ask why students switch their major in higher education and analyse this for the case of Germany, taking the timing and the degree of such switches (within and across subject groups) into account. Based on the extended rational choice framework, we identify three aspects that might explain switching majors: individual achievement in secondary education, a (mis)match between individual occupational interests and the content of studies and parental and peer judgement regarding the initial subject choice. We test the derived hypotheses by applying logistic regression models to representative data from the German National Educational Panel Study (NEPS), Starting Cohort 5. Our results indicate that the analysed aspects of individual achievement, person-major fit and social expectations affect switching majors, but their influence varies according to the degree and timing of the switch. While high-achieving students are more likely to switch majors, especially across disciplines and at a later stage in their studies, a mismatch in occupational interests mainly affects switching majors across broad subject groups. Finally, disapproval of the initial subject choice by parents and peers matters most for switches during the first two semesters and across academic disciplines.

The Academic Dropout Wheel Analyzing the Antecedents of Higher Education Dropout in Education Studies

Higher education dropout has been considered a major concern for several researchers in the field of education around the World. Although different studies were carried out to deal with the topic, they all faced common limitations. This paper explores the twofold research conducted to investigate higher education dropout in Education studies at two of the main Catalan public universities. First, a review of the different theoretical perspectives that tackled dropout was carried out to combine the direct and indirect dropout predictors. Second, some research was conducted in the faculties of Education in the selected two Catalan public universities to investigate the antecedents of higher education dropout. The utilized instrument discussed in this paper is the survey. In the first part, the predictors of higher education dropout were combined in a 5-layer model called the ¨Dimensions of Academic Dropout” wheel (Naaman, 2018). In the second part, the results of the survey analyzed quantitively revealed that dissatisfaction from the program and academic failure are the most important antecedents of higher education dropout in education studies in Catalonia.

The Role of the Academic Factor in Student’s Tendency to University Dropout

The Role of the Academic Factor in Student’s Tendency to University Dropout George S. Androulakis, Dimitra Ap. Georgiou, Pantelis Kiprianos, George Stamelos Abstract Dropout in higher education has got important dimensions worldwide. The problem has consequences on an individual, social and economic level and it is thus of significance to investigate students’ tendency to dropout before the decision to leave their studies is irreversible. Due to the complexity of the dropout phenomenon and the diversity of the influence exerted by different factors, arises intensively the need for a thorough comprehension of the mechanism and decomposition of the exerted intercorrelations. In regards to the above, this research aimed to analyze the effect of the variables within the academic spectrum on students’ tendency to dropout of their studies via hierarchical regression trees indicating both the sequence and intercorrelation. From the present work it emerged that the elements of the academic index exert a significant impact on students’ tendency to dropout via the sequence of level of study followed by the subject of study, the provision of knowledge to solve complex real-world problems, the feedback by professors on students’ work completed, the students’ perception of justice and reward in the evaluation procedures concerning them, the clarity of the educational goals and courses’ requirements as well as students’ satisfaction with the work of the teaching staff. Full Text: PDF DOI: 10.15640/jehd.v10n1a5

Mining the Student Dropout in Higher Education

Abstract Higher technological and vocational education (TVE) has served an important role in the long-term progress and industrial development of Taiwan. However, the high dropout rates in higher TVE are a challenging task for policy makers. This study is a first to propose a hybrid approach that combines both k-means and rough set theory for mining the dropout knowledge among student dropout. An empirical case of student dropout is based on the industrial-academic cooperation (IAC) education of higher TVE in Taiwan. The results of knowledge extraction from the proposed approach are illustrated as knowledge patterns/rules and clusters to provide better understanding of the reasons for or factors influencing student dropout.

Deserción y estrategias de retención: un análisis desde la universidad particular

La deserción en la educación superior es una problemática que la viven las universidades a nivel mundial, a pesar de los esfuerzos y las diferentes políticas educativas asociadas a garantizar la permanencia de los estudiantes en las universidades por los gobiernos de turno con el propósito de mejorar las tasas de titulación, aún no se ha evidenciado tasas de graduación completas por cohorte, más bien se evidencia desgranamiento, repitencia y abandono en los primeros niveles de las carreras, los que van disminuyendo en torno avanza el nivel cursado. Este trabajo hace una síntesis sobre la evolución de las políticas desde el 2007 a la actualidad y visibiliza las tasas de permanencia y retención de una universidad particular del Ecuador y considera los datos para plantear estrategias de intervención con la finalidad de disminuir la deserción y desgranamiento de estudiantes universitarios.

A Machine Learning-Based Computational System Proposal Aiming at Higher Education Dropout Prediction

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.

Retraction: Using Big Data to Determine Potential Dropouts in Higher Education (Journal of Physics: Conference Series 1432 012077)

Published 15 September 2020This article, and others within this volume, has been retracted by IOP Publishing following clear evidence of plagiarism and citation manipulation.This work was originally published in Spanish (1) and has been translated and published without permission or acknowledgement to the original authors. IOP Publishing Limited has discovered other papers within this volume that have been subjected to the same treatment. This is scientific misconduct.Misconduct investigations are ongoing at the author’s institutions. IOP Publishing Limited will update this notice if required once those investigations have concluded. To date, there is no evidence to suggest anyone other than Amelec Viloria / Jesus Silva was directly culpable for the actions that led to retraction.IOP Publishing Limited request any citations to this article be redirected to the original work (1).Anyone with any information regarding these papers is requested to contact conferenceseries@ioppublishing.org.

Uma análise da evasão discente em cursos de Engenharia de uma Universidade Pública Brasileira

O objetivo deste trabalho foi avaliar três cursos de graduação em Engenharia dos Campi Experimentais da UNESP, e especificamente, descrever a chance de o discente evadir de tais cursos como função de variáveis (socioeconômicas, demográficas e acadêmicas) significativas. Trata-se de uma pesquisa descritiva com abordagem quantitativa. Foi feito um levantamento censitário de informações concernentes aos discentes (evadidos e não evadidos) do período letivo de 2003 a 2013 em Banco de Dados da UNESP. Formulou-se a variável resposta ‘situação do discente’ como função de 24 variáveis explicativas abrangendo especificamente atributos prévios à entrada dos estudantes na universidade. Essa relação foi investigada usando técnica estatística multivariada baseada em regressão logística. Os modelos ajustados conseguiram prever corretamente cerca de 72% dos casos de evasão/permanência nestes cursos. Os resultados mostraram que descrever a chance de evasão envolve fatores acadêmicos, tais como, modalidade de Ensino Médio concluído, se frequentou cursinho, número de vezes que prestou vestibular, se já havia iniciado algum curso superior e classificação no vestibular, e também fatores socioeconômicos, tais como, sexo, cor da pele, se exerce ou exercia atividade remunerada e renda familiar. Evidenciou-se, finalmente, o método proposto, que poderá auxiliar as Instituições de Ensino Superior na análise preliminar do real risco de evasão, bem como no desenvolvimento de novas pesquisas sobre evasão discente no ensino superior com enfoque em cursos de Engenharia.

Uplift Modeling for preventing student dropout in higher education

Uplift modeling is an approach for estimating the incremental effect of an action or treatment at the individual level. It has gained attention in the marketing and analytics communities due to its ability to adequately model the effect of direct marketing actions via predictive analytics. The main contribution of our study is the implementation of the uplift modeling framework to maximize the effectiveness of retention efforts in higher education institutions i.e., improvement of academic performance by offering tutorials. The objective is to improve the design of retention programs by tailoring them to students who are more likely to be retained if targeted. Data from three different bachelor programs from a Chilean university were collected. Students who participated in the tutorials are considered the treatment group, otherwise, they are assigned to the nontreatment group. Our results demonstrate the virtues of uplift modeling in tailoring retention efforts in higher education over conventional predictive modeling approaches.

Cultural characteristic and college dropout

Cultural characteristics of students play an essential role in education. In college, the independent model of self is widely prevalent, whereas first-generation college students practice the interdependent model of self. The present paper argues that first-generation college students are more prone to drop out because of the cultural difference they experience in education. So, dropout is looking through the lenses of cultural perspectives. The paper divided into three sections. The first section focuses on cultural characteristics; the second section focuses on student dropout and finally showed how it relates. Thus, the paper attempted to highlight the importance of maintaining cultural diversity to prevent dropout in higher education.

Dropout at university level in Serbia: Analysis of measurement, research findings, services and prevention measures

Dropout in higher education attracts great attention due to serious effects on an individual and on society. This is an exploratory study aimed at: analysing drop?out measurement and tracking in the Serbian higher education, reviewing studies on dropout causes, and identifying available services and prevention measures. Various techniques are used to achieve these goals (statistics analysis, literature review, qualitative content analysis). Our results show that: 1. There are no accepted dropout definitions nor official statistics, but some rate estimate could be given; 2. Research is not systematic, but covers three major dropout factors (individual, socio-demographic, studying conditions); 3. Services are sporadic and not visible enough; 4. Legislation reveals state interest in promoting higher education, but implemented measures are not adequate enough. We see establishment of the Unified Education Information System as an important step in precise dropout measurement. There is a need for flexible studying routes and increased financial support orientated more towards vulnerable student groups. International studies highlight the importance of intensification of teacher-student and peer interaction built in modern student-centred teaching practice. At the end, we advocate creating a new educational policy by combining two major frameworks in this field and relying on inclusive research.

Using Big Data to Determine Potential Dropouts in Higher Education

In higher education, student dropout is a relevant problem, not just in Latin America but also in developed countries. Although there is no consensus to measure the education quality, one of the important indicators of university success is the time to graduation (TTG), which is directly related to student dropout [1]. Global estimates put this dropout rate at 42% [2]. In the United States, this rate is around 30% and represents a loss of 9 billion dollars in the education of these students [3]. However, desertion not only affects the quality of education and the economy of a country, but also has effects on the development of society, since society demands the contributions derived from the population with higher education such as: innovation, knowledge production and scientific discovery [4]. Using basic statistical learning techniques, this paper presents a simple way to predict possible dropouts based on their demographic and academic characteristics.

Evasão no curso de licenciatura em geografia da Universidade Estadual do Oeste do Paraná (UNIOESTE): indagações e complexidades

A evasão escolar no ensino superior é um tema que apresenta indagações e complexidades. A necessidade de ampliar e aprofundar o estudo sobre a evasão universitária remeteu este trabalho, de modo a compreender a questão da evasão no Curso de Licenciatura em Geografia da Universidade Estadual do Oeste do Paraná (UNIOESTE), Campus Marechal Cândido Rondon. O enfoque temporal ficou limitado aos anos de 2012 a 2015, desenvolveram-se levantamentos de dados e possíveis hipóteses da evasão, a fim de caracterizar as dificuldades encontradas pelos discentes na conclusão do curso. Para o desenvolvimento do estudo, optou-se pela pesquisa qualitativa, através da técnica de estudo de caso, com a participação de discentes evadidos do Curso de Licenciatura em Geografia, por meio de entrevista/questionário semiestruturado. Nesse contexto, o número de ingressantes no curso supracitado totalizou em 146 discentes. Destes, 53% se evadiram do curso. Dos 73 discentes evadidos relativos ao enfoque temporal, 53 discentes concordaram em contribuir com a pesquisa. Uma das razões mais fortes para o abandono do curso, segundo os retornos, é a dificuldade de conciliar trabalho e universidade, pois 85% dos evadidos desenvolviam atividades remuneradas ao desistirem do curso. Outro fator é que se verifica participação reduzida dos ex-alunos na vida interna da universidade, seja por falta de tempo, seja por falta de informações sobre seu funcionamento e espaços de atuação dos alunos na instituição.

Fator institucional para a evasão na educação superior


 
 
 
 
 
 O objetivo do artigo é analisar ou concentrar a produção de divulgação e testes de evasão no ensino superior não entre 2000 e 2018, com o objetivo de identificar a instituição institucional em que dois respondentes são responsáveis ​​por esse fenômeno no Brasil. Trata-se de uma pesquisa qualitativa, classificada na literatura como estado de controle. Para procurar emprego, visite os bancos de teses e dissertações do Ibict e da Capes, selecionando 267 dissertações e 32 teses para análise. Os resultados indicam que a principal tendência de pesquisa se refere a um estudo de caso nos cursos presenciais de universidades federais e que ou instituição institucional com o objetivo de serem explorados pelas dissertações e teses brasileiras. A formulação de políticas institucionais para o controle da evasão será considerada necessária, pois serão necessárias pesquisas que analisem ou processos de institucionalização dessas ações nas IES Brasiliras.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Intervenção em autorregulação da aprendizagem com alunos do ensino superior: análise da produção científica

Pesquisas recentes na área educacional têm evidenciado altas taxas de reprovação e evasão no Ensino Superior. Estudiosos afirmam que o problema pode estar relacionado às dificuldades de aprendizagem dos alunos no contexto acadêmico. A autorregulação da aprendizagem é uma perspectiva amplamente investigada pelo seu potencial em auxiliar os estudantes a aprender melhor. O presente trabalho objetivou analisar a produção científica de artigos sobre programas de intervenção em aprendizagem autorregulada no Ensino Superior, publicados em âmbitos nacional e internacional, no período de 2004 a 2016. Os resultados revelaram o impacto positivo que os programas exerceram no fortalecimento da capacidade de aprender a aprender dos universitários. Os programas de intervenção, em sua maioria, foram realizados em cursos extraclasses, com grupos grandes de alunos e dados coletados por meio de instrumentos de autorrelato. Observou-se um maior número de pesquisas realizadas internacionalmente, com o predomínio do ensino de estratégias no aprendizado de língua estrangeira.

Civic engagement and the resources of persistent students and the reasons of dropout in higher education

Background and aims Even the richest statistical databases leave the reasons of student dropout from higher education largely obscure. It is the answers of the students who drop out that the panel examinations of the cohorts of students do not contain. The theoretical background to our research was provided by Coleman’s concept of social capital (1961), Tinto’s integrational and Astin’s involvement theory, as well as Pascarella and Terenzini’s institutional integration/embeddedness ideas (2005). Methods To find a solution to that problem, we devised a quantitative, questionnaire survey, based upon the qualitative examination of students who have dropped out (DEPART 2018; N = 591). For data gathering, the snowball method was used, and almost all academic fields are represented in the database. The primary objective of the research was revealing and identifying the factors preventing students from obtaining a degree, surveying the processes and decisions that resulted in their not being able to graduate. School career, educational experience, the attitude of the family to the student’s studies, job/career orientation, educational and social experience in higher education, competitive activities, and social–economical characteristics were the topics we examined. We collected the data of the students who have dropped out with the questionnaire (IESA 2015; N = 2017) and compared the information thus collected with the social and demographic characteristics, school career, and higher educational integration of persistent students. We also compared the data to the embeddedness of (the persistent) students in various voluntary organizations (civic, sport, and religious). For the examination of persistence, a 9-item, highly reliable scale [Cronbach, 883 (IESA)] has been used since 2012. Results The results suggest that the influence of institutional factors and that of the social network is more powerful than the individual characteristics. We found considerable differences between the intra- and extra-campus influences. Discussion Our findings highlight the necessity of providing empirical foundations for the institutional dropout prevention programs.

Társadalmi státus transzmissziója és a felsőoktatási lemorzsolódás

A tanulmány a társadalmi státusz "transzmissziója" hatásának a vizsgálatával a felsőoktatási lemorzsolódás speciális aspektusára fókuszál. A felsőoktatási expanzió során a "nem tradicionális hallgatók" (akiknek a szülei alacsonyan iskolázottak) aránya megváltozott. Ezek a hallgatók több területen is hiányosságokkal bírnak, melyek növelhetik a lemorzsolódás esélyét. A kutatás a "Társadalmi és szervezeti tényezők szerepe a hallgatói lemorzsolódásban"(K_123847) OTKA projekt keretében folyt (témavezető Prof. dr. Pusztai Gabriella), az adatbázist a 2018-as felmérés alapján hoztuk létre (N=605). A válaszolók korábban lemorzsolódott hallgatók voltak. Arra törekedtünk, hogy meghatározzuk a lemorzsolódás főbb faktorait, a szociokulturális háttér főbb jellemzőit, összehasonlítva a különböző hátterű (első generációs értelmiségi hallgatók, akiknek a szülei nem diplomások, és az értelmiségi, diplomás családokból származók) jellegzetes mintázatait a lemorzsolódás területén. Az empirikus eredmények azt mutatják, hogy az alacsonyabb szülői iskolai végzettségű családokból érkezők esetében a lemorzsolódásra kényszer jellegű (nem önkéntes) elemek hatnak, míg a többi hallgató körében inkább az önkéntes lemorzsolódás tűnik gyakoribbnak.

Higher education dropout of non-traditional mature freshmen: the role of sociodemographic characteristics

ABSTRACT This paper examines the role of sociodemographic characteristics on non-traditional mature freshmen higher education dropout rates. One of Chile’s largest higher education institutions, which has an important number of mature students from more deprived social sectors, was used as a case study. A quantitative methodology was applied, based on the estimation of logistic regression models, where freshmen dropout rates were defined as the dependent variable and three types of independent variable were considered: family/demographic conditions, socio-economic situation and institutional structures. The results indicate that students who are parents, have a job, are not the heads of their households, are enrolled in longer programmes and who attended adult high school are more likely to drop out of higher education during their first year of study. Suggestions for institutional practices to better integrate mature students into higher education are discussed.