A evasão no ensino superior e os modelos de Vincent Tinto: uma pesquisa bibliográfica1

Improving completion and reducing dropout in higher education are key concerns for higher education in Europe. This study on dropout and completion in higher education in Europe demonstrates that national governments and higher education institutions use three different study success objectives: completion, time-to-degree and retention. To address these objectives policy makers at national and institutional level apply various policy instruments. These can be categorized under three main policy headings: financial incentives; information and support for students; and organizational issues. The evidence indicates that countries that have more explicit study success objectives, targets and policies are likely to be more successful. Particularly if the policy approach is comprehensive and consistent. As such, it is important that study success is an issue in the information provision to (prospective) students, in financial incentives for students and institutions, in quality assurance, and in the education pathways offered to students. Furthermore, increasing the responsibility of higher education institutions for study success, for example in the area of selecting, matching, tracking, counselling, mentoring and integrating students in academic life is clearly effective. Finally, to support the policy debate and monitoring of study success evidence, there is a need for more systematic international comparative data and thorough analysis of the effectiveness of study success policies

Student dropout in higher education remains a critical challenge with significant academic, social, and economic implications. Early identification of students at risk of dropout enables institutions to design timely and targeted interventions that support academic success and improve retention rates. This study proposes a machine learning (ML)–driven framework for the early prediction of student dropout and academic outcomes in higher education using a comprehensive, real-world dataset collected from a higher education institution. The prediction task is formulated as a multiclass classification problem with three outcomes: dropout, enrolled, and graduate. To evaluate the effectiveness of different modeling approaches, we conduct a comparative analysis of widely used ML algorithms, including Logistic Regression, Naïve Bayes, k-Nearest Neighbors, Support Vector Machine, Decision Trees, Random Forest (RF), AdaBoost, XGBoost, LightGBM, and CatBoost. Results indicate that ensemble models achieve the best performance. RF attains the highest test accuracy (0.7797) and ROC-AUC (OvR) (0.8919), while LightGBM yields the best Macro-F1 (0.7082). Feature importance analysis shows that early academic progress indicators (approved units and semester grades) are the strongest predictors, followed by selected administrative/contextual factors such as tuition-fee status and course. Overall, this study provides empirical evidence supporting the use of ML techniques as effective decision-support tools for higher education institutions. The proposed framework offers actionable insights for administrators and policymakers seeking to develop data-driven strategies aimed at reducing dropout rates, improving academic success, and promoting equitable access to educational opportunities.

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.

This paper investigates the potential causes behind student dropouts in higher education institutions (HEIs), and explores the use of Lean Six Sigma (LSS) tools in reducing dropout rates. This qualitative study used 12 semi-structured interviews with university employees (n = 9) and LSS experts (n = 3), in order to understand the complexity of the student dropout phenomenon and the role of various LSS tools in reducing the dropout rates. Analysis revealed that in order to develop a typology of student dropouts, HEIs have to maintain detailed records and sensitise relevant authorities about the impact of a student’s dropout decision. Though the small number of semi-structured interviews is a limitation of the study, the revelations of HEIs authorities and LSS experts have given new impetus to look at and take action on the issue of student dropouts in HEIs.

Student dropout is a severe problem in higher education worldwide. In technical higher education, this issue is particularly relevant, as a significant proportion of students cannot meet the requirements of basic subjects, especially in science and technology, and drop out of higher education due to failures. The reduction of student dropout in higher education institutions is becoming an increasingly urgent task due to the deteriorating demographic indicators. One such possible solution is student mentoring, in which university students with good academic results mentor their peers in need. By analyzing the results of institutional research, the study focuses on the problem of student dropout, its causes and one of the ways to reduce dropout, contemporary mentoring.

Introduction: In Latin America, university dropouts significantly influence the individual development of students and the socioeconomic progress of the region. Identifying risk factors allows educational institutions to recognize and address the specific needs of their students to create effective methods that foster academic success and retention. Objective: Analyze the various risk factors for student Dropout in Higher Education Institutions in Latin America with the purpose of proposing strategies that reduce the negative rates of this problem. Methods: A bibliographic review of studies published between 2010 and 2024 on risk factors for student dropout in higher education in Latin America was carried out, using relevant databases and applying rigorous inclusion and exclusion criteria. Results: Personal, academic, institutional and socioeconomic variables are part of the risk factors for University Dropout. The lack of access to Higher Education has a significant impact on the economic and social growth of Latin America since it is essential for the training of qualified professionals. The high dropout rate contributes to a less prepared workforce and limits job opportunities for young people. Conclusions: The prevalent risk factors were academic and personal associated with university dropout in Latin America, which is why various strategies were proposed to generate inclusive and equitable higher education through the reformulation of public and institutional policies.

In the last 40 years in much of the western world dropout rates in higher education have remained almost unchanged. Although student retention seems to be the most studied and discussed aspect, nearly every empirical study on the causes of dropout in higher education and even more the impact of retention actions carried out by universities, in most cases have achieved modest results. This paper argues that this fact finds its explanation, to a certain extent, in the nature of the methodological approaches and factual supports of the empirical studies that most of those actions were based on. In this regard, there are strong arguments and empirical evidence that reveal the deficient nature of the factual basis of the most accepted models, theorizations and measurements on dropout in higher education. Among them are those that underlie the models proposed in 2012 by Vincent Tinto and Adam Seidman, the two main current references on the subject. The most significant questions point to the low reliability of the inferences produced from the application of surveys, especially the national survey of student engagement, very recurrently applied throughout the western world in empirical studies on dropout in higher education.

Dropout: Demographic Profile of Brazilian University Students

Although the number of students in higher education institutions (HEIs) has increased over the past two decades, it is far from assured that all students will gain an academic degree. To that end, institutional analytics (IA) can offer insights to support strategic planning with the aim of reducing dropout and therefore of minimizing its negative impact (e.g., on students, academic stakeholders, and institutions). However, it is not clear how institutional stakeholders can integrate IA in their practice to overcome academic-related issues and to offer support to students who struggle to achieve their academic goals. To address this gap, we conducted focus groups with 13 institutional stakeholders of an Estonian university. By analyzing the focus group data, we identified three main categories of factors influencing dropout from the perspective of institutional stakeholders: (1) institutional experience, (2) educational goals, and (3) personal aspects. We discuss our findings from an institutional perspective with the aim of reflecting on institutional processes, organizational structures, and facilitatory roles in the context of dropout in higher education (HE). We argue that IA can provide insights into students’ institutional experience, educational goals, and personal aspects to further support decision-making on the institutional level. We envision our findings contributing to a participatory agenda for the design, implementation, and integration of IA solutions focusing on addressing dropout in HE.

Purpose Harassment of women in higher education institutions (HEIs) is a global problem, and its widespread occurrence is having negative consequences for individuals, communities and institutions. Such negative consequences lead to a high rate of female dropouts in higher education. Ensuring a safe learning environment for females is important to provide educational opportunities and overcome such inequalities in HEIs. For this purpose, this study aims to identify the influence of harassment and the contributing factors that cause harassment against females in HEIs around the globe. Design/methodology/approach This study used a narrative literature review (NLR) design, targeting major electronic bibliographic databases for study selection. Eight themes emerged from the literature: concept, influence, challenges, mitigating strategies and global policies to address harassment against women in HEIs. Findings Imbalanced power dynamics, a lack of policies against harassment in higher institutions, a lack of accountability, the silence of women and the unstable and patriarchal structure of the organisation are some contributing factors that cause harassment against women in HEIs. Such harassment leads to various mental, emotional and social consequences, including anxiety, depression, social isolation, lack of confidence, insecurity, deficient trust, poor academic performance and a high dropout rate among females in higher education. Practical implications This paper includes all the necessary aspects of harassment against females in HEIs, including consequences, factors, challenges, mitigating strategies and policies around the globe. This detailed review of every aspect can be used to analyse the current situation of HEIs and fill the gaps in current anti-harassment policies to provide a safe learning environment for every individual. Originality/value This study covers all the aspects of harassment against females in HEIs across the globe that were not previously examined.

This paper presents a systematic literature review on the application of artificial intelligence to predict student dropout in higher education. It highlights that student dropout not only affects individuals, but also institutional resources and society in general. To address this problem, the application of artificial intelligence was proposed as a tool to anticipate and enhance the academic performance of students. Through the identification of risk factors related to dropout and the provision of personalized intervention strategies. In doing so, the aim is to improve student retention and academic success. In the systematic literature review presented here, the PICO and PRISMA methodologies were used. For this purpose, Scopus databases were exhaustively searched and a total of 110 records were identified. After applying the inclusion and exclusion criteria, 18 academic articles were selected for review. The results of the systematic review indicate that the application of artificial intelligence can be effective in predicting student dropout and improving student retention. Different machine learning and deep learning models were found to be used to identify at-risk students and provide personalized recommendations. In addition, the importance of collecting and analyzing historical data to improve the accuracy of forecasting models was emphasized.

Background: Dropout in higher education is a socio-educational phenomenon that has the scope to limit the benefits of education as well as to widen social disparities. For this reason, governments have implemented various public policies for its prevention and mitigation. However, in rural populations, such policies have proven to be ineffective. The aim of this paper is to simulate public policy scenarios for the treatment of school dropout in rural higher education in Colombia from a Dynamic Performance Management approach. Methodology: To achieve the aim, a parameterised simulation model was designed with data from Colombian state entities in rural higher education. Five simulations were carried out. The analysis of the results was carried out using descriptive statistics and comparison of means using the Wilcoxon Sign Rank statistic. Results: The adoption of such an approach based on simulations suggests that policies to expand the coverage of educational credits and financial support, as well as the addition of a family income subsidy, allow for a reduction in the number of dropouts. Conclusions: A dynamic, data-driven approach can be effective in preventing and mitigating dropout in these areas. It also highlights the importance of identifying the key factors contributing to dropout. The results also suggest that government policies can have a significant impact on school retention in rural areas.

Background of Study: Student dropout in higher education is influenced by a variety of factors including demographic, socioeconomic, macroeconomic, admission-related, and academic performance data. Accurately identifying students at risk of dropping out is a significant challenge within educational data mining (EDM), especially when working with large, complex datasets.Aims and Scope of Paper: This study aims to identify an optimal subset of features that can improve the accuracy of student dropout prediction. The scope includes comparing the effectiveness of different machine learning algorithms combined with a heuristic-based feature selection method to find the best-performing model.Methods: A Wrapper-based feature selection approach was employed using Ant Colony Optimization (ACO) as the search strategy. ACO was integrated with five classifiers—Random Forest (RF), Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Neural Network (NN)—to select the most relevant feature subsets. The performance of each combination was evaluated and compared.Result: The study found that ACO combined with Random Forest (ACO-RF) outperformed the other combinations in feature selection effectiveness. The selected features were then validated using various machine learning algorithms and a neural network. Among them, the neural network achieved the highest accuracy of 93%.Conclusion: The proposed ACO-RF wrapper method is an effective feature selection strategy for predicting student dropout in higher education. The method enhances model performance, especially when used with neural networks, and offers a promising approach for early identification of at-risk students.

Student dropout in higher education institutions (HEIs) is a serious problem in many developed and developing countries including Nepal. According to the University Grants Commission (UGC) report, many campuses are at risk and have a few number of students due to the high dropout rate. This paper aims to analyse student dropout in B.Ed. programme at Marsyangdi Multiple Campus (MMC) and explores the main causes of high dropouts with some measuring strategies to mitigate the problem. Following a quantitative survey through telephone interviews with 30 dropout students, I collected information and analysed it thematically in a narrative way. The findings of the study revealed that 65.47% of students left the campus before completing their degree. The study explored the major causes of high dropouts related to four indicators such as student, institutional, family, and community. Most of the students left their studies due to financial constraints, socioeconomic factors, institutional insufficiency, employment opportunities, study abroad, and early marriage and early pregnancy. The findings of the study apply to reducing the increasing number of dropouts from the university and other similar campuses. In addition to this, it implies formulating higher education policy and reforming the HEIs as per the demands of the time.

Student dropout in higher education is a global challenge that affects educational quality, institutional performance indicators, and students' academic development. With the increasing availability of educational data and advances in data mining techniques, unsupervised learning methods such as clustering have emerged as promising approaches for identifying dropout risk patterns without the need for labeled data. This study aimed to systematically map and analyze clustering techniques applied to segment students at risk of dropout in higher education between January 2010 and July 2025. To this end, a Systematic Literature Mapping was conducted following the PRISMA protocol, which included the stages of search, screening, data extraction, and thematic analysis of the studies. The results indicate that algorithms such as K-Means, DBSCAN, HDBSCAN, BIRCH, SOM and hierarchical methods have been used with varying levels of effectiveness, with internal validation metrics predominating. Academic and behavioral variables were the most frequently used in cluster formation, while psychosocial variables, although still limited, demonstrated significant potential. The findings show that clustering is a promising approach to support student retention strategies, although further progress depends on expanding datasets, strengthening validation methods, and integrating results into institutional policies designed to promote student persistence and academic success.