Determinants of academic desertion: A case study in a Peruvian university

Abstract

University dropout has a social impact on students who do not complete their higher education studies since it limits job opportunities and personal development. Therefore, the present study aimed to determine the factors influencing academic dropout in a Peruvian public university. A non-experimental, descriptive methodology was used with a sample of 50 dropout students; to whom a questionnaire was applied. The results show that socioeconomic factors have a greater impact on student dropout; furthermore, the factors that influence dropout differ according to the length of academic studies. These factors differ depending on the study area; personality and sociocultural factors have a greater incidence in engineering and social sciences. It is concluded that university dropout is not explained by a single factor but by the interaction of various elements of a socioeconomic, academic, and personal nature.

Keywords

Student dropout socioeconomic factors personality factors sociocultural factors academic factors

Introduction

In recent years, educational policies have promoted equal opportunities for access to higher education (AlKharouf et al., 2024; Chea, 2019), steadily increasing the number of enrolled students who were previously marginalized (García and Adrogué, 2021; Schelfhout et al., 2022). Despite these advances, challenges persist in retention and completion rates, with more students failing to complete their university studies than ever before (Abu-Saad, 2016; Sánchez and Singh, 2018), nevertheless, university dropout continues to be a structural challenge that reproduces the dynamics of social and economic inequality (Amaya-Amaya et al., 2020; Mgaiwa and Ishengoma, 2023).

Many students from marginalized backgrounds face financial constraints that hinder their ability to continue their studies (Guimaraes Junior et al., 2024). Disparities in educational infrastructure and support services contribute to high dropout rates (Meng, 2024). Limited access to technology exacerbates educational inequities, especially for students in remote areas (Amjad et al., 2024). Comprehensive support systems, including financial aid and academic advising, are essential to improve retention rates (Modupe et al., 2024; Oyebola and Chima, 2024). While significant progress has been made in promoting access to higher education, current retention and completion challenges underscore the need for a multi-faceted approach that not only facilitates entry but also ensures sustained support for marginalized students (Kumar, 2024; Mangat, 2024).

In this context, college dropout is a problem that affects all higher education institutions, that is, college dropout can lead to social and economic problems and an increase in underemployment (Cayón et al., 2021; Jia and Ericson, 2017); this occurs when a student leaves the educational system without completing his or her undergraduate studies (Castro-Martínez and Machuca-Téllez, 2023).

In the European Union, the average school dropout rate for young people aged 18 to 24 was 10.6% in 2017, highlighting a major problem in the region (Agrusti et al., 2019). Australia faces a consistent 15–18% dropout rate among its university students, with a notable proportion coming from rural and remote areas, emphasizing the need to understand the experiences of students from these regions to address the problem effectively (Ghanboosi and Saleen, 2013). These statistics highlight the global challenge of student attrition in higher education and the importance of tailored interventions to improve retention rates in different countries (García de Fanelli and Adrogué de Deane, 2015; Munizaga Mellado et al., 2018).

In Latin America, where social inequalities are so pronounced, the average university dropout rate is 30%, that is, out of every 100 students, 30 abandon their studies before graduating-(Instituto Internacional para la Educación Superior en América Latina y el Caribe, 2021). Argentina reported a 38% dropout rate among university students ages 18 to 30 in 2013, with disparities based on socioeconomic status, gender, and region of residence (Viloria et al., 2020).

University dropout in Peru has been influenced by a variety of significant indicators, which can be classified into academic, socioeconomic, and institutional factors (Miño, 2021; Preciado-León et al., 2022). Understanding these indicators is crucial to developing effective interventions to reduce dropout rates (Jiménez et al., 2023). Academic performance, including standardized academic indices and percentage of credits passed, has been closely related to dropout rates (Quincho Apumayta et al., 2024). The risk of dropout peaks during the third semester, indicating that students may have difficulty adapting to the demands of college early on (Pebes, 2016).

According to the III biennial report of the National Superintendence of University Higher Education (SUNEDU), in 2020 the dropout rate was 18.1%; however, public universities had a lower rate of interruption at the national level (SUNEDU, 2022). Research indicates that dropout rates in health sciences careers range between 5.64% in dentistry and 11.11% in obstetrics (Valero et al., 2022). Among the factors that influence school dropout are vocational and economic reasons, since universities lack adequate plans to evaluate possible school dropout (Heredia Alarcón et al., 2015). In addition, predictive models have been developed to identify students at risk of dropping out, which contributes to a reduction in failure rates between 25% and 40% in courses with low passing rates (Sifuentes Bitocchi, 2018).

Academic burnout is also a major issue, with students in professional education majors experiencing moderate levels of burnout, highlighting the need for programs to help students cope with stress and decrease the prevalence of burnout (Estrada Araoz et al., 2021). Furthermore, discrete-time survival models have been applied to study student dropout at a private university in Lima, indicating that various academic, economic, and sociodemographic factors are associated with the risk of dropping out of school (Pebes, 2016).

Therefore, academic dropout is a complex, multi-faceted problem that cannot be addressed solely by analyzing historical data, since the conditions and situations in each educational environment are variable. Therefore, the study aims to determine the factors that affect academic dropout in a Peruvian public university, with a focus on the various fields of study, as well as the period of study during the first four academic cycles and the following four. Since it is a fundamental indicator in the accreditation and renewal processes of operating licenses of university centers.

While these indicators highlight the challenges faced by students, it is also important to consider that some students may thrive in less structured environments, suggesting that a one-size-fits-all approach to retention may not be effective. Tailoring support to individual needs could enhance student success and reduce dropout rates.

There are studies on academic dropout that focus only on one dimension of study, without addressing the issue holistically. Studies on academic performance, for example, Gallego et al. (2021), point out that students with low cognitive abilities experience a decrease in their academic performance, which can lead to dropping out. On the other hand, Cayón et al. (2021) mention that, in an institution in Colombia, factors such as the educational level of high school, performance on the entrance test, and good performance in the admission process influence academic performance and are possible predictors of university dropout. Additionally, some studies focus on specific services; Brito et al. (2022) highlight that the childcare service contributes significantly to academic performance by reducing dropout rates in the health sciences faculty of a university in Mexico.

On the other hand, some studies focus exclusively on engineering careers. Ortiz-Martínez et al. (2023) indicate that, in a private university in Mexico, students decide to abandon their studies due to the competitive environment and the lack of empathy on the part of teachers when teaching classes. For its part, Alvarez et al. (2020) point out that, in higher education in Cuba, computer engineering students abandon their studies in the first year due to their origin, academic performance, and score on the entrance exam.

Studies have also been conducted that seek to determine the dimensions that influence academic dropout, focusing on enrolled students rather than those who dropped out. Valencia-Arias et al. (2023) evidence that factors such as institutional support and economic situation have an impact on academic dropouts in Colombia. On the other hand, Burgos et al. (2022) identified that, at Atacama University, one of the causes of dropout among students is academic performance, as well as economic and health problems.

Research studies have widely explored the factors associated with college dropout, including socioeconomic factors, personality factors, sociocultural factors, and academic factors. Socioeconomic challenges, such as financial constraints and lack of support, have a significant impact on school dropout rates (Miño, 2021; Rizo Rodríguez, 2020). Personality traits, study habits, and academic performance also play a crucial role in student retention (Alban and Mauricio, 2018). Sociocultural influences, such as family dynamics and social expectations, can affect a student’s decision to continue their education (Ruiz Palacios, 2018). Furthermore, the pedagogical approach and teaching strategies employed by institutions can hinder or support student persistence, highlighting the importance of the Academic Factor in addressing dropout rates (Peña Fernández, 2019). These factors collectively contribute to the complex landscape of college dropout, emphasizing the need for targeted interventions to improve student success and retention.

Methodology

Design of the investigation

A longitudinal non-experimental methodology was used, given that the study variables were not intentionally manipulated, and data spanning 5 years (2019 to 2023) were analyzed. Furthermore, the approach was quantitative and descriptive, according to the objectives of the research (Hernández et al., 2018).

Participants

This research was carried out with the participation of students from the Toribio Rodríguez de Mendoza National University of Amazonas, who entered in the year 2019-I (first semester of the year) and stopped studying during the following academic cycles, making a total of 106 students who dropped out according to the information provided by the area of the General Directorate of Academic Records.

According to the inclusion criteria, students who have completed at least one academic year and were not currently enrolled were selected.

Convenience sampling was used according to the availability and accessibility of the participants, obtaining a total of 50 students who decided to participate in the study, and who were previously informed about the purpose of the research.

Instrument

The questionnaire was adapted from Ventura-Romero et al. (2019) which is designed to evaluate student dropout in university students. The instrument was developed in Google Forms and consists of two sections: general data and academic dropout domains. Includes 19 items with a Likert rating scale that varies from “never and poor” with 1 point, “rarely and average” with 2 points, “sometimes and good” with 3 points, “almost always and very good” with 4 points, to “always and excellent” with 5 points.

Analysis of data

It was carried out through a descriptive analysis using SPSS v26 software, which allowed an interpretation of the results according to the factors that affect dropout.

Results

Of the total number of students who entered the 2019-I academic semester, only 3% stopped studying temporarily or permanently, of which 72% were men. Within that group, 36% reserved their enrollment, 28% stopped studying permanently, and 18% changed their professional career. The main reasons for dropping out were different depending on the academic cycle: in the first cycle, the majority withdrew due to financial problems and lack of motivation with the degree, while, from the fifth to eighth cycle, the main reason was that they were presented with opportunities for labor.

Students who temporarily or permanently withdrew from social science majors represented 46% of the total. Of this group, 28% retired permanently, while 8% switched to study another career path, and only 8% remained intent on returning.

As for students who temporarily or permanently withdrew from engineering careers, they represented 42% of the total. Of this group, 14% withdrew permanently, 8% switched to another career path, and 20% reserved their enrollment to return to study. Figure 1 presents the factors that influence university dropout, comparing the impact levels of each factor at different stages of the academic cycle.

Figure 1.

Note. Data elaborated from the questionnaire applied.

Factors that influence university dropout

Academic, personality, and socioeconomic factors present a medium level of incidence of university dropout. On the other hand, the socioeconomic factor stands out as the one with the highest incidence, registering a high level of impact on dropout, which suggests that the economic conditions and social status of students play a crucial role in their ability to continue and complete their academic training. In contrast, the sociocultural factor is the one that has the least impact on withdrawal from university studies, indicating that the family and cultural environment of the students does not significantly influence the decision to leave the university.

These findings highlight the need to implement strategies and support programs focused on addressing the financial needs of students, whether through scholarships, student financing, or work-study programs. Additionally, it is essential to provide academic advising and career guidance services to help students develop study skills, time management, and adaptation to university life, factors that can also contribute to student retention. Understanding the academic period with the highest drop-out rates is crucial. Figure 2 shows this period together with the factors associated with dropping out, giving an overview of the behaviour of students who drop out.

Figure 2.

Note. Data elaborated from the questionnaire applied.

Study period

The results indicate that, during the first four academic cycles, personality and sociocultural factors have a greater impact on the temporary or permanent dropout of university students, which suggests that, in the initial stages of academic training, individual characteristics of students, as well as their family and cultural environment, play a crucial role in their ability to adapt and persist in the university environment. Likewise, during these first four academic semesters, academic and socioeconomic factors present a medium level of incidence in university dropout, implying that, although they are not the predominant factors, academic difficulties, and economic conditions also contribute to the decision to abandon the university studies at this stage.

However, from the fifth to the eighth cycle of studies, only the academic factor maintains a medium level of incidence in university dropouts, suggesting that as students advance in their training, academic difficulties become the most relevant factor. This can lead them to drop out, in contrast to the sociocultural factor that has a lower impact on student dropout throughout the eight academic cycles analyzed. Attrition also varies according to fields or specialisations of study. Figure 3 shows that the fields with the highest drop-out rates are engineering and social sciences.

Figure 3.

Note. Data elaborated from the questionnaire applied.

Field of studies

The results indicate that personality and sociocultural factors have a greater influence on the dropout of engineering and social sciences students, suggesting that individual characteristics, family, and cultural environment play a crucial role in the persistence and adaptation of students. In these academic areas, while, in the case of health sciences students, although these factors also exert a certain influence, this is minor compared to the other areas mentioned, indicating that other factors have a greater impact on the decision to drop out. In these health-related careers, on the other hand, academic and socioeconomic factors have a medium-level impact on professional engineering careers, which means that the academic difficulties and economic conditions of students contribute moderately to dropouts in these areas of study, making it necessary to implement strategies and support programs focused on addressing these problems.

Discussion

In this study, it was identified that the factor with the highest incidence of university dropout is socioeconomic and at a medium level, academic and personality factors. These results coincide with previous studies, such as those of Burgos et al. (2020), who affirm that economic and personal factors have a greater influence on dropping out of university studies, while lack of motivation is the main reason for students to reserve their enrollment with the possibility of returning. Valencia-Arias et al. (2023) indicate that, in the Colombian context, the personal factor related to problems of social adaptation to new environments is the one with the highest incidence of dropout.

Bayona-Oré (2024) indicates that the factors with the greatest impact are more academic than economic, such as the lack of communication with teachers, the entry level in the admission exam, and the perception of the usefulness of the degree. Similar to the results of this study, since academic factors affect a medium level, where students present problems in their basic subjects in the first year of studies, in addition, they do not perceive the usefulness of their professional careers, generating demotivation. In addition, Garcés-Prettel et al. (2024) mention that the economic and psychosocial factor increases the risk of dropout. However, these previous studies have focused on evaluating enrolled students and not those who have dropped out.

The factors that affect academic dropout vary depending on the academic cycle in which students withdraw from the university. During the first four cycles, personality and sociocultural factors have a greater impact on temporary or permanent dropouts. On the contrary, from the fifth to the eighth cycle of studies, only the academic factor has a greater influence on university dropout. However, it has been found that few studies emphasize these differences by academic periods. Alvarez et al. (2020) indicate that it is the first-year students who have the highest levels of dropout. These results coincide with those of this study, which also identified a higher percentage of dropouts in the first four cycles. In addition, Cervantes and Guerra (2023) establish that students in the first year of studies face barriers both personal and institutional, which cause desertion from the professional career.

Although this study has not focused solely on the first years, it also agrees that personal factors influence dropout, especially due to a lack of understanding of the areas of knowledge, particularly in engineering careers. On the other hand, Ritorni et al. (2022), point out that the lack of communication between students and teachers in the first year of studies leads to frustration and abandonment of studies. Finally, in the Chilean context, Gangas et al. (2022) indicate that the family and economic factors are the ones with the highest incidence of dropout, where students stop studying due to lack of work or the loss of a family member who supported their educational process.

Differences were identified in the factors that affect academic dropout depending on the area of study. In the case of engineering and social sciences students, personality and sociocultural factors have a greater influence on the decision to drop out, while, for health sciences students, although these same factors also exert some influence, their impact is minor compared to the other areas mentioned. In a different context, like the Cuban one, Alvarez et al. (2020) affirm that, in computer engineering careers, the factors with the greatest impact are the entrance exam grade and academic performance in mathematics during the first academic year.

In addition, in Mexico, Ortiz-Martínez et al. (2023) indicate that, in engineering careers, aspects such as the competitive environment and the difficulty of teaching by teachers who are not very empathetic were decisive in dropping out. However, in the Peruvian context, the results coincide with what was found in this study. Davila et al. (2022) affirm that, in the Business Administration career belonging to the social sciences, the main dropout factors were personal, educational, and institutional, reaffirming that personal problems have a greater impact on the decision to abandon university studies. Cayón et al. (2021) guarantee that, in social science administration careers, sociodemographic factors such as age and gender are predictors of dropout. Finally, in the case of health sciences, Brito et al. (2022) indicate that students who are already mothers have difficulties in raising their children that do not allow them to complete their studies.

College dropout is a multidimensional phenomenon that transcends linear explanations, as shown by studies by Amjad et al. (2024) and Guimaraes Junior et al. (2024). Beyond the socioeconomic and academic factors traditionally analyzed, there is a complex web of individual motivations that include personal, emotional, and psychological dimensions. Research by Estrada Araoz et al. (2021) on academic burnout and Alban and Mauricio (2018) on personality traits reveal that emotional exhaustion, lack of adjustment, and mental health problems can be critical determinants in the decision to drop out. Furthermore, Ortiz-Martínez et al. (2023) and Alvarez et al. (2020) have documented how institutional factors such as competitive environment, teaching quality, and lack of empathy can erode student motivation. Therefore, understanding attrition requires a holistic approach that integrates individual, institutional, and contextual dimensions, recognizing that each student trajectory is unique and responds to an intricate web of experiences and conditioning factors.

The findings of this study reveal the multifactorial complexity of college dropout, but open new perspectives for future research exploring the less visible dimensions of the educational ecosystem. The organizational culture of higher education institutions emerges as an unexplored terrain with a significant potential for understanding the mechanisms of student retention. As suggested by Valencia-Arias et al. (2023) and Burgos et al. (2022), institutional support is crucial, but it is necessary to delve deeper into how power structures, spaces for student participation, and extracurricular opportunities can transform the educational experience. Future research could benefit from a more critical analysis of the degree of agency afforded to students in decision-making, exploring how hidden curriculum barriers—beyond the socioeconomic and academic factors traditionally studied—may be reproducing dynamics of institutional exclusion and disengagement.

Implementing coordinated strategies against college dropouts requires a systemic and collaborative approach, as suggested by Modupe et al. (2024), Oyebola and Chima (2024), and Kumar (2024). One notable model is the University of British Columbia’s Student First program, which integrates financial support, academic advising, and mental health services. Following the predictive models of Sifuentes Bitocchi (2018) which have reduced failure rates by up to 40%, institutions can develop early warning systems, mentoring programs among students, and accompaniment routes that address academic, emotional, and social dimensions, as recommended by Valencia-Arias et al. (2023) and (Burgos et al., 2022). The key is to transform educational structures from rigid spaces to dynamic and personalized support communities, as emphasized by (Amaya-Amaya et al., 2020; Mgaiwa and Ishengoma, 2023).

In this sense, the study raises the need to develop more holistic research frameworks that transcend quantitative dropout analyses. As Miño (2021) and Rizo Rodríguez (2020) point out, that socioeconomic challenges are fundamental but building a sense of university community could be a crucial mediating factor not yet fully explored. Future lines of research could focus on designing methodologies that assess how institutions can create comprehensive support ecosystems that not only address economic and academic needs but also build spaces of belonging, recognition, and student empowerment. This involves researching more participatory models of university governance, peer mentoring programs, integration strategies that recognize the diversity of student trajectories, and mechanisms that allow students to be co-creators of their educational experience, rather than passive recipients of a predetermined curriculum.

Conclusions

University dropout is not explained by a single factor, but by the interaction of various socioeconomic, academic, and personal elements, essential to address the problem effectively and develop strategies that promote student retention in educational institutions.

In the first four academic cycles, personality, sociocultural, socioeconomic, and academic factors have a greater impact on dropping out of studies; while, in subsequent cycles, academic factors are the most determining, since as students advance in their training, academic difficulties become the most relevant factor.

There is a greater dropout in areas such as engineering and social sciences, due to academic, personality, and sociocultural factors, unlike health sciences where these factors have less impact.

Given the relevance and magnitude of the problem of university dropout, higher education institutions and policymakers must work in a coordinated manner to implement comprehensive solutions that address the multiple factors identified in this study.

Students who abandon their university studies change their telephone numbers and it is difficult to communicate with them, they do not respond or they do not wish to participate in the study due to lack of time.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Angelica María Carrasco Rituay

Omer Cruz Caro

Data availability statement

The data is available on request from the corresponding author.*

References

Abu-Saad

(2016) Access to higher education and its socio-economic impact among Bedouin Arabs in Southern Israel. International Journal of Educational Research 76: 96–103. DOI: 10.1016/j.ijer.2015.06.001.

Agrusti

Bonavolontà

Mezzini

(2019) University dropout prediction through educational data mining techniques: a systematic review. Journal of E-Learning and Knowledge Society 15(3): 162–182. Available at: https://doi.org/10.20368/1971-8829/1135017

Alban

Mauricio

(2018) Factors that influence undergraduate university desertion according to students perspective. International Journal of Engineering and Technology 10(6): 1585–1602. DOI: 10.21817/ijet/2018/v10i6/181006017.

AlKharouf

Shehadeh

Alrefaee

, et al. (2024) Integrative strategies for social inclusion and equity: enhancing refugee access to higher education in Jordan. Heliyon 10(11): e31762. DOI: 10.1016/j.heliyon.2024.e31762.

Alvarez

Callejas

Griol

(2020) Factors that affect student desertion in careers in Computer Engineering profile. Revista Fuentes 22(1): 105–126. DOI: 10.12795/revistafuentes.2020.v22.i1.09.

Amaya-Amaya

Huerta-Castro

Flores-Rodríguez

(2020) Big data, una estratégica para evitar la deserción escolar en las IES. Revista Iberoamericana de Educacion Superior 11(31): 166–178. DOI: 10.22201/iisue.20072872e.2020.31.712.

Amjad

Aslam

Tabassum

, et al. (2024) Digital equity and accessibility in higher education: reaching the unreached. European Journal of Education 59(4): 1–13. DOI: 10.1111/ejed.12795.

Bayona-Oré

(2024) Dropout in computer science, systems engineering and software engineering programs. Lecture Notes in Networks and Systems 800: 592–599. DOI: 10.1007/978-3-031-45645-9_57.

Brito

Tdel J

Javier

Ruíz

, et al. (2022) Permanencia y deserción en los estudiantes del área de la salud usuarios de centro de aprendizaje infantil de una universidad del sureste de México. Revista Universidad y Sociedad 14(3): 646–653.

10.

Burgos

Aliaga

Tesmer

, et al. (2020) Vista de Causas de abandono estudiantil, dónde, cómo y cuántas GUIA. International Journal of Educational Research and Innovation 18: 1–17.

11.

Burgos Videla

Aliaga Prieto

Tesmer Orellana

, et al. (2022) Causales del abandono estudiantil, dónde, cómo y cuántos: el caso de la Universidad de Atacama. International Journal of Educational Research and Innovation 18: 1–17. https://orcid.org/0000-0003-3181-8801.Buscarycorregirlarefrencia

12.

Castro-Martínez

Machuca-Téllez

(2023) La deserción universitaria en América Latina: una perspectiva ecológica. Estudios Pedagógicos (Valdivia) 49(2): 87–108. DOI: 10.4067/s0718-07052023000200087.

13.

Cayón

Correa

Scarpetta

(2021) Caracterización de los programas académicos y determinación de las variables que afectan el rendimiento de los estudiantes en el Colegio de Estudios Superiores de Administración, CESA. Formacion Universitaria 14(5): 175–186. DOI: 10.4067/S0718-50062021000500175.

14.

Cervantes

Guerra

(2023) Work in progress: impact on students dropout rates of introducing a first-year hands-on civil engineering course. In: ASEE annual conference and exposition, conference proceedings. ASEE Conferences, 1–13. DOI: 10.18260/1-2--44275.

15.

Chea

(2019) Does higher education expansion in Cambodia make access to education more equal? International Journal of Educational Development 70: 102075. DOI: 10.1016/j.ijedudev.2019.102075.

16.

Dávila

Eucaris

Héctor

, et al. (2022) Volumen 14| Número 2 | Marzo-Abril. Universidad y Sociedad. https://orcid.org/0000-0003-3181-8801---Buscarycorregirlarefrencia

17.

Estrada Araoz

Gallegos Ramos

Mamani Uchasara

(2021) Burnout académico en estudiantes universitarios peruanos. Apuntes Universitarios 11(2): 48–62. DOI: 10.17162/au.v11i2.631.

18.

Gallego

Perez de los Cobos

Gallego

JCG

(2021) Identifying students at risk to academic dropout in higher education. Education Sciences 11(8): 427. DOI: 10.3390/educsci11080427.

19.

Gangas

Mufdi

Tucas

(2022) Causes of first year student dropout, private university of Chile. Universidad Privada de Chile. Human Review. International Humanities Review / Revista Internacional de Humanidades 11: 1–11. DOI: 10.37467/revhuman.v11.4043.

20.

Garcés-Prettel

De la Ossa-Robinson

Arellano-Cartagena

, et al. (2024) Return or not to return to in-person classes? Motivations and fears that influence university dropout in Colombia in the post-pandemic era. Salud Uninorte 40(1): 52–68. DOI: 10.14482/sun.40.01.159.456.

21.

García

Adrogué

(2021) Equidad en la educación superior latinoamericana: dimensiones e indicadores. Revista Educación Superior y Sociedad 33: 85–114.

22.

García de Fanelli

Adrogué de Deane

(2015) Abandono de los estudios universitarios: dimensión, factores asociados y desafíos para la política pública. Revista Fuentes 16: 85–106. DOI: 10.12795/revistafuentes.2015.i16.04.

23.

Ghanboosi

Saleen

(2013) Factors influencing students’ attrition at sultan Qaboos university (SQU). Education 113(4): 513–524.

24.

Guimaraes Junior

Costa

Souza

, et al. (2024) Promovendo acesso equitativo à educação para comunidades marginalizadas e grupos vulneráveis. Revistaft: Ciencias Humanas 28(138): 46–47. DOI: 10.69849/revistaft/fa10202409302046.

25.

Heredia Alarcón

Andía Ticona

Ocampo Guabloche

, et al. (2015) Deserción estudiantil en las carreras de ciencias de la salud en el Perú. Anales de La Facultad de Medicina 76: 57. DOI: 10.15381/anales.v76i1.10972.

26.

Hernández

Fernández

Baptista

Mdel P

(2018) Metodologia de la investigacion. New York, NY: Mc Graw Hill.

27.

Instituto Internacional para la Educación Superior en América Latina y el Caribe (2021) 360-Texto del artículo-1846-1-10-20211203 (3). Revista Educación Superior y Sociedad 32(2): 402–421.

28.

Jia

Ericson

(2017) Equity and access to higher education in China: lessons from Hunan province for university admissions policy. International Journal of Educational Development 52: 97–110. DOI: 10.1016/j.ijedudev.2016.10.011.

29.

Jiménez

Jesús

Wong

(2023) Model for the prediction of dropout in higher education in Peru applying machine learning algorithms: random forest, decision tree, neural network and support vector machine. In: 2023 33rd conference of open innovations association (FRUCT), Publisher: IEEE, 116–124. DOI: 10.23919/FRUCT58615.2023.10143068.

30.

Kumar

(2024) Aspirations for higher education among mahadalit students and strategies for overcoming urban marginalities in India. Asian Journal of Education and Social Studies 50(6): 191–201. DOI: 10.9734/ajess/2024/v50i61405.

31.

Mangat

(2024) Inclusivity in higher education: analysing the implementation of nep 2020 for marginalized communities in India. ShodhKosh: Journal of Visual and Performing Arts 5(1): 1900–1910. DOI: 10.29121/shodhkosh.v5.i1.2024.1990.

32.

Meng

(2024) Education policy and social justice: a policy framework analysis for improving educational opportunities for marginalized groups. Journal of Education and Educational Policy Studies 2(1): 5–11. DOI: 10.54254/3049-7248/2/2024008.

33.

Mgaiwa

Ishengoma

(2023) Financing higher education in Tanzania through students’ loans scheme and its impact on equitable access. Heliyon 9(3): e13943. DOI: 10.1016/j.heliyon.2023.e13943.

34.

Miño

(2021) Factores condicionantes de la deserción universitaria. Ciencia Latina Revista Científica Multidisciplinar 5(4): 5316–5328. DOI: 10.37811/cl_rcm.v5i4.691.

35.

Modupe

Fiyinfolu

Olufemi

(2024) Fostering educational equity through leadership and policy innovations for sustainable development in higher education. International Journal of Frontline Research in Multidisciplinary Studies 4(1): 35–44. DOI: 10.56355/ijfrms.2024.4.1.0031.

36.

Munizaga Mellado

Cifuentes Orellana

Beltrán Gabrie

(2018) Retención y abandono estudiantil en la educación superior universitaria en América Latina y el Caribe: Una Revisión Sistemática. Education Policy Analysis Archives 26: 61. DOI: 10.14507/epaa.26.3348.

37.

Ortiz-Martínez

Vázquez-Villegas

Ruiz-Cantisani

, et al. (2023) Analysis of the retention of women in higher education STEM programs. Humanities and Social Sciences Communications 10(1): 101. DOI: 10.1057/s41599-023-01588-z.

38.

Oyebola

Chima

(2024) Equity and access in higher education: legal perspectives and management strategies. International Journal of Science and Research Archive 11(2): 199–206. DOI: 10.30574/ijsra.2024.11.2.0391.

39.

Pebes

(2016) An application of discrete time survival models to analyze student dropouts at a private university in Peru. [Thesis of Master, Pontificia Universidad Catolica del Peru] https://hdl.handle.net/20.500.12404/6992.

40.

Peña Fernández

(2019) Los factores pedagógicos influyen en la deserción universitaria. INNOVA Research Journal 4(3): 108–115. DOI: 10.33890/innova.v4.n3.2019.996.

41.

Preciado-León

Huerta-Hernández

Vera-Noriega

JÁ

, et al. (2022) Causas asociadas a la deserción escolar en educación superior. Una revisión sistemática del 2010 al 2020. Ra Ximhai 18(1): 83–101. DOI: 10.35197/rx.18.01.2022.04.jp.

42.

Quincho Apumayta

Carrillo Cayllahua

Ccencho Pari

, et al. (2024) University dropout: a systematic review of the main determinant factors. F1000Research 13: 942. DOI: 10.12688/f1000research.154263.1.

43.

Ritorni

Gómez

Oestmann

, et al. (2022) Factors associated to desertion of students of the virtual career of Advocacy in a public university | Fatores associados à evasão de alunos da carreira virtual de Direito em uma universidade pública | Factores asociados a la deserción de estudiantes en l. Sapienza 3(1): 21–33. DOI: 10.51798/sijis.v3i1.144.

44.

Rizo Rodríguez

(2020) Factores que inciden en la deserción estudiantil de la Carrera Ingeniería en Sistemas de Información en la FAREM-Estelí, UNAN-Managua. Nicaragua. Revista Científica de FAREM-Estelí 33: 35–51. DOI: 10.5377/farem.v0i33.9607.

45.

Ruiz Palacios

(2018) Factores que influyen en la deserción de los alumnos del primer ciclo de educación a distancia en la Escuela de Administración de la Universidad Señor de Sipán. Períodos académicos 2011-1 al 2013-1: lineamientos para disminuir la deserción. Educación 27(52): 160–173. DOI: 10.18800/educacion.201801.009.

46.

Sánchez

Singh

(2018) Accessing higher education in developing countries: panel data analysis from India, Peru, and Vietnam. World Development 109: 261–278. DOI: 10.1016/j.worlddev.2018.04.015.

47.

Schelfhout

Wille

Fonteyne

, et al. (2022) How accurately do program-specific basic skills predict study success in open access higher education? International Journal of Educational Research 111: 101907. DOI: 10.1016/j.ijer.2021.101907.

48.

Sifuentes Bitocchi

(2018) Modelos predictivos de la deserción estudiantil en una universidad privada peruana. Industrial Data 21(2): 47–52. DOI: 10.15381/idata.v21i2.15602.

49.

Superintendencia Nacional de Educación Superior Universitaria (SUNEDU) (2022, March 17) III Informe Bienal sobre la Realidad Universitaria en el Perú. https://www.gob.pe/institucion/sunedu/informes-publicaciones/2824150-iii-informe-bienal-sobre-la-realidad-universitaria-en-el-peru

50.

Valencia-Arias

Chalela

Cadavid-Orrego

, et al. (2023) University dropout model for developing countries: a Colombian context approach. Behavioral Sciences 13(5): 382. DOI: 10.3390/bs13050382.

51.

Valero

Navarro

ÁF

Larios

, et al. (2022) Deserción universitaria: Evaluación de diferentes algoritmos de Machine Learning para su predicción. Revista de Ciencias Sociales. DOI: 10.31876/rcs.v28i3.38480.

52.

Ventura-Romero

Lobos-Rivera

Ricardo Gutiérrez-Quintanilla

, et al. (2019) Construcción, validación y confiabilidad de escala de medición de deserción estudiantil universitaria Construction, validation and reliability of a scale to measure the retention of college students. Revista Entorno, Universidad Tecnológica de El Salvador. DOI: 10.5377/entorno.v0i67.74987.

53.

Viloria

Senior Naveda

Hernández Palma

, et al. (2020) Using big data to determine potential dropouts in higher education. Journal of Physics: Conference Series 1432(1): 012077. DOI: 10.1088/1742-6596/1432/1/012077.