University Dropout in Granada: A Biographical Narrative Study Addressing Student Diversity Based on External Factors

Abstract

University dropout is a problem that affects all universities around the world. It is multidimensional and multicausal in nature. The consequences of a student dropping out affect them not only financially but also in terms of their self-perception. In this article, an analysis of in-depth interviews was carried out to determine what motivations students have when leaving classrooms to provide solutions to the new cohorts that enter. The sample consisted of 21 students, including 14 men and 7 women, from different Andalusian universities. A logical minimization analysis was carried out, showing the necessary implications for each of the subjects who participated. Afterwards, frequency and percentage analyses were carried out for each of the dimensions that made up the interview, with success in primary education having the lowest percentage (4.7%) and academic orientation having the highest percentage (90.47%).

1. Introduction

Many studies have addressed university dropout from different perspectives, considering the different agents involved. Since it is not a single factor that causes students to leave higher education classrooms, university dropout is considered to be multidimensional and multicausal in nature (Fernández Cruz et al., 2024); the consequences are mainly social, specifically for the individual’s understanding of the subject, their self-concept, and their self-esteem. University dropout also has an impact at a macro-social level, since there is a direct relationship between university dropout and an increase in the unemployment rate (Arce et al., 2015; Esteban et al., 2016; Ferreyra et al., 2021; Galve-González et al., 2022 Tayebi et al., 2021), which also affects the economy.

In order to define university dropout, it is essential to understand the context that influences people to drop out. One of the most current definitions is the one proposed by Fernández Cruz et al. (2024), who defines it “as the situation of a student who has not graduated on time and is not enrolled in his or her undergraduate degree or in any other degree program at any other university for two consecutive years, so that the student has been left out of the system” (p. 1).

1.1. University Study Dropout and Inclusive Education

Based on the work of Mortagy et al. (2018), Romito et al. (2020), and Constante-Amores et al. (2021), the taxonomy of the most common factors causing university dropout can be classified into the following typologies: demographic, socioeconomic, and academic factors.

The first typology refers to student characteristics, such as age and gender. Research conducted by Freixa Niella et al. (2017), as well as data from the National Institute of Statistics (INE, 2022), indicates that the highest proportion of university dropouts comprises younger students. With regard to gender and dropout in higher education, previous empirical studies reveal complex and context-dependent patterns. Overall, women tend to present a higher risk of dropping out before completing their studies, but this trend varies according to institutional and sociocultural contexts.

Study conditions also affect male and female students differently. Specifically, women’s intentions to drop out increase in highly competitive or high-performance academic environments, whereas men tend to benefit more from structured study contexts (Marczuk & Strauss, 2023).

The second group of factors relates to students’ economic capacity to financially support themselves or to be supported while pursuing university studies. Munizaga Mellado et al. (2018) and González-Ramírez and Pedraza-Navarro (2017) indicate that students with greater economic power exhibit lower dropout rates in higher education. Consequently, research on university dropout highlights the persistence of significant social inequalities, as students from lower socioeconomic backgrounds face a substantially higher risk of leaving higher education prematurely (Müller & Klein, 2022; Herbaut, 2020).

Notably, these inequalities persist even when controlling for academic preparation, as students with higher academic ability are less likely to drop out following academic failure (Herbaut, 2020). In addition, while financial aid mechanisms, such as scholarships, may mitigate some of these challenges, they do not fully eliminate the structural disadvantages experienced by certain groups, including Black and working-class students, as evidenced in the South African context (Masutha, 2022).

To address these disparities, scholars recommend integrating academic integration models with rational choice theory (Müller & Klein, 2022) and considering the interaction between social background and academic performance when analyzing dropout processes (Herbaut, 2020). Finally, academic factors play a significant role in university student dropout. Previous research shows that poor academic performance often precedes the decision to withdraw from higher education (Belloc et al., 2010; Acevedo Calamet, 2020). Among the main academic variables associated with dropout are the level and field of study, the extent to which curricula promote real-world problem-solving skills, the quality of teacher feedback, perceived fairness in assessment processes, clarity of learning objectives, and overall satisfaction with faculty members (Androulakis et al., 2021). In addition, limited academic offerings within disadvantaged socio-academic contexts may reduce student motivation and increase the likelihood of dropout (Acevedo Calamet, 2020). Similarly, a study conducted in Indonesia identified academic dissatisfaction as one of the most influential factors contributing to university dropout, alongside other non-academic causes (Nurmalitasari et al., 2023).

Considering these academic factors, teaching staff emerge as a central element in students’ persistence or withdrawal. Well-trained university teachers who recognize and respond to student diversity are essential to ensuring quality and inclusive education, particularly in a context where student needs are increasingly diverse and dynamic. Teaching in higher education extends beyond the transmission of disciplinary knowledge to include pedagogical, technological, emotional, and ethical competencies that enable effective and holistic educational practice (Pérez-Rodríguez et al., 2021).

However, in many university settings, academic staff are highly specialized in their disciplines but lack sufficient pedagogical and inclusive training, which can hinder meaningful engagement with students and limit learning outcomes. For this reason, it is crucial that universities implement continuous professional development programs that support faculty in adopting innovative teaching methodologies, inclusive strategies for addressing classroom diversity, and the effective use of digital technologies (Bennasar-García et al., 2021).

Pedagogical and Inclusive Skills in University Teachers

A central axis of teacher training is the development of pedagogical skills. These skills not only seek to ensure that teachers master their area of specialization but also act as learning facilitators, designing dynamic and personalized educational experiences that respond to the needs of students and making the university more inclusive.

Some of the most relevant skills are as follows:

• Instructional design: The ability to develop clear, structured study plans aligned with measurable learning objectives, integrating activities that promote the active participation of students.
• Student-centered teaching: The application of methodologies such as project-based learning, collaborative learning, and the use of practical cases, which encourage student commitment and autonomy.
• Formative assessment: The use of assessment tools not only to measure academic performance, but also to provide constructive feedback that drives personal and academic growth.

In short, teacher training not only strengthens the teacher’s pedagogical abilities but also becomes a pillar to promote an enriching, inclusive, and transformative university experience (García & Morales, 2017).

2. Materials and Methods

The methodology used in our research follows a biographical narrative design. This type of qualitative research approach focuses on the collection and analysis of personal accounts and life experiences of participants. Its purpose is to understand the perceptions, meanings, and constructions of reality from the perspective of the participants themselves (Bolívar et al., 2001). This methodology is based on the idea that narratives and biographies not only reflect personal experiences but are also influenced by sociocultural and historical contexts (Connelly & Clandinin, 1995).

2.1. Instrument

The instrument we used to collect information was an interview comprising a protocol of semi-structured questions categorized according to 10 dimensions: background (3); school history in preschool and primary (7); school history in compulsory secondary education, FP, baccalaureate, and other teachings (5); guidance received and decision to access university (7); work and pre-professional experiences (4); academic experience during university studies (5); causes of dropping out (5); personal and work trajectory after dropping out (2); academic trajectory after dropping out (2); and suggestions and advice (2). In total, the questionnaire comprised 42 questions. For this research, we focused only on the dimension of causes of dropping out, consisting of 5 questions.

2.2. Population and Sample

In total, there were 21 participants (14 men and 7 women) from different Andalusian universities: 1 from the University of Cadiz, 7 from the University of Granada, and 14 from the University of Jaen. The ages of the participants ranged between 18 and 24 years.

Interviews were carried out using two methodologies: casual sampling by volunteers and non-probabilistic snowball sampling. The first strategy allowed us to capture the participation of students who were willing to share their experiences and perceptions about university dropout as former students who had dropped out of university studies. Due to the sensitive nature of the subject, having students who volunteered guaranteed greater depth in responses and an environment of trust for qualitative analysis.

Furthermore, the snowball technique was used to efficiently expand the sample. By starting with an initial group of students known to have experienced academic difficulties or risk of dropping out, these participants were able to recommend others in similar situations. This strategy is especially useful in studies where the target population may be difficult to identify or contact directly.

The interviews conducted for this qualitative study were audio-recorded and subsequently transcribed. An inductive coding process was applied to the transcripts, allowing categories to emerge from the participants’ responses. To identify the thematic content, each response was examined in relation to the corresponding analytical dimension and interview question. This process involved the identification of recurring patterns, concepts, and key terms related to prior experiences, academic guidance, reasons for dropping out, and other relevant themes.

3. Data Analysis

3.1. Biographical Narrative Coding

We adopted a biographical narrative approach to analyze the 21 life stories. The analytical process began with an inductive reading of each interview, identifying critical biographical events related to school trajectories, family support, work experiences, and decisions surrounding university dropout.

A first cycle of coding generated broad thematic categories (prior academic performance, guidance experiences, work trajectories, perceived causes of dropout, post-dropout transitions). Through constant comparison, these themes were refined into a stable analytical framework. Coding disagreements were resolved through investigator triangulation.

These narrative categories served as the conceptual basis for constructing Boolean conditions for the logical minimization analysis.

3.2. Construction of Boolean Conditions and Truth Table

From the refined coding scheme, ten binary conditions (A–J) were established. Each condition was assigned a TRUE value (capital letter) when clearly present in a student’s narrative and FALSE value (lower-case letter) when absent. A detailed coding manual ensured consistency in the assignment of Boolean values. Using these rules, we constructed a 21 × 10 truth table (

Table 1. Truth table for our analysis ¹.

Participants	AANT	BPRI	CSEC	DORI	ELAB	FACA	GCAB	HPER	IFOR	JCON
1	a	b	c	D	E	f	g	H	I	j
2	a	b	c	D	e	F	g	H	i	J
3	a	b	C	D	E	F	g	H	I	j
4	a	b	c	D	e	F	G	h	i	J
5	a	b	c	D	E	F	G	H	I	J
6	a	b	c	d	E	f	g	H	I	j
7	a	b	C	D	E	f	G	H	I	j
8	a	b	c	D	E	F	G	H	I	J
9	a	b	c	D	E	F	g	H	I	j
10	A	B	C	D	E	f	g	H	I	j
11	A	b	C	D	E	f	G	h	I	j
12	a	b	c	D	e	f	g	H	i	j
13	a	b	c	D	e	F	g	H	I	j
14	a	b	c	D	E	F	g	H	i	J
15	a	B	c	D	E	F	g	H	I	j
16	a	b	c	D	E	F	g	h	I	j
17	A	b	C	D	E	f	g	h	i	j
18	a	b	c	D	E	F	g	H	I	j
19	a	B	C	d	E	F	G	H	I	J
20	a	b	c	D	E	f	g	h	I	j
21	A	b	c	D	E	F	g	H	I	j

¹ Legend of the categories analysed as categories and as minimization codes: university background (AANT). Minimization code = A (true) a (false). Primary education success (BPRI). Minimization code = B (true) b (false). Secondary education success (CSEC). Minimization code = C (true) c (false). Good orientation (DORI). Minimization code = D (true) d (false). Positive work experiences (ELAB). Minimization code = E (true) e (false). Positive academic experiences (FACA). Minimization code = F (true) f (false). Recognition of many causes of dropping out (GCAB). Minimization code = G (true) g (false). Satisfactory personal career (HPER). Minimization code = H (true) h (false). Successful educational career (IFOR). Code in minimization = I (true) i (false).

), where rows represent participants and columns represent the presence/absence of each condition.

3.3. Logical Minimization with AQUAD 8

To identify minimal explanatory configurations, we performed Boolean minimization using AQUAD 8. For each condition, AQUAD generated all implicants capable of explaining cases in which the condition was TRUE. Essential implicants—those that cannot be reduced without losing explanatory coverage—were retained. These implicants served as analytical building blocks for identifying distinct student profiles associated with academic, personal, and contextual factors influencing dropout.

3.4. Integration of Narrative and Boolean Analyses

Logical minimization results were interpreted in conjunction with the biographical narrative data. For each essential implicant, the corresponding interviews were re-examined to reconstruct the meaning of the condition combinations in students’ lived experiences.

This integration ensured methodological transparency: Boolean configurations provided structural validity, while narrative interpretation grounded the findings in authentic participant voices.

4. Results

The data used in our research comes from a matrix of codes assigned to different interviews carried out with various participants, where each letter represents a specific category or variable. From the 21 participants studied, the combinations of uppercase letters (condition that is true or true) and lowercase letters (condition that is not true or false) were analyzed to determine the essential and necessary implicants. These codes were used to identify behavioral profiles or trajectories, which were subsequently simplified using the logical minimization technique. In our case, after categorizing, coding, and analyzing the data from the 21 interviews using AQUAD, the following results were obtained.

4.1. Analysis of the Result of Implicants with AQUAD

In this section, we present a summary of each Boolean condition used in the logical minimization analysis (A–J), the essential implications identified with AQUAD, the cases they cover, and a brief narrative interpretation of the corresponding student profiles (

Table 2. Summary of essential implicants.

Condition (Outcome)	Essential Implicant(s)	Cases Covered	Profile Interpretation
AANT = TRUE (university background)	bCDEfgIj (cases 10, 17); bCDEfhIj (cases 11, 17)	10, 11, 17	Students without primary school success (b) but with good secondary performance (C), adequate guidance (D), and generally positive work experiences (E). They tend to recognize a few causes of dropout (g) and show heterogeneous post-dropout trajectories (I/j). Even with relatively favorable trajectories, contextual and personal factors eventually lead to dropout.
BPRI = TRUE (primary school success)	acDEFgHIj (case 15); aCdEFGHIJ (case 19)	15, 19	Students who were successful in primary school (B) but often show mixed trajectories later. In both implicants, the absence of a strong university background (a) coexists with positive work experiences (E), satisfactory post-dropout personal and training paths (H, I), and limited recognition of multiple causes of dropout (g). Dropout is linked to tensions between academic history, work, and expectations.
CSEC = TRUE (secondary school success)	AbDEfgIj (cases 10, 17); AbDEfhIj (cases 11, 17)	10, 11, 17	Students with a university family background (A) and good secondary performance (C), who also report adequate guidance (D) and positive work experiences (E). Despite these favorable conditions, they either have neutral/negative university experiences (f) or recognize few causes of dropout (g). Their narratives reflect the paradox of dropping out after being relatively successful students.
DORI = FALSE (insufficient academic/vocational guidance)	aBCEFGHIJ (case 19); abcEfgHIj (case 6)	6, 19	Two main configurations characterize students who perceive deficient guidance. In one (aBCEFGHIJ), students show academic success and positive trajectories but still report a lack of genuinely helpful orientation. In the other (abcEfgHIj), students accumulate fragile academic trajectories and limited recognition of the causes of dropout. In both cases, guidance is perceived as misaligned or absent at key decision points.
ELAB = FALSE (no positive work experiences)	abcDFgHiJ (case 2); abcDFGhiJ (case 4); abcDFgHIj (case 13); abcDfgHij (case 12)	2, 4, 12, 13	Students with weak academic histories (a/b/c for background and success) and no positive work experiences. The different implicants show slight variations in guidance (D) and recognition of causes (G/H/I/J), but all reflect a lack of stabilizing work anchors. Their narratives emphasize uncertainty, economic vulnerability, and the absence of meaningful work projects that could support persistence at university.
FACA = TRUE (positive academic experiences during university)	abcDFHIJ (cases 5, 8); AbcDEgHIj (case 21); aBCdFHIJ (case 19); aBCdEGHIJ (case 19)	5, 8, 19, 21	These profiles correspond to students who, despite reporting positive academic experiences at university (F), still drop out. Combinations vary in prior academic success (B, C) and work experiences (E), but all share relatively successful performance and satisfactory post-dropout trajectories (H, I). Their stories underline that good grades alone do not guarantee persistence when degrees are misaligned with interests or life circumstances.
GCAB = TRUE (recognition of multiple causes of dropout)	abcDeFhiJ (case 4); abcDEfHIj (case 7); AbCDEfhIj (case 11); abcDEFHIJ (cases 5, 8); aBCdEFHIJ (case 19)	4, 5, 7, 8, 11, 19	Students who explicitly identify several, interconnected reasons for dropout (GCAB = TRUE). Across these implicants, they combine academic difficulties, guidance issues, work pressures, and personal factors. Their narratives reveal a reflective stance: they articulate dropout as the result of complex constellations rather than a single cause, showing high awareness of structural and biographical constraints.
HPER = TRUE (satisfactory personal trajectory after dropout)	abcEfgIj (cases 1, 6); abcDEfIj (cases 1, 7); abcDFgiJ (cases 2, 14); abcDFgIj (cases 9, 13, 18); abDEFgIj (cases 3, 9, 18); acDEFgIj (cases 9, 15, 18); bcDEFgIj (cases 9, 18, 21)	1, 2, 3, 6, 7, 9, 13, 14, 15, 18, 21	Multiple essential implicants converge on students who, after dropping out, manage to reorient their lives in a satisfactory way. Despite differences in prior academic success and work experiences, these configurations share relatively positive post-dropout personal (H) and often training (I) trajectories. These profiles show resilience and the capacity to reconstruct biographical projects beyond the abandoned degree.
IFOR = FALSE (no successful training trajectory after dropout)	abcDFgHJ (cases 2, 14)	2, 14	Students who, after leaving university, do not consolidate a coherent or successful training pathway. In both cases, the absence of a positive educational trajectory (I = FALSE) coexists with limited recognition of multiple causes (g) and non-satisfactory personal outcomes (h/J). Their narratives highlight fragmented, uncertain transitions and difficulties in redefining their educational projects.
JCON = TRUE (good quality advice)	aBCdEFGHI (case 19)	19	A singular profile in which the student reports good quality advice (J = TRUE) together with generally positive academic and work trajectories (B, C, E, F, H, I), but still drops out. This case illustrates the limits of guidance when broader structural or personal constraints (economic pressures, mismatch with interests, family expectations) continue to weigh heavily on students’ decisions.

). The conditions are presented with the result (TRUE/FALSE) that was analytically relevant in our work. For further information, see the Appendix A.

Overall, the table above shows that most conditions are not linked to dropout through a single, simple pattern, but through several essential implicants that partially overlap in the cases they cover. This reflects the configurational nature of university dropout in the sample: different combinations of family background, prior academic success, guidance, work experiences, and post-dropout trajectories can lead to similar outcomes.

Conditions AANT, BPRI, and CSEC highlight the contrast between relatively successful academic careers and later dropout. DORI and ELAB, especially in their FALSE versions, point to the importance of guidance and work as protective or risk factors. FACA and GCAB reveal that even students who do well academically and can clearly articulate the reasons for their decisions may still leave when their projects do not fit with institutional realities. HPER and IFOR shed light on what happens after dropout: some students manage to reconstruct satisfactory personal and educational trajectories, while others remain on fragmented paths. Finally, JCON shows that good advice is a necessary but not sufficient condition to prevent dropout when structural and personal tensions accumulate.

4.2. Quantitative Analysis of the Table of Truth

As a complementary analysis to the minimization and implication analysis, we focused on determining the importance of each of the categories (causes of dropping out of university studies) in our research. To this end, we based ourselves on the truth table (see

Table 3. Frequencies and percentages of the presence of each category with respect to the 21 participants.

Nº Participants Who Highlighted the Categories	AANT	BPRI	CSEC	DORI	ELAB	FACA	GCAB	HPER	IFOR	JCON
	4	1	6	19	17	13	6	15	16	6
% of presence of each category over total participants	19%	4.7%	28.5%	90.47%	80.95%	61.90%	28.5%	71.42%	76.19%	28.5%

). We must clarify that the frequencies and percentages shown below are based on this table, where capital letters indicate presences (1) and lowercase letters indicate absences (0).

As can be seen in the table, we classified the 10 categories into three different colors, according to their presence in each of the analyzed participants. The BPRI category (success in primary education) has the lowest percentage (4.7%). The dimension with the highest percentage, revealing a high presence in the category, is DORI (previous academic guidance), with a total percentage of 90.47%, followed by ELAB (work experiences) with a percentage of 80.95%. Finally, the third category is IFOR (post-dropout training), with a percentage of 76.19%. Then, we observed dimensions such as CSEC (success in secondary education), GCAB (causes of dropping out), and JCON (advice), which have the same percentage (28.5%), followed by AANT (background) (19%), which has a medium presence.

In addition to the frequency and percentage analysis carried out regarding the coverage of the analysis categories, we also implemented a hierarchical cluster analysis using the Ward method as the grouping method and the quadratic Euclidean distance in binary format (0.1) as the dissimilarity measure. With these precedents, we present the main results: the membership table and the dendrogram of the analysis categories.

As shown in Figure 1 and

Table 4. Cluster membership of each category in the implemented cluster analysis.

Category	Conglomerate Belonging
AANT	2
BRPI	1
CSEC	2
DORI	3
ELAB	3
FACA	3
GCAB	2
HPER	3
IFOR	3
JCON	2

, the cluster analysis confirms the results obtained at a descriptive level. This comparison procedure is a strategy based on the well-known methodological triangulation, or more specifically, data analysis triangulation (Rodríguez-Sabiote & Gutiérrez Pérez, 2005). The purpose of this strategy is to consolidate the results and conclusions by examining the degree of convergence or agreement between findings derived from different analytical strategies, referred to as validity as concordance (validity model) (Johnstone, 2004). Some empirical examples of this procedure can be found in the works of Rodríguez Sabiote et al. (2006) and Rodríguez-Sabiote (2019).

Thus, applied to our work, we see how Cluster 1 would correspond to the dimension with the lowest percentage of presence (4.7%), “success in primary education”. In Cluster 2, dimensions 1, 3, 7, and 10, which have a medium presence (28.5% and 19%), are found. In the last cluster, the dimensions with a greater presence are found. These dimensions correspond to “personal trajectory after dropping out”, “academic experience during university studies”, “previous academic guidance received”, “academic trajectory after dropping out”, and “work and personal experiences”.

5. Discussion

This qualitative study identified a range of external factors—such as socioeconomic conditions, academic performance, and academic guidance—that are interrelated in multiple ways, confirming the multidimensional and multicausal nature of university dropout (Fernández Cruz et al., 2024). These findings are consistent with evidence from quantitative research, which similarly highlights the complex interplay of factors influencing student withdrawal from higher education.

In this regard, the study conducted by Miranda Rodríguez and Alarcón Díaz (2021) is particularly noteworthy. It examined the risk factors associated with university dropout among students at the Technological University of Peru during the COVID-19 pandemic and found that the highest proportion of dropouts was attributable to socioeconomic factors (40.7%). Additionally, the authors emphasized the role of student motivation (33.3%), a finding that aligns with the implications of the present study, in which prior educational experiences were observed to positively motivate students and influence their persistence in higher education.

Similarly, Aldowah et al. (2020), focusing on dropout from complementary training programs, particularly MOOCs, identified key contributing factors, including students’ skills, prior experiences, course design, perceived difficulty, time constraints, and levels of social support. These results further reinforce the multifactorial nature of dropout processes across different educational contexts.

Concerning the critical issue of prior academic guidance, the sample analyzed in this study revealed that such guidance was minimal or nearly absent. This finding is consistent with previous research by Lizarte Simón (2017) and Medialdea (2014), both of whom emphasize the essential role of early guidance in fostering university persistence. In this context, Medialdea et al. (2017) described an intervention involving undergraduate and master’s students at the University of Badajoz (Faculty of Education), in which the Tutorial Action Plan (TAP) was restructured to align with the requirements of the European Higher Education Area (EHEA). The results concerned the development of several complementary subplans and notable improvements in institutional guidance processes.

The present study corroborates these findings, showing that a strong academic background (AANT) and successful performance during primary education (BPRI) constitute fundamental pillars for university persistence. Socioeconomic conditions (CSECs) must also be carefully considered, as they emerged as the primary reason for university dropout among the students in this sample.

Finally, academic performance was identified as one of the factors exerting the greatest influence on dropout. Galve-González et al. (2022) reported that academic performance was adversely affected during the COVID-19 pandemic due to the broader external context. Likewise, López-Aguilar et al. (2021), in a study involving 475 university students, found a significant relationship between academic performance and university dropout. However, other studies suggest that academic performance was not directly influenced by online teaching modalities (Sánchez-Cabero et al., 2022; Talsma et al., 2021; Kocsis & Molnár, 2024).

In summary, adequate pedagogical training in emerging educational contexts—particularly among teachers and university faculty—plays a crucial role in supporting students’ academic success and, consequently, their persistence in higher education.

6. Conclusions

This study on university dropouts, carried out using a biographical narrative approach and logical minimization and implication analysis, has provided significant insight into the multifaceted causes that lead students to drop out of their studies. The most relevant conclusions are as follows: First, we highlight the idea of the multicausal nature of university dropout. From this perspective, it is a phenomenon that cannot be attributed to a single factor; it is a problem with a multicausal and multidimensional nature. Analysis revealed that socioeconomic, academic, orientation, and personal factors interact in complex and diverse manners. This interaction underlines the need to understand each case of dropout as a singular event influenced by a combination of circumstances that varies from one student to another.

Second, we highlight the critical importance of academic guidance. This study highlights that prior academic guidance (DORI) is the factor with the greatest presence among the students interviewed, with 90.47% incidence; thus, it was identified as one of the most common causes of dropout. This suggests that reinforcing academic guidance programs from the first years of the degree is essential for the adaptation and retention of students at the university.

Thirdly, we highlight the role of academic and work experiences. Academic experiences during university studies and previous work experiences (ELAB) are also highlighted as crucial factors. In total, 80.95% of participants mentioned positive work experiences as an element that influences their permanence at the university. Students who report having satisfactory experiences, both academically and professionally, tend to show a greater probability of completing their studies. On the contrary, a lack of these experiences increases the probability of dropping out.

Fourth, we highlight the influence of academic performance. Academic performance, especially success in secondary education (CSEC), was another relevant factor. Students with high academic performance are less likely to drop out of university. The results suggests that implementing learning support programs and academic tutoring can be an effective strategy to reduce dropout rates, making education more inclusive. These programs could focus on providing students with tools to improve their study skills and time management techniques, contributing to their academic success.

Fifth, we highlight socioeconomic conditions as a risk factor. Students from environments with lower purchasing power face greater difficulties in continuing their studies, which increases the likelihood of dropping out. Our analysis underlines the importance of scholarship and financial aid programs as key instruments to support students in situations of economic vulnerability. These types of interventions are essential to reduce inequality of opportunity and promote equity in access and permanence in higher education.

Regarding the identification of essential implicants carried out with the AQUAD software (version 8.6), we highlight that this procedure allowed us to identify specific combinations of conditions that explain patterns of abandonment. These implicants show that multiple routes that can lead students to abandon their studies. For example, the profiles of students with unsatisfactory personal trajectories and those who did not receive adequate academic guidance were particularly representative in the analysis. These implicants are essential to understanding why certain cases of abandonment occur and gaining insight into the combinations of factors that must be addressed in interventions.

Finally, quantitative analysis was carried out on the truth table; it showed that academic guidance (DORI), work experiences (ELAB), and the educational path after abandonment (IFOR) are the dimensions with the highest incidence, with percentages of presence of 90.47%, 80.95%, and 76.19%, respectively. Moreover, the category with the lowest incidence was successful in primary education (BPRI), with only 4.7%. Hierarchical cluster analysis confirmed the descriptive results of this study, grouping the categories according to their frequency and impact on dropout cases. This analysis allowed us to identify three categories: those with less presence (for example, BPRI), those with medium presence (such as CSEC and GCAB), and those with high incidence (DORI, ELAB, and IFOR). The methodological triangulation used reinforces the validity of these findings and suggests that intervention programs should target the categories with the highest incidence and relevance.

Implications of This Study and Future Recommendations for Strengthening Inclusive Education

The findings of this study emphasize the necessity to develop institutional policies that acknowledge the multifactorial nature of student dropout. Higher education institutions should implement a comprehensive approach that integrates socioeconomic, academic, and personal dimensions of the student experience. In particular, the results highlight the relevance of academic guidance, suggesting that universities should reinforce academic advising systems from the initial stages of undergraduate studies in order to support students’ transition to the university context and reduce dropout rates.

Additionally, the influence of economic factors remains a key consideration, underscoring the importance of sustaining and expanding financial aid mechanisms, including scholarships and grants targeting students from low-income backgrounds. Such measures may contribute to promoting equity in access to higher education and enhancing student retention. Furthermore, the central role of academic performance in students’ persistence decisions indicates that structured tutoring programs, academic support services, and the systematic development of study skills are essential strategies for improving academic outcomes and mitigating dropout.

Based on these findings, several recommendations for future practice and research are proposed. These include the strengthening of academic guidance systems, increased financial support, and the development of integrated support strategies. Specifically, universities are encouraged to design comprehensive programs that extend beyond academic performance and incorporate emotional and social support components aimed at fostering student resilience. Continued empirical research and the ongoing evaluation and adaptation of tutoring programs are also recommended. Finally, promoting student engagement through structured opportunities for interaction and collaboration between students and faculty may enhance motivation and strengthen cohesion within the academic community.