Academic success is a multidimensional construct that extends beyond grades or cumulative grade point average (GPA) to encompass persistence, progression, learning outcomes, and students’ perceived academic achievement (OECD, 2021; Medina-Manrique et al., 2021; Ahmed et al., 2022; Basu and Chandra, 2021). Contemporary higher education research increasingly recognizes that academic success reflects not only individual student effort but also institutional capacity to provide effective learning environments.

In regulatory contexts such as Peru, academic success acquires additional significance as a core indicator of institutional quality and accountability. Following the implementation of SUNEDU, universities are evaluated based on minimum quality conditions related to teaching, academic support, infrastructure, and governance (SUNEDU, 2017; SUNEDU, 2020; SUNEDU, 2022). As a result, academic success is not merely an individual outcome but also an implicit measure of institutional compliance and effectiveness within the national quality assurance framework.

In this study, academic success is operationalized through a composite measure of self-reported GPA and perceived academic achievement. This approach aligns with prior research indicating that perceived academic performance captures meaningful differences in learning experiences, particularly in contexts where grading standards and assessment practices may vary across institutions.

Student engagement theory provides a foundational framework for understanding academic success, emphasizing the role of students’ behavioral, cognitive, and emotional involvement (Vygotsky, 1978; Garrison et al., 2010). However, engagement does not occur in a vacuum; it is strongly shaped by institutional structures, teaching practices, and regulatory incentives.

In the Peruvian context, SUNEDU’s emphasis on teaching quality, faculty qualifications, and student support services has increased institutional responsibility for fostering engagement-oriented environments. Universities that have successfully adapted to these regulatory demands tend to implement clearer curricular structures, improved feedback mechanisms, and more systematic academic support—all of which are known to enhance student engagement.

Empirical evidence consistently demonstrates that engaged students achieve higher academic performance and persistence (Johnson and Johnson, 2018; Medina-Manrique et al., 2021). Yet, engagement is unevenly distributed across institutions, particularly between public and private universities and across regions. This suggests that engagement functions as an interactional outcome, influenced by both student dispositions and institutional capacity shaped by regulatory compliance and resource availability.

A substantial body of literature identifies academic motivation, self-regulated learning, prior academic preparation, and socioeconomic background as critical individual-level determinants of academic success. These factors are particularly salient in higher education systems characterized by structural inequality.

In Peru, disparities in secondary education quality translate into heterogeneous levels of academic preparedness among university entrants. Students from rural regions and lower-income backgrounds are disproportionately affected, often entering public universities with fewer academic and material resources. Motivation and self-regulated learning thus become essential compensatory mechanisms that enable students to navigate demanding academic environments.

However, the effectiveness of these individual attributes is contingent upon institutional conditions. Highly motivated and self-regulated students may still underperform in contexts where teaching quality is weak or academic support is limited. This reinforces the need to examine individual-level determinants within a multilevel framework that accounts for institutional variation.

Institutional effectiveness research highlights teaching quality, academic support services, and resource availability as central determinants of academic success. In Peru, these institutional dimensions are directly linked to accreditation and licensing requirements enforced by SUNEDU.

Teaching quality is particularly salient, as regulatory reforms have incentivized universities to professionalize faculty roles, improve pedagogical training, and adopt student-centered teaching approaches. Institutions that meet or exceed accreditation standards often demonstrate stronger instructional clarity, more consistent feedback practices, and higher levels of faculty engagement—all factors associated with improved academic outcomes.

Academic support services, including tutoring, mentoring, and academic advising, have also gained prominence under the quality assurance regime. These services are especially important in public universities, where students are more likely to face gaps in prior preparation and external constraints related to work and family responsibilities.

Institutional resources, including digital infrastructure, libraries, and learning spaces, remain unevenly distributed across the system. While regulatory oversight has improved baseline conditions, significant disparities persist, particularly between metropolitan and regional universities. These disparities underscore the importance of examining institutional resources not as isolated inputs but as enabling conditions whose effectiveness depends on pedagogical integration (CONCYTEC, 2023; Laura-De La Cruz et al., 2022; Lavado et al., 2014; MTPE, 2022).

The distinction between public and private universities in Peru reflects differences in governance, funding mechanisms, and institutional autonomy. Public universities operate under stricter budgetary constraints and administrative regulations, while private institutions generally enjoy greater flexibility in resource allocation and managerial decision-making.

However, recent regulatory reforms have narrowed this gap by imposing common quality standards across institutional types. As a result, differences in academic success may increasingly reflect variation in how institutions respond to regulatory demands rather than ownership status per se. Universities—public or private—that strategically invest in teaching quality, academic support, and student-centered practices are more likely to achieve higher academic success.

This perspective challenges simplistic public–private comparisons and highlights the need to disentangle compositional effects from contextual and organizational influences.

Despite extensive research on academic success, three gaps remain particularly relevant. First, few studies explicitly link theoretical frameworks to national regulatory contexts, especially in Latin America. Second, institutional-level determinants are often treated as background variables rather than as active moderators of student-level processes. Third, the majority of empirical evidence relies on single-level models that obscure cross-level dynamics.

By adopting a multilevel analytical approach, this study addresses these gaps and responds directly to the Peruvian context shaped by SUNEDU and ongoing quality assurance reforms. The model allows for the examination of how institutional factors—particularly those related to accreditation and teaching quality—not only influence average academic success but also moderate the effects of student motivation and self-regulated learning.

Academic success in higher education emerges from dynamic interactions between students and the institutional environments in which they are embedded (Vygotsky, 1978; Garrison et al., 2010). In line with student engagement theory, institutional effectiveness models, and regulatory governance perspectives, this study conceptualizes academic success as a multilevel outcome shaped by determinants operating at both the individual (Level 1) and institutional (Level 2) levels.

In the Peruvian higher education system, institutional environments are strongly influenced by national quality assurance and licensing mechanisms administered by SUNEDU. These regulatory processes shape universities’ incentives to invest in teaching quality, academic support services, and learning infrastructure, thereby affecting both average levels of academic success and the strength of individual-level relationships.

Accordingly, the proposed model integrates individual-level student characteristics with institutional-level conditions while explicitly accounting for cross-level interactions. This approach allows for the examination of how institutional responses to regulatory demands condition the effectiveness of student motivation and learning strategies.

Institutional type (public vs. private) is incorporated as a contextual variable at Level 2. Although public and private universities differ in governance and funding mechanisms, SUNEDU has imposed a common set of minimum quality conditions across the system.

As a result, differences in academic success are expected to reflect variation in institutional practices and responsiveness to regulatory requirements rather than ownership status alone.

Beyond direct effects, institutional conditions may moderate the strength of individual-level relationships. Teaching quality, in particular, is expected to amplify the benefits of positive student characteristics by providing structured, supportive, and engaging learning environments.

From a regulatory perspective, accreditation-driven improvements in teaching quality may enhance the effectiveness of student motivation and self-regulated learning.

In summary, the revised research model explicitly integrates regulatory context, institutional effectiveness, and student engagement perspectives. By conceptualizing teaching quality and academic support as both direct determinants and moderators, the model captures the multilevel mechanisms through which academic success is produced in Peruvian universities operating under a consolidated quality assurance regime.

This study adopts a quantitative, explanatory research design aimed at identifying the determinants of academic success at both the student and institutional levels (OECD, 2021) within the Peruvian higher education system. Given the hierarchical structure of the data—students nested within universities—a multilevel modeling approach is employed to appropriately estimate effects operating across levels of analysis.

Hierarchical Linear Modeling (HLM) is particularly suitable in this context for three reasons. First, it accounts for institutional heterogeneity arising from differences in governance, accreditation status, and regional resource distribution. Second, it allows for the examination of cross-level interactions, which are theoretically justified in systems where institutional quality conditions the effectiveness of student-level attributes. Third, it aligns with prior higher education research examining student outcomes under regulatory and quality assurance frameworks.

The target population consists of undergraduate students enrolled in licensed Peruvian universities, including both public and private institutions operating under the SUNEDU regulatory framework.

A two-stage sampling strategy was employed. In the first stage, ten universities were selected to reflect institutional diversity in terms of ownership (public vs. private), geographic location, and organizational characteristics. The sample included universities from metropolitan and regional contexts, capturing variation in institutional resources and regulatory adaptation.

In the second stage, undergraduate students were randomly selected within each institution. Only students in their second year or above were included to ensure sufficient exposure to institutional teaching practices, academic support services, and learning infrastructure.

The final analytical sample comprised 1,020 undergraduate students nested within ten universities, exceeding recommended thresholds for multilevel analysis and allowing for robust estimation of institutional effects.

Data were collected through a structured, self-administered questionnaire distributed during the academic year (Medina-Manrique et al., 2021). Participation was voluntary, anonymity was guaranteed, and informed consent was obtained from all respondents.

The survey instrument was administered in Spanish to ensure clarity and cultural relevance. A rigorous translation–back-translation procedure was applied for reporting purposes in English. Prior to full deployment, a pilot study involving 50 students was conducted to assess item clarity, internal consistency, and completion time. Minor wording adjustments were made based on pilot feedback.

All constructs were measured using validated multi-item scales adapted from established research in higher education and educational psychology (Garrison et al., 2010; OECD, 2021). Responses were recorded on five-point Likert scales (1 = strongly disagree, 5 = strongly agree), unless otherwise indicated.

Importantly, the use of perceptual measures is theoretically and methodologically justified in this study, as students’ academic behaviors, engagement, and evaluations of institutional quality are shaped by their lived experiences within the university environment.

Preparation for employability was treated as a perceptual construct reflecting students’ subjective assessments of how well their academic experiences equip them with transferable skills relevant to the labor market. This operationalization is justified for three reasons.

First, employability outcomes often materialize after graduation, making objective measurement difficult in undergraduate samples. Second, students’ perceptions of employability influence motivation, engagement, and satisfaction during their studies, regardless of eventual labor market outcomes. Third, in the Peruvian context, alignment between academic curricula and labor market demands varies substantially across institutions and fields of study, reinforcing the relevance of subjective evaluations.

The modest association observed between employability perceptions and academic success suggests that students conceptually distinguish academic performance from labor market readiness. Rather than indicating a measurement limitation, this disparity reflects a meaningful differentiation in how students evaluate academic versus professional outcomes—an interpretation consistent with prior research.

The non-significant cross-level interaction involving digital infrastructure warrants careful interpretation. This result may reflect three complementary explanations.

First, baseline digital access has improved across Peruvian universities following regulatory reforms and pandemic-driven investments, reducing between-institution variance. Second, digital infrastructure alone may be insufficient to enhance academic success unless accompanied by pedagogically effective use. Third, students may perceive digital tools as supporting administrative and informational functions rather than directly influencing learning quality.

These findings suggest that infrastructure should be conceptualized as an enabling condition rather than a direct moderator of learning processes, reinforcing the primacy of teaching quality and academic support.

The analysis followed a stepwise multilevel modeling procedure, beginning with an unconditional model to estimate the intra-class correlation coefficient (ICC). Student-level predictors were subsequently introduced, followed by institutional-level variables and cross-level interaction terms.

Continuous predictors were centered following established recommendations to facilitate interpretation. All models were estimated using HLM 8.0 software.

Internal consistency reliability exceeded the recommended threshold (Cronbach’s α > 0.70). Construct validity was supported through prior scale validation and confirmatory factor analysis at the student level.

The study adhered to ethical guidelines for educational research. Participation was voluntary, anonymity was ensured, and ethical approval was obtained from the relevant institutional committee (de la Puente et al., 2022; Lima Garcés and Martínez Marín, 2023; Liu et al., 2023).

The analysis began with the estimation of an unconditional (null) model to assess the extent to which academic success varies across universities (Raudenbush and Bryk, 2002) operating under the Peruvian quality assurance system.

The results revealed statistically significant between-institution variance in academic success. The intra-class correlation coefficient (ICC) indicated that approximately 21% of the total variance in academic success was attributable to differences between universities, while the remaining 79% was explained by differences among students within institutions.

This finding is substantively meaningful in a system governed by common minimum quality conditions enforced by SUNEDU. Despite regulatory standardization, institutional contexts continue to play a decisive role in shaping academic outcomes, justifying the use of a multilevel analytical approach and underscoring the relevance of institutional practices beyond formal compliance.

The Level 1 model examined the influence of individual student characteristics on academic success (Ahmed et al., 2022).

Academic Motivation had a positive and statistically significant effect on academic success (β = 0.31, p < 0.001). Students with higher motivation demonstrated greater academic performance, supporting H1.

Collectively, these results confirm that academic success is strongly shaped by student-level attributes while remaining sensitive to contextual conditions.

The Level 2 model examined institutional characteristics associated with variation in average academic success across universities.

Teaching quality demonstrated the strongest institutional-level effect on academic success (γ = 0.36, p < 0.001), supporting H5. Universities characterized by higher instructional clarity, feedback quality, and pedagogical effectiveness achieved higher average student performance.

These findings reinforce the importance of institutional effectiveness over formal institutional classification.

Cross-level interaction models were estimated to examine whether institutional conditions moderate the effects of student-level predictors.

Teaching Quality × Academic Motivation showed a statistically significant interaction (γ = 0.12, p < 0.05), supporting H9. The positive association between motivation and academic success was stronger in universities characterized by higher teaching quality.

These results indicate that institutional teaching practices amplify the effectiveness of positive student characteristics, providing empirical support for student engagement theory within a regulated higher education system.

The interaction involving digital infrastructure was not statistically significant. This finding suggests that digital resources alone do not systematically strengthen the relationship between student characteristics and academic success.

In the Peruvian context, this result may reflect reduced variability in baseline digital access following recent investments and regulatory pressures. More importantly, it highlights that digital infrastructure functions as an enabling condition rather than as a direct moderator of learning processes, unless pedagogically embedded within teaching practices.

Overall, the results demonstrate that academic success in Peruvian universities is shaped by both student-level and institutional-level determinants. While individual motivation, self-regulated learning, and prior preparation play central roles, institutional teaching quality and academic support services exert substantial direct and moderating effects.

The findings underscore that even under a common regulatory framework, universities differ meaningfully in how effectively they translate quality standards into student learning outcomes.

This study set out to examine the determinants of academic success in Peruvian universities by integrating student-level and institutional-level factors within a multilevel analytical framework. The findings confirm that academic success is a contextualized outcome, shaped by individual student attributes and by institutional conditions operating under a consolidated quality assurance regime.

At the student level, academic motivation and self-regulated learning emerged as the strongest predictors of academic success. These results reaffirm the centrality of student engagement processes while underscoring their dependence on institutional environments. In Peru, where students enter higher education with heterogeneous academic preparation, these individual attributes serve as critical mechanisms for persistence and adaptation.

Prior academic preparation retained a significant effect, reflecting enduring inequalities in secondary education quality across regions and socioeconomic groups. This finding aligns with national evidence indicating that disparities in pre-university schooling continue to influence academic trajectories even after access to higher education has expanded.

The more modest association observed for socioeconomic status suggests that while structural disadvantage matters, its effects can be partially mitigated through institutional interventions—particularly those related to teaching quality and academic support services.

The institutional-level results provide clear guidance regarding which improvements in teaching quality and learning experiences are most impactful.

Teaching quality demonstrated the largest direct effect on academic success (SUNEDU, 2022; OECD, 2021) and a significant moderating role. Based on the measurement model and item-level composition, the most influential dimensions of teaching quality were:

Instructional clarity and course organization, enabling students to understand academic expectations and assessment criteria;

These dimensions were most strongly associated with academic success, particularly when interacting with student motivation and self-regulated learning. This indicates that teaching practices that structure learning and provide meaningful feedback amplify the effectiveness of positive student attributes.

Academic support services—notably tutoring and academic advising—also exerted a substantial effect. Their impact was especially pronounced among students with weaker prior preparation, suggesting a compensatory function that is highly relevant in public and regional universities.

By contrast, institutional resources, including digital infrastructure, exhibited smaller effect sizes. This finding suggests that resources alone do not drive academic success unless embedded within pedagogically coherent teaching and support systems.

Although academic success was the primary outcome, the results allow for the identification of dimensions of students’ academic experience most closely associated with perceived quality:

Satisfaction with clarity of instruction and assessment criteria

These dimensions align closely with SUNEDU’s quality conditions related to teaching, student services, and academic monitoring, reinforcing the policy relevance of the findings.

Based directly on the empirical results, the following recommendations are proposed:

Targeted faculty development programs: Universities should prioritize faculty development initiatives focused on instructional clarity, formative feedback, and active learning methodologies, rather than generic training programs.

At the policy level, the findings suggest that higher education regulation in Peru should continue shifting from compliance-oriented evaluation toward outcome-oriented quality assurance.

Specifically, SUNEDU and related agencies could:

Emphasize teaching quality and academic support effectiveness as central accreditation criteria;

Such an approach would strengthen the alignment between regulatory oversight and student-centered outcomes.

From a theoretical standpoint, this study reinforces student engagement theory by demonstrating that engagement-related attributes operate most effectively within supportive institutional environments. It also extends institutional effectiveness models by showing that institutional factors function not only as direct determinants but also as moderators of individual-level processes.

This study examined the determinants of academic success in Peruvian universities through a multilevel analytical framework that integrates student-level characteristics and institutional-level conditions within a regulated higher education system.

The findings provide robust evidence that academic success is not solely the result of individual student effort but is strongly shaped by institutional environments (OECD, 2021; SUNEDU, 2022) operating under SUNEDU’s quality assurance framework. At the student level, academic motivation and self-regulated learning emerged as the most influential predictors of academic success, underscoring the importance of engagement-related processes in higher education achievement. Prior academic preparation also played a significant role, reflecting persistent inequalities in pre-university education across regions and socioeconomic groups.

At the institutional level, teaching quality demonstrated the strongest direct effect on academic success, followed by academic support services and institutional resources. Importantly, Differences between public and private universities were substantially reduced after controlling for institutional characteristics (Ruíz-González and Briceño-Cotrina, 2020; SUNEDU, 2022), indicating that organizational practices and learning environments—rather than ownership status—primarily explain performance disparities.

The identification of significant cross-level interactions further strengthens the contribution of this study. High-quality teaching environments amplified the positive effects of student motivation and self-regulated learning, providing empirical support for student engagement theory and institutional effectiveness models within a Latin American regulatory context.

The modest association observed between perceived employability preparation and academic success warrants explicit clarification. Rather than reflecting a limitation of measurement, this finding suggests that students conceptually distinguish between academic achievement and labor market readiness.

In the Peruvian context, employability is shaped not only by academic performance but also by external factors such as labor market conditions, institutional–industry linkages, and opportunities for internships or professional practice. As a result, students may evaluate employability as a parallel but distinct outcome of higher education. Treating employability as a perceptual construct therefore captures a meaningful dimension of students’ educational experience that complements, rather than duplicates, academic success indicators.

Despite its contributions, this study has several limitations. First, academic success was measured using self-reported indicators, which may introduce reporting bias, although prior research suggests acceptable validity for such measures. Second, the cross-sectional design limits causal inference and does not capture changes in academic success over time. Third, while the institutional sample reflected diversity across public and private universities and regions, the number of institutions was relatively limited, which may constrain generalizability.

Additionally, the study focused exclusively on undergraduate students and did not incorporate faculty or administrative perspectives, which could further illuminate institutional processes influencing academic success.

Future research should adopt longitudinal designs to examine how academic success evolves throughout students’ university trajectories and into graduation and employment. Integrating objective academic records, retention data, and post-graduation outcomes would strengthen empirical validity.

Expanding the institutional sample and incorporating cross-national comparisons within Latin America would allow for deeper examination of how different regulatory frameworks shape student success. Finally, mixed-methods approaches could complement multilevel models by exploring how students and faculty experience teaching quality, academic support, and accreditation processes in practice.

Overall, this study advances higher education research by providing empirical evidence from a system undergoing regulatory consolidation, demonstrating the value of multilevel approaches for understanding academic success, and offering actionable insights for institutional leaders and policymakers.

By shifting attention from institutional ownership to institutional practices, the findings support a student-centered vision of quality assurance aligned with the objectives of SUNEDU and broader higher education reform efforts in Peru.