INTRODUCTION

In the wake of the COVID-19 pandemic, online learning emerged as the primary mode of delivering education worldwide. Educational institutions were forced to shift to digital environments to ensure academic continuity during prolonged lockdowns. This global transformation positioned virtual education as a key solution not only during emergencies but also in rethinking long-term strategies for accessibility and flexibility in higher education.

Regionally, in Latin America-and specifically in Peru-this shift revealed new demands and opportunities. Post-pandemic, many professionals sought to further their education without interrupting their work and family responsibilities. The need for flexibility prompted universities to continue offering postgraduate programs through online modalities, making education more accessible and time-efficient. In response, higher education institutions invested in technological infrastructure and pedagogical redesign to sustain virtual education as a viable long-term alternative.

At the micro level, the use of Information and Communication Technologies (ICTs) enabled new teaching and learning methods. Among these, B-learning (blended learning) and E-learning (entirely online learning) became prominent. ^{Gómez & Macedo (2011}) highlighted the importance of virtual programs in Peruvian tertiary education, stressing their impact in a globalized academic context. From the learner's perspective, ^{Martín (2012}) emphasized that student satisfaction depends largely on the methodology used and the level of instructional planning.

Online learning encompasses a wide range of digital activities that support synchronous and asynchronous interactions (^{Hashim & Yusup, 2015}). These activities include blogs, wikis, virtual labs, simulations, and case studies. ^{Moncayo et al. (2018}); ^{Lara-Carrillo & Freire-Aillón, (2022}) distinguished between synchronous activities-those conducted in real-time, such as live chats, video sessions, and collaborative problem-solving-and asynchronous activities, which allow students to engage with the content at their own pace, supporting reflection, interaction, and contextualized learning.

According to ^{Singh & Thurman (2019}), online learning consists of educational experiences facilitated through internet-connected devices, supporting both synchronous and asynchronous communication. ^{Yuhanna et al. (2020}) noted the ease with which learners can access and retrieve global information, while also connecting with experts in various fields. However, they also acknowledged challenges such as restricted access to copyrighted materials, the prevalence of plagiarism, and the dependency on stable internet and technical infrastructure. Similarly, ^{Hiranrithikorn (2019}) identified benefits including reduced commuting costs, greater schedule flexibility, and the ability to learn at one’s own pace, while also noting limitations such as reduced social interaction and the unsuitability of some subjects for online delivery.

The choice of platform is also significant. ^{Pitrah et al. (2022}) described Google Meet as a widely used video conferencing tool that enables high-definition meetings with up to 250 participants. Its integration with Google Calendar and email facilitates scheduling, and it automatically stores recorded sessions in Google Drive. ^{Singh et al. (2020}) emphasized its user-friendly interface and built-in security features, which enhance both accessibility and trust in the platform.

Student satisfaction has become a critical metric for assessing educational quality. Elliot & Healy (2001) defined satisfaction as a temporary attitude shaped by the evaluation of academic experiences. ^{Manrique & Sánchez (2019}) expanded this notion, describing it as a comprehensive perception of the university's service-including teaching quality, infrastructure, and student support. ^{Jimenez et al. (2011}) described student satisfaction as a key indicator in quality assurance, while Jimenez et al. (2011) highlighted that students, as primary recipients of educational services, are uniquely positioned to assess their effectiveness. These perspectives underscore the relevance of students’ feedback for continuous improvement. Similarly, Elliot & Shin (2002) conceptualized satisfaction as the student’s subjective evaluation of the overall educational experience.

In the specific case of a postgraduate school at a public university in Lima, Peru, fully online master’s degree programs were implemented between 2020 and 2022 as a response to the pandemic. These programs were designed to maintain quality standards established by SUNEDU (Superintendencia Nacional de Educación Superior Universitaria) and relied heavily on ICTs and platforms such as Google Meet to ensure uninterrupted academic delivery.

Thus, this study aims to examine the relationship between online learning and student satisfaction in a graduate program at a public university in Lima. This analysis seeks to inform institutional strategies for improving the effectiveness and sustainability of virtual postgraduate education in Peru.

METHOD

This study utilized a quantitative research methodology, which aimed to gather and analyze numerical data through valid and reliable instruments. The data collected were processed using statistical and mathematical techniques to draw meaningful conclusions and interpretations. The study sought to investigate the correlation between online learning and student satisfaction without manipulating either variable. Therefore, it adopted a non-experimental research design with a correlational approach, as the goal was to explore the relationship between two variables without introducing any type of intervention.

A hypothetical-deductive method guided the research, beginning with theoretical assumptions from which hypotheses were formulated and empirically tested. The population consisted of 168 second-semester students enrolled in various master’s degree programs at the postgraduate school of a public university in Lima, Peru. Based on the finite population formula, a sample of 122 students was selected for the study. The distribution of students by program is detailed in the following table 1.

Table 1. Population of second-semester students of the master's degree programs. 

Source: Information provided by teachers about the number of students enrolled in their classes.

In this study, a survey technique was used, and two online measurement scales were employed to gather quantitative data from the selected sample. The instrument was initially validated through expert judgment, and its reliability was confirmed via a pilot test. A fraction of the sample participated in the pilot test, allowing for the evaluation of the instrument’s reliability before it was applied to the full sample.

Data analysis was performed using SPSS software, version 25, which facilitated the calculation of descriptive statistics such as measures of central tendency and dispersion, as well as inferential statistics for hypothesis testing. To assess the internal consistency of the measurement instruments, Cronbach’s Alpha was calculated. This index assessed the degree of homogeneity among the items and accounted for the polytomous classification of the scales.

Table 2. Instrument Reliability. 

The reliability coefficients obtained-0.885 for the online learning scale and 0.622 for the student satisfaction scale-indicated that the instruments presented acceptable levels of internal consistency. Thus, the instruments were considered valid and reliable for application among second-semester postgraduate students in the context studied.

RESULTS AND DISCUSSION

The results revealed significant patterns regarding the relationship between online learning and students' satisfaction levels. As shown in Table 3, when online learning reached a very high level, 41.0% of students reported being very satisfied, and 5.7% indicated they were satisfied. This represented the most favorable combination between the two variables. When online learning reached a high level, the majority of students (41.8%) reported being satisfied, while 3.3% showed an acceptable level of satisfaction, and 2.5% reported being very satisfied. This suggested that even with a slightly lower level of online learning compared to the “very high” category, students generally maintained a positive perception. In contrast, with medium levels of online learning, only 0.8% of students expressed being satisfied, and 4.1% reported an acceptable level of satisfaction. Notably, none expressed being very satisfied or dissatisfied in this category. Finally, when online learning was evaluated as low, 0.8% of students reported being dissatisfied, which was the only occurrence of dissatisfaction across all levels. No students indicated any positive satisfaction under this condition.

These results highlighted that higher levels of online learning were generally associated with greater student satisfaction, with the peak satisfaction corresponding to the “very high” level of online learning.

Table 3. Distribution of comparative levels between online learning and student satisfaction. 

The inferential analysis provided evidence of a statistically significant relationship between online learning and student satisfaction. As shown in Table 4, which presented the results of the general hypothesis test, a significance level of 0.000 was found. Since this value was lower than 0.01, the null hypothesis was rejected at a 99.99% confidence level. This result indicated that the correlation between the two variables was statistically significant. Furthermore, the Spearman’s Rho coefficient was calculated at 0.859, which indicated a very high positive correlation between online learning and student satisfaction. This implied that as the quality or effectiveness of online learning increased, the level of satisfaction among students also rose proportionally. These findings supported the hypothesis that there was a strong and direct relationship between the two variables under study.

Table 4. Correlation and significance between online learning and student satisfaction 

Note: The correlation is significant at the 0.01 level (2-tailed).

Source: Own elaboration based on SPSS output.

The results corresponding to the specific hypothesis test 1 revealed a statistically significant relationship between digital educational resources and student satisfaction. As indicated in Table 5, a significance level of 0.000 was found, which was lower than 0.01. Consequently, the null hypothesis was rejected at a 99.99% confidence level, confirming that the correlation between the variables was significant.

In addition, the Spearman’s Rho coefficient was 0.751, which represented a high positive correlation. This indicated that the greater the use and quality of digital educational resources, the higher the level of satisfaction reported by students. The data supported the interpretation that digital educational resources were directly associated with students’ satisfaction, reinforcing the role these tools played in enhancing the online learning experience.

Table 5. Correlation and significance between digital educational resources and student satisfaction. 

Note: The correlation is significant at the 0.01 level (2-tailed).

Source: Own elaboration based on SPSS output.

The results presented in the specific hypothesis test 2 demonstrated a statistically significant association between online learning activities and student satisfaction. As shown in Table 6, the significance level was 0.000, which was lower than 0.01; therefore, the null hypothesis was rejected with a confidence level of 99.99%.

Furthermore, the Spearman’s Rho coefficient was 0.813, indicating a very high positive correlation. This result suggested that as the level and quality of online learning activities increased, the level of student satisfaction also tended to rise. The findings underscored the relevance of interactive and structured learning activities in promoting a positive student experience in online education environments.

Table 6. Correlation and significance between online learning activities and student satisfaction. 

Note: The correlation is significant at the 0.01 level (2-tailed).

Source: Own elaboration based on SPSS output.

DISCUSSION

The main objective of this research was to analyze the relationship between online learning and student satisfaction among postgraduate students at a public university in Lima. Based on the results obtained from hypothesis testing, it was concluded that there is a statistically significant relationship between online learning and student satisfaction. The findings revealed that as the quality of online learning improved, so did students' satisfaction levels. The high positive correlation (Spearman’s correlation coefficient = 0.859) confirmed this association.

This result aligns with the findings of ^{Figueroa (2020}), who reported a strong positive correlation (r = 0.861) between online education and student satisfaction, highlighting the role of flexibility and instructional design. Similarly, González (2021) found an even stronger relationship (Spearman’s Rho = 0.997) in his study on virtual education, emphasizing the relevance of student-centered approaches and digital support systems. Moreover, the present findings are supported by the research of ^{Yekefallah et al. (2021}), who concluded that multiple factors within e-learning environments-such as interaction, feedback, and platform quality-significantly influenced students’ satisfaction during the COVID-19 pandemic. These prior studies reinforce the conclusion that effective online learning environments contribute substantially to the improvement of student satisfaction.

The first specific objective aimed to determine the relationship between digital educational resources and student satisfaction. The hypothesis testing showed a high positive correlation (Spearman’s Rho = 0.751), indicating that better access to and integration of digital resources were associated with higher levels of student satisfaction. These results are consistent with González (2021), who reported a perfect correlation between the use of digital educational resources and student satisfaction, emphasizing the importance of digital content variety and relevance. In line with this, Martínez (2021) defined digital educational resources as tools used by teachers to develop curriculum-based competencies. Therefore, when students perceive these tools as effectively embedded in the learning process, their satisfaction tends to increase accordingly.

The second specific objective was to determine the relationship between online learning activities and student satisfaction. The findings demonstrated a very high positive correlation (Spearman’s Rho = 0.813), suggesting that well-designed online activities have a significant impact on students' learning experiences. This result is consistent with the study conducted by ^{Castillo et al. (2022}), which found that both synchronous and asynchronous online learning activities-when aligned with learning goals-fostered high levels of satisfaction. The authors highlighted the value of meaningful tasks, collaborative work, and timely feedback as critical components for promoting student engagement and satisfaction in virtual settings.

Overall, the findings of this study are consistent with previous research and confirm that the design of online learning environments, the integration of digital resources, and the implementation of interactive activities are key factors in improving student satisfaction at the postgraduate level.

CONCLUSIONS

The research achieved its objective of determining the relationship between online learning and student satisfaction in a postgraduate school at a public university in Lima. It allowed for the identification of key aspects within the online learning experience that were positively associated with how students evaluated their academic process.

The study provided a foundation for understanding how online learning environments influenced student satisfaction, particularly by highlighting the role of digital educational resources and learning activities. These findings served as a starting point for strengthening institutional strategies aimed at improving the virtual academic experience.

Additionally, the research contributed to recognizing the importance of providing continuous training to educators in the effective use of digital tools, with the purpose of enhancing the quality of instruction. The scope of the study encouraged future lines of inquiry that could include new variables or explore diverse educational contexts.

CONFLICT OF INTERESTS. The authors declare that there is no conflict of interest regarding the publication of this scientific article.