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Start date: 2023Subject: search.filters.subject.Perceived learning

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    The relationship between perceived learning, academic performance and academic engagement in virtual education for university students
    (Asian Online Journal Publishing Group, 2024-01-01)
    This study aimed to determine whether the three dimensions of academic engagement (cognitive, emotional and behavioral) were positively associated with perceived learning and academic performance. The participants were 301 university students from Lima. Structural equation models were used to test the proposed theoretical relationship between the variables. The results indicated that the model showed satisfactory fit indices (CFI = 0.956, TLI = 0.949, RMSEA = 0.043, SRMR = 0.062). Perceived learning was found to be predicted by cognitive engagement (β = 0.447, p < 0.01) and emotional engagement (β = 0.230, p < 0.05). However, there was no statistically significant relationship between behavioral engagement and perceived learning (β = 0.035, p = 0.840). On the other hand, academic performance was predicted by behavioral engagement (β = 0.393, p < 0.05) but not by cognitive (β = -0.164, p = 0.301) or emotional (β = 0.001, p = 0.991) engagement. The study highlights the importance of fostering academic engagement in university students to enhance both their academic performance and perceived learning.
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    Relationship Between Technology Acceptance Model, Self-Regulation Strategies, and Academic Self-Efficacy with Academic Performance and Perceived Learning Among College Students During Remote Education
    (Frontiers Media SA, 2023-01-01)
    Introduction: The aim of this study was to examine the relationship between the technology acceptance model, self-regulation strategies, and academic self-efficacy with academic performance and perceived learning among college students during remote education. Methods: The participants were 301 university students from Lima. Structural equation model was used to test the proposed theoretical relationships between the variables. On the one hand, the study sought to explore the relationship between academic self-efficacy and self-regulation strategies with the technology acceptance model. On the other hand, it sought to determine whether the three dimensions of the technology acceptance model are positively related to perceived learning and academic performance. Results: The results suggest the importance of improving psychological variables such as self-efficacy and self-regulation strategies to improve the acceptance of technology, which would also improve the academic performance and perceived learning of students in a virtual environment. Discussion: The discussion highlights the significance of self-efficacy and metacognitive strategies in influencing technology perception and attitudes, ultimately impacting perceived learning and academic performance in virtual education.
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    Assessment of challenges in virtual instruction quality and its relationship to learning outcomes and student satisfaction
    (Springer Science+Business Media, 2026-12-01)
    This study examines whether the Framework to Assess Challenges in Virtual Education (FACVE), C1 dimension (challenges to virtual education quality), covering teaching quality, interaction, and assessment challenges, is associated with students' perceived learning outcomes and satisfaction. Self-reported survey data were collected from graduate business students at one of Peru's top universities between September 17, 2021, and February 6, 2022, following an initial emergency transition and during a stable term in which courses were delivered through institutionally standardized online modalities. Using PLS-SEM, FACVE-C1 was modeled as a higher-order construct, and its relationships with students' perceived learning outcomes (LO) and student satisfaction (SS) were estimated. Virtual instruction quality challenges were negatively related to perceived learning (β = −0.470; R² = 0.221) and to student satisfaction (β = −0.116), while perceived learning was strongly related to student satisfaction (β = 0.821; R² = 0.777). Among the FACVE-C1 components, interaction challenges contributed most strongly, indicating that strengthening student–instructor and student–student interaction structures is a high-leverage target in online graduate business education. The findings provide empirical support for FACVE-C1 as a parsimonious diagnostic lens for identifying quality-related constraints that shape students' experience and perceived learning in planned virtual instruction contexts.
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