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Item type:Publication, Academic performance of micro-entrepreneurs in business training programs: evidence from the application of an i4.0 educational system during the COVID-19 pandemic(Belgrade University, 2021-01-01)There is limited information on the academic performance obtained by teaching through an i4.0 educational system. Therefore, this article aims to close the gap by presenting the existing literature and the quantitative results obtained from the evaluations and surveys made to micro-entrepreneurs with little knowledge of digital technologies, and in many cases with different levels of education, who have been trained during the COVID-19 pandemic, between August and December 2020. The business training program used an i4.0 educational system based on IoT, the cloud, social networks and Web services. The results showed that the participants achieved a satisfactory academic performance and met the objectives of the training program in business-related topics. Likewise, the results established that the academic performance of the student in a business training program through an i4.0 system is not directly related to the student's previous educational level. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Análisis predictivo del desempeño académico en niños peruanos: el rol del control intencional y el agrado hacia la escuela(Universidad de Almeria, 2022-09-01)Introduction. In Peru, there are few studies that include individual differences between stu-dents to explain their academic performance at the beginning of formal schooling. For that reason, the aim of the present study was to predict the perceived academic performance of a group of students based on the following variables: effortful control (EC) and school liking (SL). Method. A sample of 423 students, between 5 and 8 years old (M = 6.29, SD = 0.89), was gathered from public schools in socioeconomically disadvantaged areas in Lima. A total of 45 teachers gave information about their perceptions regarding the study variables in their re-spective students. Results. The hierarchical linear regression analysis results show that both effortful control and school liking are positive and significant predictors of perceived academic performance, even after controlling for students’ cognitive skills and their mothers’ educational level. Discussion and Conclusion. Possible explanations for these findings and their relevance in the Peruvian context are discussed - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Generative AI solutions for faculty and students: A review of literature and roadmap for future research(Informing Science Institute, 2024-01-01)Aim/Purpose This paper aims to address the gap in comprehensive, real-world applications of Generative Artificial Intelligence (GenAI) in education, particularly in higher education settings. Despite the evident potential of GenAI in transforming educational practices, there is a lack of consolidated knowledge about its practical effectiveness and real-world impact. Background This study addresses this gap by conducting a systematic literature review to collate and analyze real-life instances of GenAI applications in higher education, thus providing a nuanced understanding of its practical implementations and measurable outcomes. Methodology The paper utilizes a systematic literature review methodology, adopting the PRISMA approach complemented by a thematic analysis procedure to ensure a comprehensive and in-depth evaluation of the literature. It synthesizes information from relevant articles from 2022 to 2024, focusing on the applications of GenAI in higher education. This analysis covers various aspects, including research settings, analysis scales, data types, collection tools, and analytical methods. Contribution The paper contributes to the academic community by offering a comprehensive review of GenAI applications in education, highlighting the current precision level of these tools, and providing strategic recommendations for their effective use in academia. Furthermore, the research defines seven specific cases where Gen AI can be utilized as a reference for educational institutions in their adoption strategies. Findings Key findings include the versatility of GenAI in generating teaching materials, enhancing skill development, supporting student tasks, academic performance evaluation, feedback delivery, and its role as a virtual assistant and in research support. Recommendations for Practitioners Practitioners are advised to explore the integration of GenAI for diverse educational purposes, from content creation to student assessment, while being cognizant of its limitations and ethical considerations. Recommendations for Researchers Future research should focus on addressing the gaps identified, such as the implications of GenAI in research roles, its application in various disciplines, and the exploration of newly developed AI tools tailored to specific educational needs. Impact on Society The findings of this paper highlight the potential of GenAI in revolutionizing the educational sector, offering personalized learning experiences, and significantly influencing teaching methodologies and student engagement, but it also reveals significant deficiencies of Generative AI, known as hallucinations, which can impact the expected results. Future Research Subsequent research should explore the evolving capabilities of GenAI models, their impact on various academic disciplines, and the development of pedagogical strategies to optimize their use in education. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Use of Nearpod and Blum Modeling to Strengthen the Academic Performance of University Students in Mathematics(Richtmann Publishing Ltd, 2023-09-01)The method used by educators at the higher level is not usually the most appropriate, which usually affects the learning process of students and, in turn, their academic performance. The main of this study is to demonstrate that Blum's modeling and the use of the Nearpod strengthen academic performance in the mathematics course in university students. Method. applied typology, quantifiable approach, explanatory level and pre-experimental design, likewise, the sample consisted of 30 university students, to whom a questionnaire created and validated by the researcher was applied. RESULTS. they indicate that Blum's modeling and the use of the Nearpod strengthen academic performance (p=0.000<0.05). Discussion. Using the Nearpod as a tool to improve academic performance is effective, because it presents characteristics that motivate the student to learn. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Pedagogical Strategies and Academic Performance in Theses Seminars: A Study in an Undergraduate Management Program in Peru(Babeș-Bolyai University, 2023-03-20)Purpose Writing a thesis is a difficult endeavor for undergraduate students, especially in management careers, due to the highly practical approach of the discipline. Students often find difficult to understand and apply research methods in concrete research projects, so a proper set of teaching-learning strategies is critical. This study aimed to examine the effect of these strategies on the academic performance of students in two research seminars in an undergraduate management program in Peru. Design/methodology/approach The research adopted a mixed approach. The quantitative component included a survey of 249 students in both seminars, while the qualitative one involved only some of the students using three focus groups. The corresponding data analysis included stepwise linear regression models and content analysis. Findings The study found that a clear course structure, adequate research methods literature, good advisor–student communication and goal planning and achievement were the key determinants of the students' final grades. Originality/value This research fills a gap in previous studies on the subject by including a broader set of strategies and by statistically estimating the strategies' effects on academic performance. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Use of technological resources in the era of artificial intelligence and its effect on academic performance(Frontiers Media, 2026-07-08)The advancement of digital technologies and generative artificial intelligence (GenAI) has transformed learning environments in higher education; while tools such as ChatGPT offer personalized experiences and immediate feedback, their relationship with academic performance remains insufficiently understood in university contexts. This study analyzed the use of technological resources in the AI era, considering frequency of use, perceived usefulness, familiarity, and type of use, as well as their contribution to university students' academic performance and identifying technological and contextual factors associated with it. A data-mining approach was employed, applying 10 supervised regression algorithms using 10-fold cross-validation, complemented by interpretability analysis using SHAP values to identify relevant predictors. Regularized regression models (Elastic Net; Lasso) showed the best relative predictive performance. The SHAP analysis revealed that the field of study (? 0.41) and study cycle (? 0.34) are the dominant predictors, while the use of AI for academic writing (? 0.10) emerged as the technological variable with the greatest positive contribution to academic GPA within the model. Academic performance was mainly associated with structural factors related to students' educational trajectories, while AI tools showed a complementary role. The predictive contribution of technology appears to depend more on pedagogical integration than on frequency of use alone. These findings suggest the need to integrate digital strategies into higher education, implementing them in a differentiated manner by discipline and academic level, and promoting the guided and purposeful use of AI and technological resources in learning processes.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Influence of sleepiness, quantity, and quality of sleep on academic performance in adolescent students of a Colombian public institution(Medknow, 2026-06-01)BACKGROUND: This study aimed to examine the relationship between sleep-related variables and academic performance in Spanish Language and Mathematics among adolescents from a Colombian public school. MATERIALS AND METHODS: A descriptive, cross-sectional study was conducted, which included 185 students (12–18 years, both sexes) from a Colombian public school. Two questionnaires were applied to obtain the data: the Pittsburg Sleep Quality Index (PSQI) and the Epworth Sleepiness Scale (ESS). Academic performance was measured using final and subject-specific grades in Spanish and Mathematics. Bivariate and multivariate linear regression analyses were performed to explore associations between sleep variables and academic outcomes, adjusting for age and sex. RESULTS: Bivariate analyses revealed significant negative associations between sleepiness and key academic performance indicators, including final average grade, Spanish language average grade, and annual average grade. By contrast, no significant associations were observed between overall sleep quality and academic performance. When controlling for confounding variables in multivariate regression analyses including sleepiness, age, and sex showed that the previously observed associations between sleepiness and academic performance were attenuated and no longer statistically significant. These findings suggest that the effect of sleepiness on academic outcomes may be influenced by age, indicating increased somnolence among older adolescents. CONCLUSION: Daytime sleepiness, rather than subjective sleep quality, appears to negatively influence academic performance in adolescents, particularly in language-related outcomes. Interventions to reduce sleepiness may enhance educational achievement. Longitudinal studies using objective sleep measures are recommended to clarify causal pathways linking sleep, cognition, and learning. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Data Mining to Identify University Student Dropout Factors(Multidisciplinary Digital Publishing Institute (MDPI), 2025-11-01)University dropout poses academic, social, and economic challenges that call for effective prevention strategies. The objective was to identify determining factors of student dropout through educational data mining and machine learning models. A survey was administered to 527 undergraduate students, and the data were processed with classification algorithms (Adaboost, Gradient Boosting, Extra Trees, Random Forest, Decision Tree, and XGBoost), complemented with interpretation techniques such as SHAP and sensitivity analysis. The results revealed that, in addition to prior academic performance (GPA), psychological support emerged as the most influential predictor across all models, followed by institutional and socioeconomic variables, including academic program, age, and parental job stability. Integrating psychological, institutional, and family factors into predictive systems enhances model accuracy and provides practical evidence to inform educational policies, strengthen student support programs, and design early interventions to promote retention in higher education.2