Tocto Cano, Esteban

Preferred name

Tocto Cano, Esteban

Main Affiliation

estocan@upeu.edu.pe

ORCID

0000-0003-4729-4192

Scopus Author ID

57219482984

Mostrando 1 - 10 de 22

Some of the metrics are blocked by your
consent settings
Optimización y cuantificación de procesos utilizando BPM - Process Optimization and Quantification using BPM
(2013-04-29)
Se muestra una propuesta para la Optimización y la Cuantificación de Procesos usando herramientas Business Process Management (BPM) en el ámbito universitario. Se optimizó y automatizó el Proceso de Gestión de Prácticas Pre Profesionales (PGPP) de la Universidad Peruana Unión, filial Tarapoto (UPeU FT). El desarrollo del modelo se realizó en función a entrevistas en el nivel operativo como a nivel de dirección, la optimización del proceso se efectuó utilizando una herramienta para la construcción de modelos BPM. La herramienta cuenta con diferentes utilitarios los cuales permitieron redefinir los procesos, subprocesos, tareas, entregables, roles, responsabilidades. En términos de la medición de efectividad del proceso propuesto se implementó en la solución una serie de Key Performance Indicators (KPI’s) que permitieron la medición de la efectividad de la solución puesta. El enfoque cuantitativo, así la definición y trabajo con los indicadores fueron definidos en función a los diversos principios expuestos en la metodología de BPM.
Some of the metrics are blocked by your
consent settings
Implantación de Soluciones de Mejora de Procesos Basadas en BPM e ITIL V3 en un Contexto Universitario: Caso Universidad Peruana Unión
(2011-06-01)
Some of the metrics are blocked by your
consent settings
Editorial
(2017-09-25)
Editorial
Some of the metrics are blocked by your
consent settings
A predictive approach to teaching quality transition and improvement in higher education
(2026-06-08)
Rosa Linda Mamani Morales
;
Zulma Quispe Mamani
;
Ronny Ivan Gonzales Medina
;
Edgar Tito Susanibar Ramirez
;
Introduction University teaching quality in Latin America is increasingly influenced by faculty professional development and digital teaching competencies. However, the mechanisms through which these factors jointly shape students' perceptions of instructional quality remain insufficiently understood. This study examined the direct and mediated relationships among continuing teacher training (FCD), teacher digital competencies (CDD), and perceived university teaching quality (CEU) in a Peruvian higher education context. Methods A non-experimental, cross-sectional correlational design was conducted with independent samples of 384 university teachers and 384 undergraduate students from public and private universities in the Puno region of Peru. Data were collected using validated psychometric instruments and analyzed through confirmatory factor analysis and structural equation modeling with bootstrapped standard errors (5,000 replications). Results FCD showed a significant positive association with CDD ( β = .42, p &lt; .001) and directly predicted CEU ( β = .32, p &lt; .001). CDD also positively predicted CEU ( β = .30, p &lt; .001) and partially mediated the relationship between FCD and CEU (indirect effect: β = .127, 95% CI [.080, .180]). Discussion Findings indicate that digital teaching competencies represent a key mechanism through which continuing teacher training contributes to improved instructional quality. The study provides robust evidence from an underrepresented Latin American context and highlights the importance of integrating digital competency development into faculty development policies to strengthen teaching quality in higher education.
Some of the metrics are blocked by your
consent settings
Validation of a questionnaire assessing artificial intelligence use in secondary mathematics education in Peru
(2026-01-23)
Edgar Tito Susanibar Ramirez
;
Abel Tapia Díaz
;
Rudy Ricardo Torres Gallegos
;
Elia Clorinda Andrade Girón
;
The aim of this study was to adapt and validate a questionnaire to measure the use of artificial intelligence (AI) applications in mathematics teaching by Peruvian secondary school teachers. A previously validated instrument from Jordan was translated, culturally adapted, and psychometrically validated in the Peruvian context. The study followed a quantitative, cross-sectional, and psychometric design with intentional non-probabilistic sampling. The sample included 150 teachers for exploratory analysis and 266 for confirmatory analysis. The validation process involved expert judgment, reliability analysis, exploratory factor analysis, and confirmatory factor analysis. The instrument showed excellent internal consistency (Cronbach's α = 0.96) and sampling adequacy (KMO = 0.95). EFA identified a three-factor structure explaining 66% of the variance, later reorganized into two dimensions: didactic use of AI and strategic and formative use of AI. CFA supported a second-order hierarchical model, showing strong factor loadings. Despite moderate fit indices, the questionnaire demonstrates conceptual validity and practical relevance. It is a reliable tool for assessing AI-related teaching practices and supporting teacher training and educational innovation in Peru and Latin America, with potential to inform inclusive and equitable approaches to artificial intelligence integration in secondary mathematics education.
Some of the metrics are blocked by your
consent settings
Modelo de Control Operacional basado en el Modelo de Control Estratégico Balanced Scorecard - Operational Control Model based on the model of strategic control Balanced Scorecard
(2014-06-03)
;
Héctor José Ñopo Aguilar
La presente investigación tiene como propósito identificar similitud de estructura entre un Modelo de Gestión Estratégica Balanced Scoreard (BSC) y el Modelo de Gestión por Procesos; de tal forma que se pueda diseñar una herramienta de control, basada en la estructura identificada, que pueda servir tanto para el Control Estratégico como para el Control Operacional. Se han definido dos modelos, representando dos formas complementarias de analizar la gestión empresarial, se presenta una estructura similar para controlar la estrategia y controlar los procesos, ambos temas muy diferentes.
Some of the metrics are blocked by your
consent settings
A Holistic Maturity Model for Quality Assessment and Innovation in Peruvian Universities
(2025-01-24)
;
Sandro Paz Collado
;
This study proposes a holistic maturity model to evaluate and optimize the performance of Peruvian universities. It addresses key dimensions such as favorable governance, university talent (including students, faculty, and administrators), substantial resources, and results. It is based on the Design Science Research methodology and the Mettler framework. On the other hand, the Delphi method was selected for its ability to consolidate expert opinion. Aiken’s V coefficient was used to determine content validity, evaluating criteria such as clarity, relevance, and coherence, to ensure the reliability of the instrument. This model defines concrete practices for each maturity level, facilitating the progressive implementation of improvements in different university contexts. It contributes to Education 4.0 through the IT strategic alignment practices of the enabling governance dimension, promoting the implementation of personalized teaching methods and hybrid learning models. Regarding the Society 5.0 approach, the model prioritizes social impact and environmental sustainability through university social responsibility, ensuring that universities contribute to human and technological development. Finally, this proposal will support decision making in university management and educational policies in Peru and in international contexts.
Some of the metrics are blocked by your
consent settings
Implementing the SDGs in Higher Education: A Systematic Review
(2024-09-05)
Jackson Edgardo Perez Carpio
;
;
;
Sustainable development is essential for the well-being of society, as it enabling future generations to meet their own needs. Therefore, it is crucial to work on the 17 Sustainable Development Goals (SDGs) to eradicate poverty, protect the planet and ensure peace and prosperity. Universities must integrate actions related to the SDGs and communicate their results in research that promotes sustainability and sustainable development. A methodology including terms related to academic institutions, such as “universi*”, “college”, “the 17 goals”, “17 SDGs” and “sustainable development goals” was used to conduct a systematic literature search using the Boolean operators AND and OR to perform a systematic literature search. The resulting search string was: ((universi*) OR (college)) AND ((“the 17 goals”) OR (“17 SDGs”)) AND (“sustainable development goals”). The goal was to conduct a systematic literature review, that included articles on the implementation of the SDGs from 2015 to 2023, categorized them into three groups. Articles whose titles were not related to the scope of this research were excluded. It was concluded that research on SDG implementation in universities is limited, which can be attributed to the pandemic, lack of demand, and insufficient implementation of international policies regarding SDG compliance. Received: 16 March 2024 / Accepted: 15 August 2024 / Published: 05 September 2024
Some of the metrics are blocked by your
consent settings
AI-driven optimization in cloud computing: a systematic review of cost, resource management, and security
(2026-04-30)
Ronaldy Solano Ito López
;
Romel Gutierrez Oscata
;
Ángel Rosendo Condori-Coaquira
;
Ronny Ivan Gonzales Medina
;
Cloud computing environments face persistent structural challenges in cost control, dynamic resource allocation, and security risk management, which traditional infrastructure approaches fail to address adequately. This systematic literature review aimed to synthesize empirical evidence on the application of artificial intelligence (AI) and machine learning (ML) models for cost optimisation, resource management, and security enhancement in cloud computing environments. Following the PRISMA 2020 guidelines and the Kitchenham-Charters methodology, a structured search was conducted across IEEE Xplore, Web of Science, ScienceDirect, and the ACM Digital Library, covering the period 2020-2025. From an initial pool of 216 records, 18 primary studies were selected after applying the PICOC framework, predefined inclusion and exclusion criteria, and a dual-reviewer quality assessment process yielding substantial inter-rater agreement (Cohen's κ = 0.86). The synthesized evidence demonstrates that predictive provisioning systems and intelligent load-balancing mechanisms reduce operational costs by up to 85%, metaheuristic algorithms such as the Whale Optimization Algorithm and Particle Swarm Optimization improve energy efficiency by 30%-40% and increase resource utilization by up to 80%, and deep learning-based intrusion detection systems achieve accuracy levels exceeding 92%. These findings confirm that AI constitutes a structural mechanism for strengthening economic efficiency, operational resilience, and the sustainability of cloud infrastructures. However, heterogeneity in simulation environments, limited validation in production-scale deployments, and insufficient coverage of virtual machine migration dynamics represent critical gaps requiring standardized benchmarking frameworks and empirical validation in hybrid and multicloud architectures. A quantitative synthesis (Table 1) reveals that metaheuristic algorithms achieve 30%-40% cost and energy efficiency improvements, while ensemble deep learning approaches attain >97% security threat detection rates.
Some of the metrics are blocked by your
consent settings
Data-Driven Socioeconomic Segmentation for Residential Energy Planning: A Machine Learning Approach
(2026-05-05)
Lucas Camaz Ferreira
;
Felipe Leite Coelho da Silva
;
Josiane da Silva Cordeiro
;
;
The Brazilian residential sector is one of the largest consumers of electricity, making residential energy consumption a critical component of national energy systems. Electricity consumption patterns in this sector are closely associated with household appliance ownership and, consequently, with socioeconomic status. For residential energy planning to operate more equitably and efficiently, it is essential that consumption analyses be aligned with the socioeconomic conditions of the population. This study examines the role of socioeconomic variables in residential energy planning through the application of supervised machine learning algorithms within a data-driven socioeconomic segmentation framework. Decision trees, support vector machines, and artificial neural networks were implemented using data from the Brazilian residential sector to evaluate model performance and to determine the extent to which household socioeconomic status can be inferred from variables related to appliance ownership and electricity consumption characteristics. The results showed that household appliances, such as refrigerators, microwave ovens, and air conditioners, exhibited substantial predictive power in relation to socioeconomic status, thus improving the interpretation and understanding of residential energy consumption from a multidimensional perspective. The neural network model achieved the highest predictive performance. By enabling data-driven socioeconomic segmentation based on observable electricity consumption patterns, this approach provides relevant insights for residential energy planning and contributes to more targeted and equitable energy policy design, supporting Sustainable Development Goal 7 on Affordable and Clean Energy and Sustainable Development Goal 10 on Reduced Inequalities.

Tocto Cano, Esteban

Filtros

Ajustes

Ordenar por

resultados por página