Doctorado
Permanent URI for this communityhttps://hdl.handle.net/11285/551013
Colección de Tesis presentadas por alumnos para obtener un Doctorado del Tecnológico de Monterrey.
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- Hacia visiones pluriversales de las tecnologías digitales: las experiencias de la Red de comunicadoras y comunicadores, Boca de Polen, A.C.(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024) Manjarrez Bastidas, Laura Elizabeth; Ricaurte, Paola; dnbsrp; Baca, Carlos; Escuela de Humanidades y Educación; Campus Ciudad de México; Lugo, NohemíLa presente investigación se propone ofrecer una perspectiva emergente para el análisis de las tecnologías digitales, focalizándose en los conceptos de pluriversalidad y comunalidad y a partir en las vivencias de la Red de Comunicadores Boca de Polen, una entidad civil que ha respaldado a emisoras comunitarias e indígenas en Chiapas, México, durante más de dos décadas. A partir de los preceptos de la investigación colaborativa y mediante un proceso de sistematización, este estudio ahonda en las vivencias tanto de la red como de las radios acompañadas, desvelando una profunda interacción de prácticas sociotécnicas enraizadas en experiencias comunales. Al enfocarse en una experiencia específica de apropiación y resistencia, y al buscar cambiar el lugar de enunciación, se exploran nuevas facetas de preocupaciones y oportunidades habilitadas por las tecnologías digitales. Así, el conocimiento experiencial obtenido de Boca de Polen emerge como una representación palpable de una visión pluriversal de la tecnología, crucial para fomentar reflexiones críticas inclusivas sobre las múltiples implicaciones de las tecnologías digitales.
- Emotional intelligence, local brands, and brand coolness: Unveiling the dynamics of consumer responsibility in sustainable consumption(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-09-09) Agredano González, Carlos; Toldos Romero, María de la Paz; emimmayorquin; Alvarado Herrera, Alejandro; Escuela de Graduados en Administración y Dirección de Empresas; Sede EGADE Estado de México; Rialp-Criado, JosepThe study of consumer behavior is a fascinating yet complex subject in both marketing literature and human psychology. Recently, there has been a heightened interest in sustainable consumption and consumption patterns have affected serious environmental issues, causing us to question whether making these conscious and responsible decisions will impact how we perceive brands and influence future consumer purchases. To respond to this question and based on the signaling theory (Erdem and Swait, 1989) and the theory of planned behavior (Ajzen, 1991), three scientific studies were elaborated to empirically prove the relationships between variables with a methodical basis that can conduct responsible behavior while making stronger brands. The manuscript presents three studies that aim to identify the antecedent variables that explain consumer decision-making in the relationship between consumer responsibility for sustainable consumption and local or global brands, specifically, the objective is to understand the factors that influence this behavior. Firstly, the role of emotional intelligence and flourishing in predicting consumer responsibility for sustainable consumption was examined, and the relationship between these variables and the mediating effect of personal norms, and frugality. Secondly, the study examines how consumer responsibility for sustainable consumption can affect the likelihood of purchasing local brands, the mediation role of local brand attitude and local brand as a social signaling value, and how these relationships are moderated through materialism and ethnocentrism. Lastly, it was presented the impact of the perceived local or global brand on how cool those brands are. In the findings for studies one and two, PLS structural equation modeling was applied to a representative sample of 430 respondents from an online survey in Mexico. This study demonstrated that emotional intelligence has a positive impact on individuals' flourishing, and personal norms and frugal behaviors play a mediating role that positively influences consumer responsibility for sustainable consumption. The second study explored the consumer responsibility for sustainable consumption and its positive direct and indirect effect on local brand purchase likelihood through the mediation of local brand attitude, and local brand as a social signaling value and the moderation role of materialism and ethnocentrism in those direct and indirect effects. In the last study it was applied PLS structural equation modeling and a multigroup analysis, to evaluate the impact of perceived local and global brands on brand coolness and how this perception is affected by their attitude toward a global or local consumer culture in retailers’ brands. A multinational survey was used to collect information from the United States, Australia, and Mexico. Businesses, brand managers, and policymakers should prioritize these factors in marketing strategies to enhance local brand visibility and influence consumer behavior positively and responsibly. The initial investigation offered insights into the antecedent factors driving responsible and sustainable consumption. The findings in second study, enhance the understanding of how to develop effective local brand marketing strategies from the perspective of construal level theory, and this is particularly relevant when targeting consumers who have a strong sense of personal responsibility and prioritize sustainability, providing managerial implications on how to promote consuming locally, thereby promoting responsible consumption. These results suggest that adopting a glocal strategy can benefit retailers seeking international growth and enhance their overall coolness factor.
- Methodology to improve compact extended range ev-powertrain module(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023-06-07) Puma Benavides, David Sebastian; Calderón Nájera, Juan de Dios; puemcuervo, emipsanchez; Galluzzi Aguilera, Renato; Bustamante Bello, Martin Rogelio; Loyola Morales, Félix; School of Engineering and Sciences; Campus Monterrey; Izquierdo Reyes, JavierThe constant growth of the vehicle fleet means that more and more emissions are being emitted into the environment, with the transportation sector contributing around 21% of CO2 in data updated to 2023. Reducing emissions and carbon footprint, leaving aside the dependence on fossil fuels, has been the premise for developing vehicles with new technologies and developing clean energy for their use. As a result, the sale of internal combustion vehicles reached its highest peak in 2017, and from there, the sale of electric and hybrid vehicles has grown yearly. However, combustion, electric and hybrid vehicles have yet to achieve optimal efficiency; therefore, generating optimizations in their powertrain is viable as research topics, as well as for the extension of the range in electric vehicles, which at the moment is a factor that makes their purchase unattractive. Therefore, this thesis aims to review and evaluate technologies that can function as range extenders for electric vehicles, considering their efficiency, low pollution levels, and compatibility for integration into electric vehicle platforms. To facilitate this evaluation, an algorithm incorporating equations representing characteristic curves of mechanical or electrical components will be developed for Extended Range Electric Vehicle EREV. This algorithm will provide valuable insights into the behavior and energy analysis of potential range extender (ICE-Alternator/Generator). Furthermore, the optimization of the entire powertrain system will be considered to ensure all components operate at peak efficiency. These objectives constitute the core of this dissertation. Through powertrain improvement, specifically by adjusting the differential gear ratio from 4.3 to 3.54, significant improvements in vehicle performance can be achieved. Energy savings during standardized driving cycles such as NEDC, WLTC-2, and WLTC-3 can reach up to 10%. Additionally, integrating an auxiliary power unit (APU) into the vehicle architecture can substantially enhance the vehicle's range. By employing an ICE-Alternator configuration with a maximum power of 12.8 kW, the vehicle's travel distance can be extended by up to 170%. Alternatively, an ICE-Generator configuration with a maximum power of 3.2 kW can increase travel distance by up to 39%. Implementing an effective control strategy that optimizes fuel consumption based on the battery's state of charge further enhances APU utilization, resulting in efficiency gains of up to 3.5%. The proposed methodology for developing extended-range electric vehicles, along with the validated algorithm through practical implementations and testing, enables comprehensive energy analyses. This approach provides a more accurate understanding of the performance of vehicle platforms incorporating range extenders.
- DC-DC high-order converters for renewable applications(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-05-07) Garza Arias, Enrique; Valdez Resendiz, Jesús Elias; Rosas Caro, Julio César; Escobar Valderrama, Gerardo; Castañeda Cuevas, Herman; Escuela de Ingeniería y Ciencias; Campus Monterrey; Guillén Aparicio, DanielThis dissertation develops new theory and techniques for DC-DC power converters interfacing renewable energies, specifically for converters classified as high-order based on their mathematical model, and thus generally more complex and difficult to analyze. The thesis encompasses the development of a new circuit topology, a control design focused on high-order dynamic systems, and the study of conversion systems between renewables and the utility grid. The dynamics of high-order converters present challenges for the stability and synthesis of controllers. By studying the nonlinear model of fourth-order non-minimum phase converters through zero dynamics, it has been established that some of these can attain stability under direct voltage at high regulation, in contrast to second-order converters. Controllers have been designed to take advantage of these results in order to remove the current sensing stage while still achieving high bandwidth voltage regulation. Research has also been conducted into creating novel DC converters with a lower component size than similar circuits. From this, an improved super-boost converter has been developed that requires lower energy storage on its capacitors and inductors than the traditional boost and fourth-order boost converters, for a given specification of input current and output voltage ripples. Additionally, an inverter system for a fuel cell stack has been developed using double-dual converters in cascade to increase the voltage gain and power handling of the system. Results from tests at different power and voltage levels show that the proposed system also achieves reduced switch voltage stresses and reduced output current ripples. Simulation and experimental results were obtained in order to validate the theoretical analysis. Mathematical models, steady state values and stability conditions were calculated, and prototype designs were developed to corroborate the performance of the converters, control and efficiency.
- Commercial delivery policies: inventory management models with power demand pattern(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-06-29) Khan, Md. Al-Amin; Cárdenas Barrón, Leopoldo Eduardo; emiggomez, emipsanchez; Loera Hernández, Imelda de Jesús; Smith, Neale R.; Treviño Garza, Gerardo; Bourguet Díaz, Rafael Ernesto; School of Engineering and Sciences; Campus MonterreyIn the fast-paced, ever-changing environment of contemporary business, inventory management stands as a vital, ongoing endeavor. The discipline of decision-making in inventory management assumes a central role in this dynamic environment that is marked by continuous change and intense competition. Navigating the complexities of decision-making poses challenges, particularly in accurately assessing the multifaceted aspects of decision-making processes amid varying circumstances, including demand fluctuations, different types of discounts, and contractual agreements. At the same time, the increasing concern among consumers regarding the environmental footprint of their purchases, coupled with government-mandated regulations, complicates the decision-making process for businesses. In this milieu, sustainable inventory management practices have emerged as a pertinent research area, prompting heightened scrutiny of the impacts of emission guidelines on inventory practices, not only aimed at addressing broader societal concerns but also at ensuring the financial sustainability of businesses. This thesis adopts a specialized demand structure known as the power demand pattern (PDP) to depict fluctuations in demand over the storage period of a company, providing a robust framework for understanding customer demand dynamics across various products. The company maintains its inventory by acquiring items through quantity discounts in exchange for a quantity-sensitive prepayment as part of a contractual arrangement. This thesis introduces a novel concept by considering the installment frequency for fulfilling prepayment obligations as a decision variable for the company, incorporating a transaction fee for each installment. Furthermore, theoretical formulas are developed under different sorts of demand structures, incorporating the influences of selling price and storage time, to assess the profitability of inventory management processes under a combined link-to-order prepayment and quantity discount scheme. A significant advancement by integrating sustainability considerations into both inventory management and pricing strategies within the framework of the PDP is accomplished in this thesis. Through systematic identification and comparison of sustainable inventory management practices under varying emission guidelines, this study provides valuable insights aimed at optimizing profits within the PDP. Therefore, the insights derived from this study offer organizations practical techniques to navigate the complex regulatory environment effectively and achieve sustainable financial performance. Moreover, intensive and comprehensive in-depth sustainable inventory practices under the PDP are established specifically for growing items (GIs). This thesis investigates the impact of weight loss resulting from bleeding and non-consumable components on optimal pricing and inventory strategies for a farm, delving into previously unexplored areas within the literature on GIs. A comparative analysis is conducted on the operations of a livestock farm, operating within several environmental regulations. Consequently, the comprehensive methodology improves sustainable inventory management techniques and offers practical strategies to mitigate environmental impacts and enhance economic feasibility in livestock production.
- Extracting the embedded knowledge in class visualizations from artificial neural networks for applications in dataset and model compression and combinatorial optimization(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-04-25) Abreu Pederzini, Jose Ricardo; Terashima Marín, Hugo; emiggomez, emipsanchez; González Mendoza, Miguel; Juárez Jiménez, Julio Antonio; Rosales Pérez, Alejandro; Bendre, Nihar; School of Engineering and Sciences; Campus Monterrey; Ortiz Bayliss, José CarlosArtificial neural networks are efficient learning algorithms, considered universal approxima-tors for solving numerous real-world problems in areas like computer vision, language processing, or reinforcement learning. To approximate any given function, neural networks train a large number of parameters that can go up to the millions or even billions in some cases. The large number of parameters and hidden layers in neural networks makes them hard to interpret, which is why they are often referred to as black boxes. In the quest to make artificial neural networks interpretable in the field of computer vision, feature visualization stands outas one of the most developed and promising research directions. While feature visualizations are a useful tool to gain insights about the underlying function learned by a neural network, they are still considered simply as visual aids that require human interpretation. In this doctoral work, we propose that feature visualizations—class visualizations in particular—are analogous to mental imagery in humans and contain the knowledge that the model extracted from the training data. Therefore, when correctly generated, class visualiza-tions can be considered as a conceptual compression of the data used to train the underlying model, resembling the experience of perceiving the actual training samples just as mental imagery resembles the real experience of perceiving the actual physical event. We present results showing that class visualizations can be considered a conceptual compression of the training data used to train the underlying model and present a methodology that enables the use of class visualizations as training data. To achieve this goal, we show that class visualizations can be used as training data to develop new models from scratch, achieving, in some cases, the same accuracy as the underlying model. Additionally, we explore the nature of class visualizations through different experiments to gain insights on what exactly class visualizations represent and what knowledge is embedded in them. To do so, we com- pare class visualizations to the class average image from the training data and demonstrate how the other classes that a model is trained on affect the shape and the knowledge embedded in a class visualization. We show that class visualizations are equivalent to visualizing the weight matrices of the output neurons in shallow network architectures and demonstrate that class visualizations can be used as pretrained convolutional filters. We experimentally show the potential of class visualizations for extreme model compression purposes. Finally, we present a novel methodology to enable the use of Artificial Neural Networks along with class visualizations for the solution of combinatorial optimization problems, such as the 2D Bin Packing Problem, by training an Artificial Neural Network to score potential solutions to a 2D BPP and then using that network to generate an ’optimal’ (local optima) solution to the problem by extracting a class visualization from the network via backpropagation to the network’s input. Even though we show the use of class visualizations as a tool to solve the bin packing problem, it is important to note that class visualizations have the potential to be used in the same way to solve other types of combinatorial optimization problems. For other types of combinatorial optimization problems, we just need to design a neural network that is capable of scoring solutions to the particular combinatorial optimization problem and extract class visualizations from such a network to generate a candidate solution to the problem.
- A digital twin model with knowledge graph-driven dense captioning(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-06) Wajid, Mohammad Saif; Ortiz Bayliss, José Carlos; 212577; Therasima Marin, Hugo; emiggomez, emipsanchez; Ortiz Bayliss, José Carlos; Carrasco Jiménez, José Carlos; Ceballos Cancino, Héctor Gibrán; School of Engineering and Sciences; Campus Monterrey; Najafirad, PeymanThis dissertation is submitted to the Graduate Programs in the School of Engineering and Sciences in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science. This document explores how a digital twin model for the TEC District is developed and how the knowledge graph can be used for dense captioning of security events happening in the TEC District digital twin model. This also describes and analyses factors responsible for security breaches in the city using the concept of neutrosophy. The thesis proposes novel techniques for advancing dense captioning with the integration of knowledge graphs and neutrosophy, harnessing the capabilities of digital twin technology. Digital twins, as virtual replicas of physical entities or systems, offer a comprehensive framework for understanding and simulating real-world scenarios. They have emerged as a powerful tool in various industries, including manufacturing, healthcare, and urban planning. These models rely on detailed simulations of cities, including video data, to analyze and describe various security events using dense captioning. However, the accuracy and relevance of these simulations depend heavily on the quality of the captions generated for the video content. Captioning of videos based on temporal information presents a challenging task, involving the limitation of distracting information over time and space, which is crucial but poses difficulties. Additionally, ensuring robustness to false positives during captioning and addressing storage issues are significant challenges obtained from the literature. Also gathering information from knowledge graphs and providing context is another key task because of the presence of indeterminacy in data. This poses a challenge for defining aggression subjectively and automatically describing events while optimizing classifiers for faster caption generation and selecting optimal parameters. A widely used technique for dense video captioning is a knowledge graph that provides a structured representation of knowledge, organizing and connecting information extracted from videos. By incorporating knowledge graphs into the digital twin model, the relevance and context of the captions are significantly enhanced. However, knowledge graphs may fail to capture indeterminate factors that can dramatically impact situation analysis. Indeterminate factors, such as unpredictable human behavior or environmental conditions, are crucial in determining event sequences in digital twin models. In this dissertation, we aim to create a digital twin model for the TEC District for effective dense captioning of events with the knowledge graph model district area. In the proposed model, knowledge graphs play a crucial role in enhancing the context and relevance of captions by organizing and connecting information extracted from videos. It provides a structured representation of knowledge, enabling a more comprehensive understanding of video content. We have also utilized neutrosophy to address indeterminate and uncertain events, thereby enhancing the efficiency of dense captioning. This work is carried out in three phases; the first phase identifies various traits of character taken from datasets and literature, leading to different events among the masses using Neutrosophic Cognitive Maps (NCMs). This is done to identify the significance of various determinate and indeterminate factors while analyzing the security events. This task was earlier performed using Fuzzy Cognitive Maps (FCMs) in some research domains other than dense video captioning where indeterminate or uncertain factors were not considered. Therefore, we provide a brief comparison between NCMs and FCMs and show how effective NCMs are when considering the uncertainty of concepts while carrying out tests for describing events. In the second phase, a knowledge graph model for dense captioning is developed. As captioning is based on a knowledge graph, the time consumption for generating the video captions was considerably reduced. Also, we used the Bidirectional Long Short-Term Memory (BiLSTM) classifier to analyze the flow of the information provided by the captions, and the efficiency is further enhanced by using the Recurrent Neural Network (RNN). The enabling of the Squacc optimization algorithm in both RNN and BiLSTM effectively optimized the classifier’s parameters and helped to obtain an efficient output. The performance metrics BLEU, ROUGE, CIDEr, METEOR, and SPICE demonstrated the superiority of the research. Later in the third phase, we developed a digital twin for the TEC District, Monterrey, Nuevo Leon, Mexico. We carried out this work by defining and developing five layers in our digital twin model: the ground layer, BIM layer, Mobility infrastructure, district 3D model, and finally, the digital twin. Here, we used some common software applications for the development of TEC District Digital Twin, such as Esri ArcGIS for data management (Map data, GeoJson, and 2D data), City Engine for assigning rule files of buildings, vegetation, water, road network and manipulation of 2D, 3D data, and QGIS for shape files. 3D modeling software Blender, and Nvidia Omniverse for the final digital twin was used. Using the potential of these tools and techniques, Digital Twin is proposed for the buildings, road network, and vegetation of the TEC District (Tecnologico De Monterrey District) region. Here, we integrated our dense captioning model with the TEC Distrcit digital twin to obtain captions of security events using knowledge graphs. The general idea of this investigation is to provide a better understanding of digital twins and dense video captioning. By leveraging the capabilities of these technologies, organizations can generate more accurate and insightful analyses of digital twin models, enabling a wide range of applications in various fields. These technologies will also aid surveillance and security in urban planning, offering significant benefits for organizations looking to optimize their operations and enhance their decision-making processes. All the models described in this investigation can be applied to a wider range of instances to achieve acceptable results with respect to time and quality.
- Posibilidades tecnoestéticas del GIF: reflexiones críticas y creativas desde Latinoamérica(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024) Abbruzzese Abajían, Mariángela; ZAVALA SCHERER, DIEGO; 220138; Bañuelos Capistrán, Jacob; emiggomez; Rodríguez Blanco, Sergio; Escuelas de Humanidades y Educación; Campus Ciudad de México; Zavala Scherer, DiegoEl GIF nace en 1987 como un formato de archivo de imágenes digitales, pero excede las condiciones de su invención y se convierte en un medio para la expresión afectiva, el humor, la reacción en chats, las prácticas meméticas, el placer visual, y las prácticas artísticas. Este permite que las imágenes se muevan en loops infinitos, sin controles de reproducción, con una paleta cromática simple que lo vuelve accesible para ser visto y compartido entre miles de usuarios de la red. Esta tesis doctoral propone un modelo reflexivo y analítico en torno a las posibilidades tecnoestéticas (Simondon, 2017) del GIF, utilizando la metodología de investigación-creación (Ballesteros y Beltrán, 2018), para diseñar e impartir un taller de reflexión y creación de GIFs en los siguientes espacios educativos: Centro de Cultura Digital, A.C. Yolia, El Rule Comunidad de Saberes, Tecnológico de Monterrey Campus Toluca, Casa Ensenada 60 Centro Cultural, Casa del Lago UNAM y Biblioteca Francisco Zarco. Tanto el proceso como los resultados, permiten abrir caminos hacia las pedagogías libres y la reflexión crítica, creativa y propositiva desde Latinoamérica. Caminos que, desde la experiencia práctica, se desmarcan del frenesí de la novedad y el hiperconsumo, propios de la dinámica capitalista que define las formas dominantes de uso de las tecnologías digitales.
- Compliant mechanisms and joints: control and tailored stiffness via architectured metamaterials(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-06-05) Arredondo Soto, Mauricio; Gómez Espinosa, Alfonso; dnbsrp; Román Flores, Armando; Urbina Coronado, Pedro Daniel; Jiménez Martínez, Moisés; School of Engineering and Sciences; Campus Monterrey; Cuan Urquizo, EnriqueIn this work the compliance matrix method is used to develop an analytical methodology for the kinetostatic analysis of Flexure-Based Compliant Parallel Mechanisms (FBCPM) under arbitrary force and displacements conditions. Furthermore, the characteristics of metamaterials, specifically the zero Poisson's ratio lattice structures, are used to design a novel type of prismatic compliant joints namely Zero Poisson's ratio Prismatic Compliant Joints (ZP-PCJ) with advantageous features such as high flexibility in a desired direction while achieving favorable levels of stiffness in the non-desired directions, and accurate analytical models that allow their implementation in FBCPM. In the first chapter, the relevant concepts are presented, in addition, the main problem, hypothesis and objectives are stated. The second chapter presents the literature review on the state of the art in the topics of the kinematic analysis of compliant mechanisms and the use of metamaterials for compliance purposes. Chapter three deals with the theoretical background corresponding to the Compliance Matrix Method (CMM) and the kinematic analysis of FBCPM using this method, ending with a summary of the CMM that synthesizes and unifies all the variants commonly found in the literature. In chapter four the proposed analytical method for the kinematic analysis of FBCPM is presented and successfully validated by three cases: i) using a 2D-FBCPM comparing with FEA-simulation results, ii) using a 3D-FBCPM comparing with FEA-simulation results, and iii) using a Compliant Spherical Parallel Mechanism (CSPM) comparing with both FEA-simulation and experimental results where input displacements were also used. Chapter five deals with the concept of meta-flexures, introducing the new type of prismatic compliant joints called ZP-PCJ based on the advantageous characteristics of zero Poisson's lattice structures. The compliance matrices of these ZP-PCJs are obtained analytically using Castigliano's second theorem and compliance simplification, and successfully validated with both FEA-simulations and experimental tests. In addition, the proposed ZP-PCJs are implemented in a 2D-FBCPM whose kinetostatic analysis is performed with the method presented in the previous chapter, demonstrating via FEA-simulations, the validity and accuracy of their analytical models. Finally, conclusions and future work are described in chapter six.
- Competencia intercultural en la enseñanza de idiomas. Una perspectiva desde la dimensión afectiva y el aprendizaje colaborativo(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2016-10) Focsan, Raluca; Flores Fahara, Manuel; dnbsrp; Escuela de Humanidades y Educación; Campus MonterreyEl presente estudio examina la conjunción áulica entre la competencia intercultural y la dimensión afectiva bajo el uso de la estrategia de aprendizaje colaborativo a través de la pregunta de investigación ¿De qué manera los docentes incorporan la dimensión afectiva de la competencia intercultural en la enseñanza de idiomas y como ésta es asimilada y desarrollada desde la perspectiva de los alumnos bajo un ambiente de aprendizaje colaborativo? Sobre ésta se elucida la transformación de la enseñanza de idiomas en una institución educativa de nivel superior y su departamento de idiomas en plena formación como preocupación que atiende la línea de una política didáctica lingüística y brecha que existe entre la teoría y su práctica, partiendo de las tres dimensiones de la competencia intercultural, cognitiva, comportamental y afectiva, esta última en virtud del contexto en estudio. Desde un enfoque metodológico cualitativo de tipo etnográfico, el investigador aborda de forma natural en el campo las consideraciones de los sujetos de estudio, cinco docentes y siete alumnos de licenciatura a través de dos entrevistas semi estructuradas y una tabla de observación como técnicas para la recolección de datos. Procesualmente, se realiza la presentación, análisis interpretativo y discusión de los resultados conforme las categorías de análisis surgidas. Por último, se lucen las conclusiones y recomendaciones, las cuales advierten la competencia intercultural en toda su complejidad, abriendo puertas a la dimensión afectiva y en colaboración como método de enseñanza integral de idiomas.
