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Permanent URI for this collectionhttps://hdl.handle.net/11285/345284
Artículo científico o editorial en una publicación periódica académica sujeto a revisión de pares. Cumple con los índices internacionales o bases de datos de amplia cobertura, como el listado del Current Contents, ISI WEB of Knowledge (http://isiknowledge.com/) e índice de revistas mexicanas de CONACYT (www.conacyt.mx/dac/revistas). Éstos indizan y resumen los artículos de revistas seleccionadas, en todas las áreas del saber.
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- Navigating the pitfalls: analyzing the behavior of LLMs as a coding assistant for computer science students - a systematic review of the literature(IEEEXplore, 2024-08-14) Pirzado, Farman Ali; Ahmed, Awais; Mendoza Urdiales, Roman Alejandro; Terashima Marin, Hugo; https://ror.org/03ayjn504; https://ror.org/04qr3zq92In recent years, large language models (LLMs) have been employed significantly in different domains of computing education. Nevertheless, these models have been focused on essential adherence to their integration as coding assistants in computing education. However, attention has been switched to thoroughly examining and analyzing LLM behavior, particularly in computing education for programming tasks such as code generation, code explanation, and programming error message explanation. Therefore, it becomes imperative to understand their behavior to examine potential pitfalls. This article addresses this gap systematically and details how different LLM-based coding chatbots, such as ChatGPT, Codex, Copilot, and others, react to various coding inputs within computing education. To achieve this objective, we collected and analyzed articles from 2021 to 2024, and 72 studies were thoroughly examined. These objectives include investigating the existing limitations and challenges associated with utilizing these systems for coding tasks, assessing their responses to prompts containing coding syntax, examining the impact of their output on student learning, and evaluating their performance as debugging tools. The findings of this review highlight that it is premature to incorporate these systems into computing education due to their limitations that may limit their effectiveness as comprehensive coding assistants for computer science students. These limitations include issues with handling prompts containing code snippets, potential negative impacts on student learning, limited debugging capabilities, and other ineffectiveness. The finding also reports multiple research directions that can be considered in future research related to LLMs in computing education.
- Experimenting with soft robotics in education: A systematic literature review from 2006 to 2022(IEEEXplore, 2024-03-05) Cayetano Jiménez, Israel Ulises; Martinez Rios, Erick Axel; Bustamante Bello, Rogelio; Ramírez Mendoza, Ricardo Ambrocio; Ramírez Montoya, María Soledad; https://ror.org/03ayjn504Educational robotics (ER) is a discipline of applied robotics focused on teaching robot design, analysis, application, and operation. Traditionally, ER has favored rigid robots, overlooking the potential of soft robots (SRs). While rigid robots offer insights into dynamics, kinematics, and control, they have limitations in exploring the depths of mechanical design and material properties. In this regard, SRs present an opportunity to expand educational topics and activities in robotics through their unique bioinspired properties and accessibility. Despite their promise, there is a notable lack of research on SRs as educational tools, limiting the identification of research avenues that could promote their adoption in educational settings. This study conducts a Systematic Literature Review (SLR) to elucidate the impact of SRs across academic levels, pedagogical strategies, prevalent artificial muscles, educational activities, and assessment methods. The findings indicate a significant focus on K-12 workshops utilizing soft pneumatic actuators. Furthermore, SRs have fostered the development of fabrication and mechanical design skills beyond mere programming tasks. However, there is a shortage of studies analyzing their use in higher education or their impact on learning outcomes, suggesting a critical need for comprehensive evaluations to determine their effectiveness, rather than solely relying on surveys for student feedback. Thus, there is an opportunity to explore and evaluate the use of SRs in more advanced settings and multidisciplinary activities, urging for rigorous assessments of their influence on learning outcomes. By undertaking this, we aim to provide a foundation for integrating SRs into the educational robotics curriculum, potentially transforming teaching methodologies and enriching students’ learning experiences.
- Adaptive evaluation for barriers elimination: The OpenEDR4C platform(IEEEXplore, 2024) Álvarez Icaza Longoria, Inés; Molina Espinosa, José Martín; Suárez Brito, Paloma; Alvarado Reyes, Ignacio; Tecnologico de Monterrey, Institute for the Future odf Education, Monterrey, Nuevo Leon; https://ror.org/03ayjn504The demands of contemporary work and education environments, embodied in Industry and Education 4.0 paradigms, have caused special attention to reducing the digital and educational gap among marginalized students and lifelong learners. Adaptive learning has become a relevant strategy to increase student engagement as it fosters better inclusion, equity, and learning outcome results. Sustainable Development Goal No. 4, Quality of Education, will be reached when every resource, platform, and program can be accessed by individuals regardless of their background, abilities, or capacities in every moment of their lives. The OpenEDR4C is a digital platform created to offer open educational resources (OERs) to scale up the Complex Thinking mega-competence through social, scientific, and technological entrepreneurship training. The platform is also designed to eliminate barriers in five dimensions across the learning path offered by each student according to a self-declared profile. The framework sets a differentiated pathway for the learning experience built with a decision tree and diverse multichannel educational resources. The pilot implementation of the platform allowed the final development stage to validate the functional platform launch. The findings of this study are a helpful contribution to digital education environment design and conceptualization, as they offer (1) a tool for inclusive and accessible platform conceptualization, (2) a profiling strategy framework for adaptive learning paths, and (3) the identification of classes or groups of users based on their needs and characteristics to offer the corresponding content.
- Forecasting gender in open education competencies: A machine learning approach(IEEEXplore, 2023-11-29) Ibarra Vázquez, Gerardo; Ramírez Montoya, María Soledad; https://ror.org/03ayjn504This article aims to study the performance of machine learning models in forecasting gender based on the students' open education competency perception. Data were collected from a convenience sample of 326 students from 26 countries using the eOpen instrument. The analysis comprises 1) a study of the students' perceptions of knowledge, skills, and attitudes or values related to open education and its sub-competencies from a 30-item questionnaire using machine learning models to forecast participants' gender, 2) validation of performance through cross-validation methods, 3) statistical analysis to find significant differences between machine learning models, and 4) an analysis from explainable machine learning models to find relevant features to forecast gender. The results confirm our hypothesis that the performance of machine learning models can effectively forecast gender based on the student's perceptions of knowledge, skills, and attitudes or values related to open education competency.
- Scenarios for student-centered learning: RFiD Pocket Lab and IoT Platform as teaching tools(IEEEXplore, 2022-05-11) Crespo Saucedo, Raúl; López Caudana, Edgar Omar; Romo Medrano Mora, Katya Eugenia; Mantilla Caeiros, Alfredo Victor; https://ror.org/03ayjn504This paper contributes to the realization of learner-centered learning scenarios. It is developing an IoT learning platform for different educational levels. On the other hand, the use of “Pocket Labs” is proposed as an effective tool in the meaningful learning of emerging technologies. The fact that these tools can guide the student towards the realization of projects using a set of technologies that are on the rise generate as advantages: an increase in personnel familiar with the IoT, which has an economic impact on the industry, or that the learning is long term, as students develop and improve their understanding. Furthermore, the intention is to develop Information and Communication Tools (ICT) disciplinary competencies, which aligns with the spirit of the Tecnologico de Monterrey’s educational model. Both strategies are a relatively new teaching concept that supports students’ creativity and initiative, allowing them to wear and experiment with actual equipment at any time and place of their choice. Both experiences result in significant long-term learning in fundamental technologies for Education 4.0 and Industry 4.0. These include IoT, RFID (Radio Frequency Identification), NFC (Near Field Communications), Cloud Services, Etc.
- A basic permanent magnets array interaction project for teaching artificial intelligence as a complementary model(IEEEXplore, 2020-08-18) Varas Ibarra, Carlos; Sánchez Ochoa Ariza, Fernando; Luna Calderón, Luis Julio; Molina Gutiérrez, Arturo; López Caudana, Edgar Omar; https://ror.org/03ayjn504There are new algorithms such as artificial intelligence (AI) methodologies that have achieved accurate representation of experimental systems. On the other hand,undergraduate freshmen students must understand AImethodologies since the industry has developed several products based on those and some academic problems also can be solved using AI. If those students do not learn how to model real systems using AI, they will be losing the opportunity of applying this powerful tool for solving several real problems in their professional life. Since the AI model can be a representation for forecasting the performance of the real model, this model can help the design process and provide information during its operation. This paper proposes an engineering project to teach artificial intelligence algorithms using real systems that are non-linear. Since permanent magnets are used in several applications, they can be attractive for modeling those when they are interacting between them; hence, this paper shows the interaction among them when they are deployed as an electrical power source. Moreover, this source could be classified as a renewable energy source. The basic generation of electrical energy is based on changing the magnetic field. Although the operation principle is basic, the electrical source has a non-liner description that is extremely complex so AI could be applied to create a model that represents those non-linear relationships in a precise manner. The main goal of this work is to describe an undergraduate project that can be used for teaching how to model a real system using AI algorithms. The main characteristics and properties of the permanent magnets are studied for the comprehension of how magnets can be implemented. It is also examined the viability for the construction of an electric motor using only permanent magnets, based on the analysis of different designs and materials and finally an AI model is created.