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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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- eComplexity: psychometric properties to test the validity and reliability of the instrument(ERIC, 2024-07) Castillo Martínez, Isolda Margarita; Velarde Camaqui, Davis; Ramírez Montoya, María Soledad; Sanabria Zepeda, Jorge Carlos; https://ror.org/03ayjn504Reasoning for complexity is a fundamental competency in these complex times for solutions to social problems and decision-making. The purpose of this paper is to demonstrate the validity and reliability of the eComplexity instrument by presenting its psychometric properties. The instrument consists of a Likert-type scale questionnaire designed to measure college students' perceptions of their mastery levels of complex reasoning competency as well as the subcompetencies that comprise it. The instrument was applied to higher education students in different countries of Latin America and Spain. The questionnaire articulates four types of thinking: systemic, scientific, critical and innovative and consists of 25 items. The methodology used was instrumental and psychometric, which seeks to demonstrate the validity and reliability of the eComplexity questionnaire. The results obtained from the research were as follows: The exploratory factor analysis indicated a Kaiser-Meyer-Olkin index (KMO) > .80, a significance of p< .05 and a Cronbach's Alpha value of 0.93. Likewise, a Confirmatory Factor Analysis was carried out and was possible to corroborate the internal structure validity of the instrument. In addition to Cronbach's Alpha coefficient, McDonald's Omega, and Guttman's Lambda coefficients were calculated to calculate reliability. With the results obtained it was possible to conclude that the instrument is valid and reliable, can be used in various university contexts to support integrated training necessary to address current challenges and contribute to educational research. It is recommended for future studies that the research can be expanded by using an instrument that can move from perceptual terms to measuring levels of complex reasoning mastery, but it is valuable to contrast with the students' perception to have a broader vision.
- The impact of large language models on higher education: exploring the connection between AI and Education 4.0(Frontiers, 2024-06-14) Peláez Sánchez, Iris Cristina; Velarde Camaqui, Davis; Glasserman Morales, Leonardo David; https://ror.org/03ayjn504; https://ror.org/0297axj39; Hernández Montoya, DianaThe digital transformation has profoundly affected every facet of human life, with technological advancements potentially reshaping the economy, society, and our daily living and working modalities. Artificial Intelligence (AI), particularly Generative AI (GAI), has emerged as a pivotal disruption in education, showcasing the capability to produce diverse and context-relevant content. Generative Artificial Intelligence (GAI) has revolutionized natural language processing, computer vision, and creative arts. Large language models (LLMs) like GPT-4 and Open Assistant and tools like DALL-E and Midjourney for the visual and creative domain are increasingly used for various tasks by students and others with critical information needs. AI presents novel avenues for crafting effective learning activities and developing enhanced technology-driven learning applications in the educational sector. However, integrating AI with a pedagogical focus pose challenge. Education 4.0, which integrates emerging technologies and innovative strategies, aims to prepare new generations for a technologically fluid world. This systematic literature review aims to analyze the use of LLMs in higher education within the context of Education 4.0’s pedagogical approaches, identifying trends and challenges from a selection of 83 relevant articles out of an initial set of 841 papers. The findings underscore the significant potential of LLMs to enrich higher education, aligning with Education 4.0 by fostering more autonomous, collaborative, and interactive learning. It highlights the necessity for human oversight to ensure the quality and accuracy of AI-generated content. It addresses ethical and legal challenges to ensure equitable implementation, suggesting an exploration of LLM integration that complements human interaction while maintaining academic integrity and pedagogical foundation.