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Item ¿Cómo realizar una revisión sistemática de literatura?(Grupo GRIAL, 2024-04-18) García-Peñalvo, F. J.Curso impartido en el ICE de la Universidad Politécnica de Madrid (ETSI Caminos) el 18 de abril de 2024 dentro del Plan de Formación del Profesorado del Curso 2023-2024. El objetivo del curso es introducir a los investigadores a la realización de una revisión de literatura. Se parte de la necesidad de realizar revisiones de literatura para conocer el estado de la cuestión, distinguiendo el concepto de revisión del de revisión sistemática. Hay varios tipos de revisiones sistemáticas que se presentan y se inciden en los tipos más utilizados, la revisión sistemática, las revisiones de ámbitos y los mapeos de literatura. Una vez que se tienen los conceptos básicos se introducen los marcos metodológicos de referencia para realizar las revisiones sistemáticas. Se describen en detalle las tres grandes fases de una revisión sistemática (planificación, realización e informe). Se termina con un sencillo caso de estudio de un mapeo sistemática de literatura. Es importante apoyarse en diferentes herramientas durante el proceso, se introducen varias, pero se pone un mayor énfasis en Parfisal y Bibliometrix. Los objetivos específicos del curso son: 1. Conocer qué se entiende por revisión sistemática de literatura. 2. Evaluar el esfuerzo necesario para realizar una revisión sistemática de literatura. 3. Planificar una revisión sistemática de literatura. 4. Realizar una revisión sistemática de literatura. 5. Plasmar el trabajo realizado en un informe o artículo de investigación. Los contenidos del seminario son: 1. Introducción a las revisiones sistemáticas 2. Revisiones sistemáticas de literatura vs. Revisiones de mapeo de literatura y Revisiones de alcance 3. Marcos metodológicos de referencia para la realización de revisiones sistemáticas de literatura 4. Fase de planificación 5. Fase de realización 6. Fase de informe 7. Caso de estudio 8. Colección de flujos de trabajo y herramientas para realizar revisiones de literatura 9. Bibliometrix 10. ConclusionesItem TIC, motivación y rendimiento académico en educación primaria: meta-análisis, revisión de literatura y estado de la cuestión(Ediciones Universidad de Salamanca, 2024-03-25) Ábalos-Aguilera, F.; Romero-Rodríguez, L. M.; Bernal Bravo, C.Al igual que está ocurriendo en países como Suecia, algunos centros españoles se encuentran en la diatriba de retirar los dispositivos digitales de las aulas de primaria. El reciente informe PISA 2023 alerta de un desplome del rendimiento académico de los alumnos españoles, identificando que la creciente exposición a las pantallas podría contribuir a que los estudiantes estén “más tiempos distraídos”. En la misma línea, el informe PIRLS de 2021 ya concluía que los alumnos españoles de entre 9 y 10 años han experimentado una disminución de 7 puntos en los últimos años, asociándolo a las distracciones que la exposición a pantallas y la conexión a Internet pueden estar generando en clase. La presente investigación busca examinar, mediante un meta-análisis y revisión de la literatura científica, las posiciones de los investigadores en el último lustro (2019-2023) acerca de la incorporación de las TIC en las aulas de educación primaria. Los resultados revelan que la mayoría de los estudios cuasi-experimentales (n=10) sobre la correlación entre el uso de recursos digitales, el engagement y el rendimiento académico ha sido positivo, sobre todo si esta incorporación de las TIC viene acompañada de estrategias metodológicas como la gamificación, el flipped classroom o el Aprendizaje Basado en Proyectos (ABP). Factores como el diseño de los recursos digitales, la edad del profesorado y la falta de formación efectiva, se identifican como determinantes en la preferencia por metodologías convencionales en lugar de la adopción de recursos digitales. Esta investigación pone de manifiesto que la simple inclusión de las TIC no es un predictor de mejoramiento, que tantos autores han alegado, quizá con demasiado entusiasmo, y en cierta medida con algunas perspectivas utópicas.Item ModelViz: A Model-Driven Engineering Approach for Visual Analytics System Design(IEEE, 2024-03-29) Khakpour, A.; Vázquez-Ingelmo, A.; Colomo-Palacios, R.; García-Peñalvo, F. J.; Martini, A.Visual analytics systems should be able to consolidate data from disparate sources, conduct exploratory analysis, create visualizations that suit different users, and integrate seamlessly with decision-making activities to support data-driven decision-making. However, current mainstream visual analytics solutions often lack support for all these requirements. To address this gap, we propose the use of model-driven engineering to design visual analytics systems. To demonstrate the feasibility of this approach, we developed a Domain-Specific Modeling Language (DSML) named ModelViz to design visual analytics systems for consumer goods supply chain applications. Furthermore, we present the work of our DSML, using data from a manufacturing company as a case study. Finally, we evaluated ModelViz quantitatively by comparing it with other similar works from the literature. Our results demonstrate that this approach meets the requirements and provides a promising direction for designing visual analytics systems by considering domain-specific aspects to help achieve business goals.Item Evaluating ChatGPT-Generated Linear Algebra Formative Assessments(Universidad Internacional de la Rioja, 2024-02-13) Rigaud Téllez, N.; Rayón Villela, P.; Blanco Bautista, R.This research explored Large Language Models potential uses on formative assessment for mathematical problem-solving process. The study provides a conceptual analysis of feedback and how the use of these models is related in the context of formative assessment for Linear Algebra problems. Particularly, the performance of a popular model known as ChatGPT in mathematical problems fails on reasoning, proofs, model construction, among others. Formative assessment is a process used by teachers and students during instruction that provides feedback to adjust ongoing teaching and learning to improve student’s achievement of intended instructional outcomes. The study analyzed and evaluated feedback provided to engineering students in their solutions, from both, instructors and ChatGPT, against fine-grained criteria of a formative feedback model that includes affective aspects. Considering preliminary outputs, and to improve performance of feedback from both agents’ instructors and ChatGPT, we developed a framework for formative assessment in mathematical problem-solving using a Large Language Model (LLM). We designed a framework to generate prompts, supported by common Linear Algebra mistakes within the context of concept development and problem-solving strategies. In this framework, the instructor acts as an agent to verify tasks in a math problem assigned to students, establishing a virtuous cycle of learning of queries supported by ChatGPT. Results revealed potentialities and challenges on how to improve feedback on graduate-level math problems, by which both educators and students adapt teaching and learning strategies.Item Can Generative AI Solve Geometry Problems? Strengths and Weaknesses of LLMs for Geometric Reasoning in Spanish(Universidad Internacional de la Rioja, 2024-02-28) Parra, V.; Sureda, P.; Corica, A.; Schiaffino, S.; Godoy, D.Generative Artificial Intelligence (AI) has emerged as a disruptive technology that is challenging traditional teaching and learning practices. Question-answering in natural language fosters the use of chatbots, such as ChatGPT, Bard and others, that generate text based on pre-trained Large Language Models (LLMs). The performance of these models in certain areas, like Math problem solving is receiving a crescent attention as it directly impacts on its potential use in educational settings. Most of these evaluations, however, concentrate on the construction and use of benchmarks comprising diverse Math problems in English. In this work, we discuss the capabilities of most used LLMs within the subfield of Geometry, in view of the relevance of this subject in high-school curricula and the difficulties exhibited by even most advanced multimodal LLMs to deal with geometric notions. This work focuses on Spanish, which is additionally a less resourced language. The answers of three major chatbots, based on different LLMs, were analyzed not only to determine their capacity to provide correct solutions, but also to categorize the errors found in the reasoning processes described. Understanding LLMs strengths and weaknesses in a field like Geometry can be a first step towards the design of more informed methodological proposals to include these technologies in classrooms as well as the development of more powerful automatic assistance tools based on generative AI.Item Generative Artificial Intelligence in Product Design Education: Navigating Concerns of Originality and Ethics(Universidad Internacional de la Rioja, 2024-02-20) Barlett, K. A.; Camba, J. D.Image-generative artificial intelligence (AI) is increasingly being used in the product design process. In this paper, we present examples of how it is being used and discuss the possibilities of how applications may evolve in the future. We discuss the legal and ethical implications of image-generative AI, including concerns about bias, hidden labor, theft from artists, lack of originality in the outputs, and lack of copyright protection. We discuss how these concerns apply to design education and provide recommendations to educators about how AI should be addressed in the design classroom. We recommend that educators introduce AI as one tool among many in the designer’s toolkit and encourage it to be used as a process tool rather than for generating final design deliverables. We also provide guidance for how educators might engage students in discussions about AI to enhance their learning.Item Virtual Reality and Language Models, a New Frontier in Learning(Universidad Internacional de la Rioja, 2024-02-21) Izquierdo-Domenech, J.; Linares-Pellicer, J.; Ferri-Molla, I.The proposed research introduces an innovative Virtual Reality (VR) and Large Language Model (LLM) architecture to enhance the learning process across diverse educational contexts, ranging from school to industrial settings. Leveraging the capabilities of LLMs and Retrieval-Augmented Generation (RAG), the architecture centers around an immersive VR application. This application empowers students of all backgrounds to interactively engage with their environment by posing questions and receiving informative responses in text format and with visual hints in VR, thereby fostering a dynamic learning experience. LLMs with RAG act as the backbones of this architecture, facilitating the integration of private or domain-specific data into the learning process. By seamlessly connecting various data sources through data connectors, RAG overcomes the challenge of disparate and siloed information repositories, including APIs, PDFs, SQL databases, and more. The data indexes provided by RAG solutions further streamline this process by structuring the ingested data into formats optimized for consumption by LLMs. An empirical study was conducted to evaluate the effectiveness of this VR and LLM architecture. Twenty participants, divided into Experimental and Control groups, were selected to assess the impact on their learning process. The Experimental group utilized the immersive VR application, which allowed interactive engagement with the educational environment, while the Control group followed traditional learning methods. The study revealed significant improvements in learning outcomes for the Experimental group, demonstrating the potential of integrating VR and LLMs in enhancing comprehension and engagement in learning contexts. This study presents an innovative approach that capitalizes on the synergy between LLMs and immersive VR technology, opening avenues for a transformative learning experience that transcends traditional boundaries and empowers learners across a spectrum of educational landscapes.Item A Trustworthy Automated Short-Answer Scoring System Using a New Dataset and Hybrid Transfer Learning Method(Universidad Internacional de la Rioja, 2024-02-05) Maslim, M.; Wang, H.-C.; Putra, C. D.; Prabowo, Y. D.To measure the quality of student learning, teachers must conduct evaluations. One of the most efficient modes of evaluation is the short answer question. However, there can be inconsistencies in teacher-performed manual evaluations due to an excessive number of students, time demands, fatigue, etc. Consequently, teachers require a trustworthy system capable of autonomously and accurately evaluating student answers. Using hybrid transfer learning and student answer dataset, we aim to create a reliable automated short answer scoring system called Hybrid Transfer Learning for Automated Short Answer Scoring (HTL-ASAS). HTL-ASAS combines multiple tokenizers from a pretrained model with the bidirectional encoder representations from transformers. Based on our evaluation of the training model, we determined that HTL-ASAS has a higher evaluation accuracy than models used in previous studies. The accuracy of HTL-ASAS for datasets containing responses to questions pertaining to introductory information technology courses reaches 99.6%. With an accuracy close to one hundred percent, the developed model can undoubtedly serve as the foundation for a trustworthy ASAS system.Item A Cybernetic Perspective on Generative AI in Education: From Transmission to Coordination(2024-02-21) Griffiths, D.; Frías-Martínez, E.; Tlili, A.; Burgos, D.The recent sudden increase in the capabilities of Large Language Models (LLMs), and generative AI in general, has astonished education professionals and learners. In formulating a response to these developments, educational institutions are constrained by a lack of clarity concerning human-machine communication and its relationship to models of education. Ideas and models from the cybernetic tradition can help to fill this gap. Two paradigms are distinguished: (1) the transmission paradigm (combining the model of learning implied by the instruments and processes of formal education and the conduit model of communication), and (2) the coordination paradigm (combining the constructivist model of learning and the coordination model of communication). It is proposed that these paradigms have long coexisted in educational practice in a modus vivendi, which is disrupted by LLMs. If an LLM can pass an examination, then from within the transmission paradigm this can only understood as demonstrating that the LLM has indeed learned and understood the material being assessed. At the same time, we know that LLMs do not in fact have the capacity to learn and understand, but rather generate a simulacrum of intelligence. It is argued that this paradox prevents educational institutions from formulating a coherent response to generative AI systems. However, within the coordination paradigm the interactions of LLMs and education institutions can be more easily understood and can be situated in a conversational model of learning. These distinctions can help institutions, educational leaders, and teachers, to frame the complex and nuanced questions raised by GenAI, and to chart a course towards its effective use in education. More specifically, they indicate that to benefit fully from the capabilities of generative AI education institutions need to recognize the validity of the coordination paradigm and adapt their processes and instruments accordingly.Item Ethical Implications and Principles of Using Artificial Intelligence Models in the Classroom: A Systematic Literature Review(Universidad Internacional de la Rioja, 2024-02-27) Tang, L.; Su, Y.-S.The increasing use of artificial intelligence (AI) models in the classroom not only brings a large number of benefits, but also has a variety of ethical implications. To provide effective education, it is now necessary to understand the ethical implications of using AI models in the classroom, and the principles for avoiding and addressing these ethical implications. However, existing research on the ethical implications of using AI models in the classroom is rather sparse, and a holistic overview is lacking. Therefore, this study seeks to offer an overview of research on the ethical implications, ethical principles and the future research directions and practices of using AI models in the classroom through a systematic literature review. Out of 1,445 initially identified publications between 2013 and 2023, 32 articles were included for final coding analysis, identified using explicit inclusion and exclusion criteria. The findings revealed five main ethical implications, namely algorithmic bias and discrimination, data privacy leakage, lack of transparency, decreased autonomy, and academic misconduct, with algorithmic bias being the most prominent (i.e., the number of existing studies is the most), followed by privacy leakage, whereas decreased autonomy and academic misconduct were relatively understudied; and six main ethical principles, namely fairness, privacy, transparency, accountability, autonomy and beneficence, with fairness being the most prominent ethical principle (i.e., the number of existing studies is the most), followed by privacy, while autonomy and beneficence were relatively understudied. Future directions of research are given, and guidelines for future practice are provided: (1) further substantive discussion, understanding and solution of ethical implications are required; (2) the precise mechanism of ethical principles of using AI models in the classroom remains to be elucidated and extended to the implementation phase; and (3) the ethical implications of the use of AI models in the classroom require accurate assessment.