Inteligencia artificial y aprendizaje colaborativo asistido por computadora en la programación: un estudio de mapeo sistemático

Objective: The Computer-Supported Collaborative Learning (CSCL) approach integrates artificial intelligence (AI) to enhance the learning process through collaboration and information and communication technologies (ICTs). In this sense, innovative and effective strategies could be designed for learning computer programming. This paper presents a systematic mapping study from 2009 to 2021, which shows how the integration of CSCL and AI supports the learning process in programming courses. Methodology: This study was conducted by reviewing data from different bibliographic sources such as Scopus, Web of Science (WoS), ScienceDirect, and repositories of the GitHub platform. It employs a quantitative methodological approach, where the results are represented through technological maps that show the following aspects: i) the programming languages used for CSCL and AI software development; ii) CSCL software technology and the evolution of AI; and iii) the ACM classifications, research topics, artificial intelligence techniques, and CSCL strategies. Results: The results of this research help to understand the benefits and challenges of using the CSCL and AI approach for learning computer programming, identifying some strategies and tools to improve the process in programming courses (e.g., the implementation of the CSCL approach strategies used to form groups, others to evaluate, and others to provide feedback); as well as to control the process and measure student results, using virtual judges for automatic code evaluation, profile identification, code analysis, teacher simulation, active learning activities, and interactive environments, among others. However, for each process, there are still open research questions. Conclusions: This work discusses the integration of CSCL and AI to enhance learning in programming courses and how it supports students' education process. No model integrates the CSCL approach with AI techniques, which allows implementing learning activities and, at the same time, observing and analyzing the evolution of the system and how its users (students) improve their learning skills with regard to programming. In addition, the different tools found in this paper could be explored by professors and institutions, or new technologies could be developed from them.Objetivo: El enfoque de aprendizaje colaborativo asistido por computadora (CSCL) integra la inteligencia artificial (IA) para mejorar el proceso de aprendizaje a través de la colaboración y las tecnologías de la información y la comunicación (TICs). En este sentido, se podrían diseñar estrategias innovadoras y efectivas para el aprendizaje de la programación de computadoras. Este artículo presenta un estudio sistemático de mapeo de los años 2009 a 2021, el cual muestra cómo la integración del CSCL y la IA apoya el proceso de aprendizaje en cursos de programación. Metodología: Este estudio se realizó mediante una revisión de datos proveniente de distintas fuentes bibliográficas como Scopus, Web of Science (WoS), ScienceDirect y repositorios de la plataforma GitHub. El trabajo emplea un enfoque metodológico cuantitativo, en el cual los resultados se representan a través de mapas tecnológicos que muestran los siguientes aspectos: i) los lenguajes de programación utilizados para el desarrollo de software de CSCL e IA; ii) la tecnología de software CSCL y la evolución de la IA; y iii) las clasificaciones, los temas de investigación, las técnicas de inteligencia artificial y las estrategias de CSCL de la ACM. Resultados: Los resultados de esta investigación ayudan a entender los beneficios y retos de usar el enfoque de CSCL e IA para el aprendizaje de la programación de computadoras, identificando algunas estrategias y herramientas para mejorar el proceso en cursos de programación (e.g., La implementación de estrategias del enfoque CSCL utilizadas para formar grupos, de otras para evaluar y de otras para brindar retroalimentación); así como para monitorear el proceso y medir los resultados de los estudiantes utilizando jueces virtuales para la evaluación automática del código, identificación de perfiles, análisis de código, simulación de profesores, actividades de aprendizaje activo y entornos interactivos, entre otros. Sin embargo, aún hay preguntas investigación por resolver para cada proceso. Conclusiones: Este trabajo discute la integración del CSCL y la IA para mejorar el aprendizaje en cursos de programación y cómo esta apoya el proceso educativo de los estudiantes. Ningún modelo integra el enfoque CSCL con técnicas de IA, lo cual permite implementar actividades de aprendizaje y, al mismo tiempo, observar y analizar la evolución del sistema y de la manera en que sus usuarios (estudiantes) mejoran sus habilidades de aprendizaje con respecto a la programación. Adicionalmente, las diferentes herramientas encontradas en este artículo podrían ser exploradas por profesores e instituciones, o podrían desarrollarse nuevas tecnologías a partir de ellas

Multi Agent Systems

Research on multi-agent systems is enlarging our future technical capabilities as humans and as an intelligent society. During recent years many effective applications have been implemented and are part of our daily life. These applications have agent-based models and methods as an important ingredient. Markets, finance world, robotics, medical technology, social negotiation, video games, big-data science, etc. are some of the branches where the knowledge gained through multi-agent simulations is necessary and where new software engineering tools are continuously created and tested in order to reach an effective technology transfer to impact our lives. This book brings together researchers working in several fields that cover the techniques, the challenges and the applications of multi-agent systems in a wide variety of aspects related to learning algorithms for different devices such as vehicles, robots and drones, computational optimization to reach a more efficient energy distribution in power grids and the use of social networks and decision strategies applied to the smart learning and education environments in emergent countries. We hope that this book can be useful and become a guide or reference to an audience interested in the developments and applications of multi-agent systems

Towards an automatic monitoring for higher education learning design

The development of new Information Technologies (IT) has originated new possibilities to design pedagogical methodologies that provide the necessary knowledge and skills in the higher education. This paper presents a metadata-based model representation that is used to represent, detect, and even automatically correct possible pitfalls in the schedule process of a Learning Design (LD) in e-learning environments. This metadata-based model is combined with Artificial Intelligence techniques, such as, planning and scheduling to monitor how is evolving a particular LD, and to propose solutions in those modules of the design that learning problems among the students have been found.This work was funded by the Universidad de Alcalá project UAH PI2005/084 and the CICYT project TSI2006- 12085

Using learning by doing methodology for teaching multi-agent systems

[EN] In recent years the teaching of subjects related to Artificial Intelligence has grown notably in higher education degrees. This is the case of the discipline of multi-agent systems, which usually is part of the majority of master's degrees in Artificial Intelligence. Multi-agent systems (MAS) offer solutions for distributed decision making, where a set of autonomous intelligent agents must reach an agreement to solve a problem. These types of problems are usually complex and distributed, difficult to abstract and simplify for classroom teaching. The main problem that teachers of this subject have to face, is to be able to integrate the whole set of related techniques and algorithms in a practical example that is easy to understand and address within the framework of the planning of a course. This paper deals with the use of the "learning by doing" methodology in a subject of multi-agent systems in the Master's Degree in Artificial Intelligence at the Universitat Politècnica de València. This methodology is applied by avoiding master classes to focus on practice. The classes become a scientific-technological experience. The students and the teacher are a team working with a common purpose, seeking to achieve a goal. To do this, the whole course has been reformulated, proposing the students to solve different typical problems of the MAS area on the same domain, in this case the improvement of urban mobility and the efficient use of energy in the cities. It is considered to be a sufficiently current topic that can motivate the student to participate and propose solutions. To achieve this objective, a multi-agent system tool has been developed that allows students to simulate the different situations proposed and develop solutions. The tool provides them with an urban simulation environment where they can easily introduce their own strategies to be carried out by each simulation agent. In this way, students are proposed different challenges where they can develop negotiation strategies to simulate the operation of urban taxi fleets, and cooperation strategies, where different agents help each other to achieve a common goal. This tool, called SimFleet, has been developed in an open way and published as open source, so that it can be used by any teaching team that wishes to do so, and even receive external contributions and improvements thanks to its open character. This learning by doing methodology supported by the SimFleet simulation tool has been applied in two consecutive academic years obtaining better results in student assessment and learning than in previous courses. Furthermore, the results of the student satisfaction surveys have shown a notable increase when using these technologies, which reinforces the idea that this type of learning is more useful and more satisfactory for students.This work was partially supported by MINECO/FEDER RTI2018-095390-B-C31 project of the Spanish government.Palanca Cámara, J.; Jordán, J.; Julian Inglada, VJ. (2021). Using learning by doing methodology for teaching multi-agent systems. IATED. 3866-3871. https://doi.org/10.21125/inted.2021.0794S3866387

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

SIMBA: a simulator for business education and research

Business simulators are used for decision-making since different scenarios can be evaluated without risk. They are also used in business management education. The main goal of this paper is to introduce SIMBA (SIMulator for Business Administration), a new simulator that serves as a web-based platform for business education, permitting both classroom and distance education. This paper also adds a research aspect in business intelligence because SIMBA can be used as a fieldwork tool for the development and evaluation of intelligent agents. The simulator creates a more complex competitive environment in which intelligent agents play the role of business decision makers.This work has been partially sponsored by a regional project CCG08-UC3M/TIC-4141 of the Comunidad de Madrid, a national project TIN2008-06701-C03-03 of the Ministerio de Ciencia e Innovación of Spain and a contract with Simuladores Empresariales S.L.Publicad

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications