Artificial intelligence tools for academic management: assigning students to academic supervisors

[EN] In the last few years, there has been a broad range of research focusing on how learning should take place both in the classroom and outside the classroom. Even though academic dissertations are a vital step in the academic life of both students, as they get to employ all their knowledge and skills in an original project, there has been limited research on this topic. In this paper we explore the topic of allocating students to supervisors, a time-consuming and complex task faced by many academic departments across the world. Firstly, we discuss the advantages and disadvantages of employing different allocation strategies from the point of view of students and supervisors. Then, we describe an artificial intelligence tool that overcomes many of the limitations of the strategies described in the article, and that solves the problem of allocating students to supervisors. The tool is capable of allocating students to supervisors by considering the preferences of both students and supervisors with regards to research topics, the maximum supervision quota of supervisors, and the workload balance of supervisors.Sanchez-Anguix, V.; Chalumuri, R.; Alberola Oltra, JM.; Aydogan, R. (2020). Artificial intelligence tools for academic management: assigning students to academic supervisors. IATED. 4638-4644. https://doi.org/10.21125/inted.2020.1284S4638464

An artificial intelligence tool for heterogeneous team formation in the classroom

Nowadays, there is increasing interest in the development of teamwork skills in the educational context. This growing interest is motivated by its pedagogical effectiveness and the fact that, in labour contexts, enterprises organize their employees in teams to carry out complex projects. Despite its crucial importance in the classroom and industry, there is a lack of support for the team formation process. Not only do many factors influence team performance, but the problem becomes exponentially costly if teams are to be optimized. In this article, we propose a tool whose aim it is to cover such a gap. It combines artificial intelligence techniques such as coalition structure generation, Bayesian learning, and Belbin's role theory to facilitate the generation of working groups in an educational context. This tool improves current state of the art proposals in three ways: i) it takes into account the feedback of other teammates in order to establish the most predominant role of a student instead of self-perception questionnaires; ii) it handles uncertainty with regard to each student's predominant team role; iii) it is iterative since it considers information from several interactions in order to improve the estimation of role assignments. We tested the performance of the proposed tool in an experiment involving students that took part in three different team activities. The experiments suggest that the proposed tool is able to improve different teamwork aspects such as team dynamics and student satisfaction

Algoritmo genético para la generación automática de equipos de trabajo en entornos educativos

[ES] Es algo importante y necesario el trabajo en equipo en la sociedad moderna. Sin embargo, la creación de un equipo de trabajo no es tarea fácil para los humanos por la gran cantidad de posibilidades que se nos aparece. Este proyecto intenta solucionar el problema de crear equipos balanceados mediante el diseño, desarrollo e implantación de un algoritmo genético. Durante el mismo se llevarán a cabo simulaciones en el que se demuestra la viabilidad para conseguir resultados buenos para el mundo real en un tiempo muy reducido[EN] Teamworking is a special and important in this times. Unfortunally, the creation of a good team is not an easy task for humans, because the huge quantity of possibilities. In this project, we are going to fix this problem using genetic algorithms. During the same simulations will be carried out in which the viability is demonstrated to obtain good results for the real world in a very reduced timeChacón Martínez, MÁ. (2018). Algoritmo genético para la generación automática de equipos de trabajo en entornos educativos. http://hdl.handle.net/10251/110482TFG

PA1710-5-macet : Make-modelo adaptativo para la conformación de equipos de trabajo

El modelo adaptativo para la conformación de equipos de trabajo (MAKE), está basado en problemáticas relacionadas con la cohesión, composición, comunicación y el tiempo de conformación. Las competencias, conocimientos, personalidad y roles son las características definidas en MAKE. Se establecieron tres (3) sistemas: conformación, evaluación e incorporación. Se construyó un prototipo funcional como soporte a los sistemas planteados implementando dos (2) algoritmos: distancias y grafos. Se validó el modelo en el ámbito educativo y el sector productivo. El resultado de los procesos de conformación de equipos fue evaluado respecto a los requerimientos de cada organización. Finalmente, se presentan conclusiones y trabajo futuro.The adaptive model for the formation of work teams (MAKE), is based on problems related to cohesión, composition, communication and time of conformation. The competences, knowledge, personaiity and roles are the characteristics defined ¡n MAKE. Three (3) systems were established:conformation, evaluation and incorporation. A functional prototype was constructcd as support for the proposed systems implementing two (2) algorithms: distances and graphs. The model was validated in the educational field and the productive sector. The result of the processes of conformity of equipment was evaluated with respect to the requirements of each organizaron. Finally, conclusions and future work are presented. The adaptive model for the formation of work teams (MAKE), is based on problems related to cohesión, composition, communication and time of conformation. The competences, knowledge, personaiity and roles are the characteristics defined ¡n MAKE. Three (3) systems were established:conformation, evaluation and incorporation. A functional prototype was constructcd as support for the proposed systems implementing two (2) algorithms: distances and graphs. The model was validated in the educational field and the productive sector. The result of the processes of conformity of equipment was evaluated with respect to the requirements of each organizaron. Finally, conclusions and future work are presented.Magíster en Ingeniería de Sistemas y ComputaciónMaestrí

A Team Formation Tool for Educational Environments

PAAMS, the International Conference on Practical Applications of Agents and Multi-Agent Systems is an evolution of the International Workshop on Practical Applications of Agents and Multi-Agent Systems. PAAMS is an international yearly tribune to present, to discuss, and to disseminate the latest developments and the most important outcomes related to real-world applications. It provides a unique opportunity to bring multi-disciplinary experts, academics and practitioners together to exchange their experience in the development of Agents and Multi-Agent Systems. This volume presents the papers that have been accepted for the 2014 special sessions: Agents Behaviours and Artificial Markets (ABAM), Agents and Mobile Devices (AM), Bio-Inspired and Multi-Agents Systems: Applications to Languages (BioMAS), Multi-Agent Systems and Ambient Intelligence (MASMAI), Self-Explaining Agents (SEA), Web Mining and Recommender systems (WebMiRes) and Intelligent Educational Systems (SSIES)