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

Synergistic Team Composition

Effective teams are crucial for organisations, especially in environments that require teams to be constantly created and dismantled, such as software development, scientific experiments, crowd-sourcing, or the classroom. Key factors influencing team performance are competences and personality of team members. Hence, we present a computational model to compose proficient and congenial teams based on individuals' personalities and their competences to perform tasks of different nature. With this purpose, we extend Wilde's post-Jungian method for team composition, which solely employs individuals' personalities. The aim of this study is to create a model to partition agents into teams that are balanced in competences, personality and gender. Finally, we present some preliminary empirical results that we obtained when analysing student performance. Results show the benefits of a more informed team composition that exploits individuals' competences besides information about their personalities

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

Learning to collaboration: can integrated learning improve students perceptions and outcomes?

[EN] The introduction of the so-called Student Outcomes (SOs) in curricula is a main objective for the Universitat Politècnica de València (UPV). SOs are adaptive dimensions of students to the labour world and to lifelong learning. They are a complementary reference to academic marks, but they also grow to be academic skills in themselves. However, the integration of these SOs in the methodology and dynamic of traditional subjects and the obtaining of objective evidence of their achievement and results is a big challenge. UPV details in its Strategy Plan 2015-2020 the significance of these SOs, and integrates actions to promote initiatives to help meet the challenge, such as Educational Innovation and Improvement Projects (PIME). The present work explains an activity carried out within this framework. This contribution describes an experience carried out with the collaboration of lecturers of three different subjects taught in the third year of the degree of Tourism and double degree Tourism and Business Management: Catering Production Management, New Technologies Applied to Tourism, and Business English. The actions completed were designed to simultaneously develop and assess different SOs: Comprehension and integration (CT-01) , Team work and leadership (CT-06), and effective communication (CT-08). Project based learning methodologies were used. Different groups of students, created using the Belbin Team Role Method, developed a catering business project. For it, they used content learnt in the subject Catering Production Management, created a website (contents based on New Technologies Applied to Tourism), and presented their work in English (related to Business English). The contents, techniques and knowledge were developed in parallel in the three subjects and integrated in the project. Specific assessment actions were designed for each subject, the project was considered for the final mark of all three subjects. Thus, the greatest possible amount of synergies among the subjects was created. The final result of the project was presented both in written form and in oral form in English. The three lecturers jointly evaluated the works presented, considering knowledge, content and outcomes accomplished. Two surveys were used to measure the project; one half-way, to assess its development, and another at the end, to assess the results. Then, there was a comparison of individual results vs group results. Student feedback about the adequacy of the methodology, class dynamism and learning outcomes was satisfactory, as was collaboration between lecturers. As the main limiting factor of the project, we can mention the small size of the group, which did not allow a parallel investigation with experimental and control groups, and the difficulty to include part-time teachers of other subjects to the project due to their lack of availability, as well as the existing syllabus and class distribution, which do not allow much flexibility outside the usual activities.This work is supported by the projects PIME B-05/2017 & PIME B-22/2016 from UPV.Osorio Acosta, E.; Mestre-Mestre, EM.; Palomares Chust, A. (2019). Learning to collaboration: can integrated learning improve students perceptions and outcomes?. IATED. 1541-1549. https://doi.org/10.21125/inted.2019.0467S1541154

Group Formation Techniques in Computer-Supported Collaborative Learning: A Systematic Literature Review

Group formation is an essential process for group development lifecycle. It has been a growing concern to many researchers to be applied automatically in collaborative learning contexts. Forming a group is an atomic process that is affected by various factors. These factors differ depending on the group members characteristics, the context of the grouping process and the techniques used to form the group(s). This paper surveys the recently published work in group formation process providing a systematic literature review in which 30 relevant studies were analyzed. The findings of this review propose two taxonomies. The first one is for the attributes of group formation while the second is for the grouping techniques. Furthermore, we present the main findings and highlight the limitations of existing approaches in computer supported collaborative learning environment. We suggest some potential directions for future research with group formation process in both theoretical and practical aspects. In addition, We emphasize other improvements that may be inter-related with other computing areas such as cloud computing and mobility

Accountability and Project-based Learning

We offer a review of the publications concerning accountability of instructors and students for project-based learning (PBL) in an educational institution. At first, the PBL approach, its methodological justification, and the characteristics of the PBL environment that promotes taking accountability for learning are examined. Then, the publications are reviewed regarding their potential contribution to determination, creation, and development of accountability for PBL. Determination of accountability demonstrates its constructive role in improvement of teaching and learning. Creation of accountability is considered through collaborative knowledge building and using the comprehensive assessment of students’ learning while execution of study projects. Development of accountability for PBL is encouraged by PBL enhancement. It caused analysis of a computer-mediated adaptive support for PBL stimulating and facilitating collaborative knowledge building by students while learning by doing. The adaptive support provides adaptive formation of the collaborative groups, the adaptive assessment of the PBL to correspond with the progress of students’ knowledge, and adaptive management of a collaborative learning based on execution of the projects. Keywords: project-based learning, accountability, adaptive suppor

A game theoretical model for a collaborative e-learning platform on privacy awareness

De nos jours, avec l'utilisation croissante des technologies numériques, l'éducation à la préservation de la vie privée joue un rôle important en particulier pour les adolescents. Bien que plusieurs plateformes d'apprentissage en ligne à la sensibilisation à la vie privée aient été mises en œuvre, elles sont généralement basées sur des techniques traditionnelles d'apprentissage. Plus particulièrement, ces plateformes ne permettent pas aux étudiants de coopérer et de partager leurs connaissances afin d’améliorer leur apprentissage ensemble. En d'autres termes, elles manquent d'interactions élève-élève. Des recherches récentes sur les méthodes d'apprentissage montrent que la collaboration entre élèves peut entraîner de meilleurs résultats d'apprentissage par rapport à d'autres approches. De plus, le domaine de la vie privée étant fortement lié à la vie sociale des adolescents, il est préférable de fournir un environnement d'apprentissage collaboratif où l’on peut enseigner la préservation de la vie privée, et en même temps, permettre aux étudiants de partager leurs connaissances. Il serait souhaitable que ces derniers puissent interagir les uns avec les autres, résoudre des questionnaires en collaboration et discuter de problèmes et de situations de confidentialité. À cet effet, ce travail propose « Teens-online », une plateforme d'apprentissage en ligne collaborative pour la sensibilisation à la vie privée. Le programme d'études fourni dans cette plateforme est basé sur le Référentiel de formation des élèves à la protection des données personnelles. De plus, la plateforme proposée est équipée d'un mécanisme d'appariement de partenaires basé sur la théorie des jeux. Ce mécanisme garantit un appariement élève-élève stable en fonction des besoins de l'élève (comportement et / ou connaissances). Ainsi, des avantages mutuels seront obtenus en minimisant les chances de coopérer avec des pairs incompatibles. Les résultats expérimentaux montrent que l'utilité moyenne obtenue en appliquant l'algorithme proposé est beaucoup plus élevée que celle obtenue en utilisant d'autres mécanismes d'appariement. Les résultats suggèrent qu'en adoptant l'approche proposée, chaque élève peut être jumelé avec des partenaires optimaux, qui obtiennent également en retour des résultats d'apprentissage plus élevés.Nowadays, with the increasing use of digital technologies, especially for teenagers, privacy education plays an important role in their lives. While several e-learning platforms for privacy awareness training have been implemented, they are typically based on traditional learning techniques. In particular, these platforms do not allow students to cooperate and share knowledge with each other in order to achieve mutual benefits and improve learning outcomes. In other words, they lack student-student interaction. Recent research on learning methods shows that the collaboration among students can result in better learning outcomes compared to other learning approaches. Motivated by the above-mentioned facts, and since privacy domain is strongly linked to the social lives of teens, there is a pressing need for providing a collaborative learning platform for teaching privacy, and at the same time, allows students to share knowledge, interact with each other, solve quizzes collaboratively, and discuss privacy issues and situations. For this purpose, this work proposes “Teens-online”, a collaborative e-learning platform for privacy awareness. The curriculum provided in this platform is based on the Personal Data Protection Competency Framework for School Students. Moreover, the proposed platform is equipped with a partner-matching mechanism based on matching game theory. This mechanism guarantees a stable student-student matching according to a student's need (behavior and/or knowledge). Thus, mutual benefits will be attained by minimizing the chances of cooperating with incompatible students. Experimental results show that the average learning-related utility obtained by applying the proposed partner-matching algorithm is much higher than the average utility obtained using other matching mechanisms. The results also suggest that by adopting the proposed approach, each student can be paired with their optimal partners, which in turn helps them reach their highest learning outcomes