Collaborative Learning Team Formation Considering Team Roles: An Evolutionary Approach based on Adaptive Crossover, Mutation and Simulated Annealing

In this paper, a hybrid evolutionary algorithm is proposed to solve a collaborative learning team formation problem in higher education contexts. This problem involves a grouping criterion evaluated satisfactorily in a great variety of higher education courses as well as training programs. This criterion is based on the team roles of students, and implies forming well-balanced teams respecting the team roles of their members. The hybrid evolutionary algorithm uses adaptive crossover, mutation and simulated annealing processes, in order to improve the performance of the evolutionary search. These processes adapt their behavior regarding the state of the evolutionary search. The performance of the hybrid evolutionary algorithm is exhaustively evaluated on data sets with very different complexity levels, and after that, is compared with those of the algorithms previously reported in the literature to solve the addressed problem. The results obtained from the performance comparison indicate that the hybrid evolutionary algorithm significantly outperforms the algorithms previously reported, in both effectiveness and efficiency.Fil: Yannibelli, Virginia Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; ArgentinaFil: Amandi, Analia Adriana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Instituto de Sistemas Tandil; Argentin

An Automatic Group Formation Method to Foster Innovation in Collaborative Learning at Workplace

Despite group formation in learning environments is commonly and successfully approached, there is a gap in the research literature with respect to its application in corporative learning. Regarding that creativity is as an important factor to increase innovation in companies, in the present research, we propose a group formation method, considering preferred roles and functional diversity, aiming to improve creativity in collaborative learning at workplace. We employed Tabu Search algorithm to automatically form groups based on Nonaka\u27s knowledge creation theory and preferred roles from Belbin’s model. We performed a case study to compare the quality of socio-cognitive interactions duringcollaborative learning in groups formed by the proposed method and randomly formed groups. The results show that groups formed by preferred roles and functional diversity are more creative and present enhanced fluency and more elaborated products in comparison to randomly formed groups

Group Formation Using Multi Objectives Ant Colony System for Collaborative Learning

Collaborative learning is widely applied in education. One of the key aspects of collaborative learning is group formation. A challenge in group formation is to determine appropriate attributes and attribute types to gain good group results. This paper studies the use of an improved ant colony system (ACS), called Multi Objective Ant Colony System (MOACS), for group formation. Unlike ACS that transforms all attribute values into a single value, thus making any attributes are not optimally worth, MOACS tries to gain optimal values of all attributes simultaneously. MOACS is designed for various combinations of attributes and can be used for homogeneous, heterogeneous or mixed attributes. In this paper, sensing/intuitive learning styles (LSSI) and interests in subjects (I) are used in homogeneous group formation, while active/reflective learning style (LSAR) and previous knowledge (KL) are used for heterogeneous or mixed group formation. Experiments were conducted for measuring the average goodness of attributes (avgGA) and standard deviation of goodness of attributes (stdGA). The objectives of MOACS for homogeneous attributes were minimum avgGA and stdGA, while those for heterogeneous attributes were maximum avgGA and minimum stdGA. As a conclusion, MOACS was appropriate for group formation with homogeneous or mixed

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

Complex negotiations in multi-agent systems

Los sistemas multi-agente (SMA) son sistemas distribuidos donde entidades autónomas llamadas agentes, ya sean humanos o software, persiguen sus propios objetivos. El paradigma de SMA ha sido propuesto como la aproximación de modelo apropiada para aplicaciones como el comercio electrónico, los sistemas multi-robot, aplicaciones de seguridad, etc. En la comunidad de SMA, la visión de sistemas multi-agente abiertos, donde agentes heterogéneos pueden entrar y salir del sistema dinámicamente, ha cobrado fuerza como paradigma de modelado debido a su relación conceptual con tecnologías como la Web, la computación grid, y las organizaciones virtuales. Debido a la heterogeneidad de los agentes, y al hecho de dirigirse por sus propios objetivos, el conflicto es un fenómeno candidato a aparecer en los sistemas multi-agente. En los últimos años, el término tecnologías del acuerdo ha sido usado para referirse a todos aquellos mecanismos que, directa o indirectamente, promueven la resolución de conflictos en sistemas computacionales como los sistemas multi-agente. Entre las tecnologías del acuerdo, la negociación automática ha sido propuesta como uno de los mecanismos clave en la resolución de conflictos debido a su uso análogo en la resolución de conflictos entre humanos. La negociación automática consiste en el intercambio automático de propuestas llevado a cabo por agentes software en nombre de sus usuarios. El objetivo final es conseguir un acuerdo con todas las partes involucradas. Pese a haber sido estudiada por la Inteligencia Artificial durante años, distintos problemas todavía no han sido resueltos por la comunidad científica todavía. El principal objetivo de esta tesis es proponer modelos de negociación para escenarios complejos donde la complejidad deriva de (1) las limitaciones computacionales o (ii) la necesidad de representar las preferencias de múltiples individuos. En la primera parte de esta tesis proponemos un modelo de negociación bilateral para el problema deSánchez Anguix, V. (2013). Complex negotiations in multi-agent systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/21570Palanci

From examples to knowledge in model-driven engineering : a holistic and pragmatic approach

Le Model-Driven Engineering (MDE) est une approche de développement logiciel qui propose d’élever le niveau d’abstraction des langages afin de déplacer l’effort de conception et de compréhension depuis le point de vue des programmeurs vers celui des décideurs du logiciel. Cependant, la manipulation de ces représentations abstraites, ou modèles, est devenue tellement complexe que les moyens traditionnels ne suffisent plus à automatiser les différentes tâches. De son côté, le Search-Based Software Engineering (SBSE) propose de reformuler l’automatisation des tâches du MDE comme des problèmes d’optimisation. Une fois reformulé, la résolution du problème sera effectuée par des algorithmes métaheuristiques. Face à la pléthore d’études sur le sujet, le pouvoir d’automatisation du SBSE n’est plus à démontrer. C’est en s’appuyant sur ce constat que la communauté du Example-Based MDE (EBMDE) a commencé à utiliser des exemples d’application pour alimenter la reformulation SBSE du problème d’apprentissage de tâche MDE. Dans ce contexte, la concordance de la sortie des solutions avec les exemples devient un baromètre efficace pour évaluer l’aptitude d’une solution à résoudre une tâche. Cette mesure a prouvé être un objectif sémantique de choix pour guider la recherche métaheuristique de solutions. Cependant, s’il est communément admis que la représentativité des exemples a un impact sur la généralisabilité des solutions, l'étude de cet impact souffre d’un manque de considération flagrant. Dans cette thèse, nous proposons une formulation globale du processus d'apprentissage dans un contexte MDE incluant une méthodologie complète pour caractériser et évaluer la relation qui existe entre la généralisabilité des solutions et deux propriétés importantes des exemples, leur taille et leur couverture. Nous effectuons l’analyse empirique de ces deux propriétés et nous proposons un plan détaillé pour une analyse plus approfondie du concept de représentativité, ou d’autres représentativités.Model-Driven Engineering (MDE) is a software development approach that proposes to raise the level of abstraction of languages in order to shift the design and understanding effort from a programmer point of view to the one of decision makers. However, the manipulation of these abstract representations, or models, has become so complex that traditional techniques are not enough to automate its inherent tasks. For its part, the Search-Based Software Engineering (SBSE) proposes to reformulate the automation of MDE tasks as optimization problems. Once reformulated, the problem will be solved by metaheuristic algorithms. With a plethora of studies on the subject, the power of automation of SBSE has been well established. Based on this observation, the Example-Based MDE community (EB-MDE) started using application examples to feed the reformulation into SBSE of the MDE task learning problem. In this context, the concordance of the output of the solutions with the examples becomes an effective barometer for evaluating the ability of a solution to solve a task. This measure has proved to be a semantic goal of choice to guide the metaheuristic search for solutions. However, while it is commonly accepted that the representativeness of the examples has an impact on the generalizability of the solutions, the study of this impact suffers from a flagrant lack of consideration. In this thesis, we propose a thorough formulation of the learning process in an MDE context including a complete methodology to characterize and evaluate the relation that exists between two important properties of the examples, their size and coverage, and the generalizability of the solutions. We perform an empirical analysis, and propose a detailed plan for further investigation of the concept of representativeness, or of other representativities

Formación automática de grupos colaborativos considerando estilos de aprendizaje y rendimiento académico

El Aprendizaje Colaborativo (AC) se vincula con métodos de enseñanza y de aprendizaje donde los estudiantes trabajan en pequeños grupos para resolver una consigna común. El avance tecnológico que se ha producido en las últimas décadas permitió al AC adoptar herramientas computacionales que facilitan la colaboración, la coordinación y la comunicación transformándolo en Aprendizaje Colaborativo Soportado por Computadora (ACSC). Crear grupos e instar a sus miembros a resolver una consigna de manera colaborativa, no garantiza en forma alguna que el comportamiento y el rendimiento de esos grupos sean adecuados, ni que la experiencia de enseñanza y de aprendizaje sea exitosa. Una de las variables que influyen en los resultados es la constitución de los grupos. Es por esto que nuestra línea de investigación se enfoca en la formación automática de grupos colaborativos on-line. En particular, se propone una herramienta software que crea grupos de estudiantes teniendo en cuenta sus estilos de aprendizaje, combinándolos de tal forma que se incrementen o maximicen sus rendimientos académicos. En este artículo se describen los objetivos de la investigación, el enfoque propuesto para concretar la formación automática de grupos colaborativos, y algunos antecedentes relevantes.Eje: Tecnología Informática Aplicada en Educación.Red de Universidades con Carreras en Informática (RedUNCI

Evolutionary Decomposition of Complex Design Spaces

This dissertation investigates the support of conceptual engineering design through the decomposition of multi-dimensional search spaces into regions of high performance. Such decomposition helps the designer identify optimal design directions by the elimination of infeasible or undesirable regions within the search space. Moreover, high levels of interaction between the designer and the model increases overall domain knowledge and significantly reduces uncertainty relating to the design task at hand. The aim of the research is to develop the archetypal Cluster Oriented Genetic Algorithm (COGA) which achieves search space decomposition by using variable mutation (vmCOGA) to promote diverse search and an Adaptive Filter (AF) to extract solutions of high performance [Parmee 1996a, 1996b]. Since COGAs are primarily used to decompose design domains of unknown nature within a real-time environment, the elimination of apriori knowledge, speed and robustness are paramount. Furthermore COGA should promote the in-depth exploration of the entire search space, sampling all optima and the surrounding areas. Finally any proposed system should allow for trouble free integration within a Graphical User Interface environment. The replacement of the variable mutation strategy with a number of algorithms which increase search space sampling are investigated. Utility is then increased by incorporating a control mechanism that maintains optimal performance by adapting each algorithm throughout search by means of a feedback measure based upon population convergence. Robustness is greatly improved by modifying the Adaptive Filter through the introduction of a process that ensures more accurate modelling of the evolving population. The performance of each prospective algorithm is assessed upon a suite of two-dimensional test functions using a set of novel performance metrics. A six dimensional test function is also developed where the areas of high performance are explicitly known, thus allowing for evaluation under conditions of increased dimensionality. Further complexity is introduced by two real world models described by both continuous and discrete parameters. These relate to the design of conceptual airframes and cooling hole geometries within a gas turbine. Results are promising and indicate significant improvement over the vmCOGA in terms of all desired criteria. This further supports the utilisation of COGA as a decision support tool during the conceptual phase of design.British Aerospace plc, Warton and Rolls Royce plc, Filto

Adapting Collaborative Learning Tools to Support Group Peer Mentorship

Group peer mentorship is a relatively new addition to the area of collaborative learning. We see an untapped potential in supporting this model of mentorship with the existing collaborative learning tools like peer review and wiki. Therefore, we proposed to use a modified peer review system and a modified wiki system. From our preliminary studies using both peer review and wiki systems, we found that participants preferred the peer-review system to the wiki system in supporting them for mentorship. Therefore, this dissertation specifically addresses how to adapt the peer review system to support group peer mentorship. We proposed a modified peer review system, which comprises seven stages – initial submission of the first draft of the paper by the author, the review of author’s paper by peer reviewers, release of review feedback to the author, back-evaluation of their reviews by the authors, modification of the paper by the author, submission of the final paper and the final stage where both authors and reviewers provide an evaluation of the peer review process with respect to their learning, their perception of the helpfulness of the process, and their satisfaction with the process. We also proposed to use our group matching algorithm, based on some constraints and the principles of the Hungarian algorithm, to achieve a diversified grouping of peers for each peer review session. With these, we conducted six peer review studies with the graduate and undergraduate students at the University of Saskatchewan and teachers in Chile. This dissertation reports on the findings from these studies. We found that peer review, with some modifications, is a good tool to facilitate group peer mentorship. An evaluation of the performance of our group matching algorithm showed an improvement over three other algorithms, with respect to three metrics – knowledge gain of peers, time and space consumption of the algorithm. Finally, this dissertation also shows that wiki has the potential to support group peer mentorship, but needs further research