Ariel Monteserin

Automatic detection of learning styles: state of the art

A learning style describes the attitudes and behaviors, which determine an individual’s preferred way of learning. Learning styles are particularly important in educational settings since they may help students and tutors become more self-aware of their strengths and weaknesses as learners. The traditional way to identify learning styles is using a test or questionnaire. Despite being reliable, these instruments present some problems that hinder the learning style identification. Some of these problems include students’ lack of motivation to fill out a questionnaire and lack of self-awareness of their learning preferences. Thus, over the last years, several approaches have been proposed for automatically detecting learning styles, which aim to solve these problems. In this work, we review and analyze current trends in the field of automatic detection of learning styles. We present the results of our analysis and discuss some limitations, implications and research gaps that can be helpful to researchers working in the field of learning styles.

Argumentation-based negotiation planning for autonomous agents

When we negotiate, the arguments uttered to persuade the opponent are not the result of an isolated analysis, but of an integral view of the problem that we want to agree about. Before the negotiation starts, we have in mind what arguments we can utter, what opponent we can persuade, which negotiation can finish successfully and which cannot. Thus, we plan the negotiation, and in particular, the argumentation. This fact allows us to take decisions in advance and to start the negotiation more confidently. With this in mind, we claim that this planning can be exploited by an autonomous agent. Agents plan the actions that they should execute to achieve their goals. In these plans, some actions are under the agents control, while some others are not. The latter must be negotiated with other agents. Negotiation is usually carried out during the plan execution. In our opinion, however, negotiation can be considered during the planning stage, as in real life. In this paper, we present a novel approach to integrate argumentation-based negotiation planning into the general planning process of an autonomous agent. This integration allows the agent to take key decisions in advance. We evaluated this proposal in a multiagent scenario by comparing the performance of agents that plan the argumentation and agents that do not. These evaluations demonstrated that performance improves when the argumentation is planned, specially, when the negotiation alternatives increase.

Assisting students with argumentation plans when solving problems in CSCL

In CSCL systems, students who are solving problems in group have to negotiate with each other by exchanging proposals and arguments in order to resolve the conflicts and generate a shared solution. In this context, argument construction assistance is necessary to facilitate reaching to a consensus. This assistance is usually provided with isolated arguments by demand, but this does not offer students a real and integral view of the conflicts. In this work, we study the utilisation of argumentation plans to assist a student during the argumentation. The actions of an argumentation plan represent the arguments that a student might use during the argumentation process. Moreover, these plans can be integrated with the tasks needed to reach a shared solution. These plans give the student an integral and intuitive view of the problem resolution and the conflict that must be resolved. We evaluated our proposal with students of an Artificial Intelligence course. This evaluation was carried out by comparing three different assistance scenarios in which students had to solve exercises: no assistance, assistance with isolated arguments, and assistance with argumentation plans. The results obtained show that reaching consensus was easier for the students when the assistance was provided using argumentations plans.

A reinforcement learning approach to improve the argument selection effectiveness in argumentation-based negotiation

Highlights? We present an approach to improve the argument selection. ? We identify each element of the RL model in the context of the argument selection. ? Our approach allows the agent to improve the argument effectiveness as the agents experience increases. ? We tested this approach in a multiagent system, in a stationary as well as in a dynamic environment. Deciding what argument to utter during a negotiation is a key part of the strategy to reach an expected agreement. An agent, which is arguing during a negotiation, must decide what arguments are the best to persuade the opponent. In fact, in each negotiation step, the agent must select an argument from a set of candidate arguments by applying some selection policy. By following this policy, the agent observes some factors of the negotiation context (for instance, trust in the opponent and expected utility of the negotiated agreement). Usually, argument selection policies are defined statically. However, as the negotiation context varies from a negotiation to another, defining a static selection policy is not useful. Therefore, the agent should modify its selection policy in order to adapt it to the different negotiation contexts as the agent gains experience. In this paper, we present a reinforcement learning approach that allows the agent to improve the argument selection effectiveness by updating the argument selection policy. To carry out this goal, the argument selection mechanism is represented as a reinforcement learning model. We tested this approach in a multiagent system, in a stationary as well as in a dynamic environment. We obtained promising results in both.

Whom should I persuade during a negotiation? An approach based on social influence maximization

Abstract During a negotiation, an agent must make several key decisions in order to achieve a profitable agreement. When the negotiation is carried out in a social context, agents can use persuasion, besides the traditional exchange of concessions. To carry out the persuasion and make concessions, the agents must employ resources that are usually scarce. For this reason, the agents should carefully decide which opponent they should persuade to maximise their profit, especially when the negotiation involves multiple parties. To make this decision, we propose that the agents should persuade the opponents with a high influence on the other agents involved in the negotiation. Therefore, we represent a negotiation context as a social influence maximization problem and solve it under a model that learns how influence flows in a network by analyzing historical information. This allows an agent to determine what opponents exert the highest influence. Finally, the agent uses this information to decide which opponent to persuade during the negotiation. Experimental results showed that the agreement rate increased when agents applied this approach.

Análisis de la formación de grupos en Aprendizaje Colaborativo Soportado por Computadoras

Este artigo discute a colaboracao entre estudantes na internet e a troca de conhecimento por meio desse canal de comunicacao. Propoe uma metodologia que visa potencializar as relacoes entre os usuarios de uma rede educacional situada num contexto especifico. Tal metodo tem como fundamento as teorias do contexto e a analise de redes sociais (ARS), para promover um mapeamento das redes estudadas.Refletir sobre curriculo escolar formal e comunidades de aprendizagem como metafora das TIC dinamizam os caminhos empiricos, construcoes criticas e aprofundamento da difusao do conhecimento como parte do processo de humanizacao/ tecnologizacao do homem. Oriundos de processos e movimentos contemporâneos, a consolidacao de ambientes computacionais nas escolas potencializa a construcao do conhecimento e a socializacao de praticas pedagogicas inovadoras. Esta investigacao assume as situacoes especificas curriculares, procura descobrir o que existe de mais essencial e caracteristico, partindo do conhecimento de curriculo e suas bases teoricas tradicionais, para a construcao da discussao sobre um curriculo em rede associada a instrumentalizacao das comunidades de aprendizagem.O sistema Moodle constitui-se atualmente numa das mais importantes ferramentas de apoio a cursos na Web. Apesar disto, seu modelo apresenta algumas deficiencias para uma estruturacao hierarquica e compartilhamento de materiais digitais entre disciplinas e turmas do seu ambiente virtual. Este artigo apresenta um estudo sobre a arquitetura central do Moodle, propondo a definicao de um novo nucleo, visando o aprimoramento destas caracteristicas.Estudo descritivo, qualitativo, com estudantes da 3a serie de Graduacao em Enfermagem de uma Universidade Publica de Sao Paulo, SP. Os participantes construiram Mapas Conceituais, por meio do software Cmap Tools®. Os dados foram coletados em um Grupo Focal e todos os sujeitos indicaram que o uso do software facilita e garante a organizacao, visualizacao e correlacao dos dados, porem houve dificuldades iniciais relacionadas ao manejo das ferramentas. Conclui-se, que o software Cmap Tools® favoreceu a construcao dos MC por seus recursos de formatacao, porem estrategias de orientacao deveriam ser implantadas. Como resultado, desenvolveu-se um manual para o uso do software Cmap Tool® em video Podcasting.Ha poucas iniciativas com respeito aos ambientes de virtuais para a divulgacao de materiais curriculares sobre modelagem matematica. Esses ambientes oferecem acesso as praticas pedagogicas em modelagem. Este trabalho apresenta um sistema Web para hospedar atividades de modelagem e materiais multimidia para descrever o desenvolvimento do ambiente de modelagem em sala de aula e apoiar outros professores na implementacao em suas praticas pedagogicas.A composicao e sequenciamento de Objetos de Aprendizagem sao discutidas neste trabalho a partir da representacao da estrutura conceitual de um dominio em termos das suas relacoes de dependencia. A composicao de Objetos de Aprendizagem e modelada a partir da estrutura narrativa de um discurso considerando-se os aspetos formais dos planos do conteudo e de expressao. O aspecto formal do conteudo da composicao e dado pelas pelos conceitos e seus relacionamentos e forma da expressao corresponde aos tipos de signos definidos pelo LOM. A estrutura da composicao obtida independe do tipo de midia utilizado e o modelo adequa-se as propostas de composicao adaptativas tanto do ponto de vista do meio como das disponibilidades de conexao.

Building user argumentative models

Knowing how a user builds his/her arguments during a discussion gives useful advantages if we want to assist the user or analyse his/her argumentative skills. This paper presents a novel mechanism to build user argumentative models, which captures the argumentative style to generate arguments. To this end, we observe how users generate arguments, and apply a generalised association rules algorithm to discover rules for argument generation. These rules depict the argumentative style of the user. They are composed of an antecedent, which represents the conditions to build an argument, and a consequent, which represents such argument. To evaluate this proposal, we show results obtained in the domain of meeting scheduling. We discovered interesting rules from a group of users discussing in that domain, and checked that about 60% of the arguments that users had generated in a test situation can be also generated from the rules previously learnt, at least partially. Finally, although this work focuses on modelling users’ argumentative style, we discuss how this promising approach could be applied in different knowledge domains.

PUMAS-GR: A Negotiation-Based Group Recommendation System for Movies

Providing recommendations to groups of users has become popular in many applications today. Although several group recommendation techniques exist, the generation of items that satisfy all group members in an even way still remains a challenge. To this end, we have developed a multi-agent approach called PUMAS-GR that relies on negotiation techniques to improve group recommendations. We applied PUMAS-GR to the movies domain, and used the monotonic concession protocol to reach a consensus on the movies proposed to a group.

A MAS Approach for Group Recommendation Based on Negotiation Techniques

Providing recommendation to groups of users has become a promising research area, since many items tend to be consumed by groups of people. Various techniques have been developed aiming at making recommendations to the group as a whole, but satisfying all group members in an even way still remains as a challenge. We propose a multi-agent approach based on negotiation techniques for group recommendation. In this approach we use the multilateral monotonic concession protocol to combine individual recommendations into a group recommendation. We applied our proposal in the movies domain. The results obtained indicate that using this negotiation protocol, users in the groups were more evenly satisfied than with traditional ranking aggregation approaches.

Influence-based approach to market basket analysis

Abstract In this article, we propose an approach to market basket analysis based on the notion of social influence. While traditional market basket analysis looks for combinations of products that frequently co-occur in transactions, we seek to find a set of influential products that, if bought by a customer, will increase the sales volume of the shop. We believe that customers who purchase influential products would also be influenced to purchase other products. We validated our approach with two real-world datasets collected from online shoppings and one dataset collected from a supermarket concluding that influential products identified by our approach increase the influence spread with respect to different baselines: best-selling, highest centrality, frequent sequence initiator, and most promoted products.