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.

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Providing recommendations 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 a group as a whole. Most works use aggregation techniques to combine preferences, recommendations or proles. However, satisfying all group members in an even way still remains as a challenge. To deal with this problem, we propose an extension of a multi-agent approach based on negotiation techniques for group recommendation. In the approach, we use the multilateral Monotonic Concession Protocol (MCP) to combine individual recommendations into a group recommendation.In this work, we extend the MCP protocol to allow users to personalize the behavior of the agents. This extension was evaluated in two dierent domains (movies and points of interest) with satisfactory results. We compared our approach against dierent baselines, namely: a preference aggregation algorithm, a recommendation aggregation algorithm, and a simple one-step negotiation.The results show evidence that, when using our negotiation approach, users in the groups are more uniformly satised than with traditional aggregation approaches.