Approaches to identify student learning styles through emotions in a classroom

Sistema de Gestión de las emociones en un salón de clases inteligente basado en Modelos de confianza y reputación

Authors: J Cordero, J Aguilar, K Aguilar, M Martinez

Abstract:

In this article, we propose a model of trust and reputation, for the management of emotions in a Smart Classroom (SaCI). In general, a SaCI can model its different hardware and software components as agents. On the other hand, in intelligent environments modeled with agents, it is necessary to define the interaction mechanisms used in conversations between agents. In addition, the conversations should be enriched with models of trust and reputation of the agents involved in them, so that each agent can decide what information coming from the different agents, to consider when executing their decision-making processes. In particular, this paper defines models of trust and reputation for the SaCI agents. These models are used in a conversation that performs the management of emotions in SaCI, which allows to define the emotion that prevails at a given moment in it, which we call social emotion. The conversation is tested in several cases of study, in order to validate its ability to establish the social emotion of SaCI. The experimental results with the emotion management system based on models of trust and reputation, demonstrate that it is able to determine the social emotion in SaCI regardless of the characteristics of its context and learning from the behavior of agents, which gives it great robustness.

Keywords: trust model, reputation model, emotion management, social emotion, smart classroom, multiagent systems.

Link: https://www.proquest.com/openview/f74b61fb8680f9b19d07dd2417468366/1?pq-origsite=gscholar&cbl=1006393 

Specification of the Autonomic Cycles of Learning Analytic Tasks for a Smart Classroom

Authors: Jose Aguilar, Jorge Cordero and Omar Buendía

Abstract:
In this article, we propose the concept of ‘‘Autonomic Cycle Of Learning Analysis Tasks’’ (ACOLAT), which defines a set of tasks of learning analysis, whose objective is to improve the learning process. The data analysis has become a fundamental area for the knowledge discovery from data extracted from different sources. In the autonomic cycle, each learning analysis task interacts with each other and has different roles: Some of them must observe the learning process, others must analyze and interpret what happens in it, and finally, others make decisions in order to improve the learning process. In this article, we study the application of the autonomic cycle in a smart classroom, which is composed of a set of intelligent components of hardware (e.g., smart board) and software (e.g., virtual learning environments), which must exploit the knowledge generated by the ACOLAT to improve the learning process in the smart classroom. Moreover, we present the set of ACOLATs present in a smart classroom and the implementation of some of them.

Keywords: Learning Analytics, Smart Classroom, Autonomic Computing, Learning Environments, Knowledge Discovery

Link: http://journals.sagepub.com/doi/abs/10.1177/0735633117727698

Competences as Services in the Autonomic Cycles of Learning Analytic Tasks for a Smart Classroom

Authors: Alexandra González-Eras, Omar Buendia, Jose Aguilar, Jorge Cordero and Taniana Rodriguez

Abstract:
Learning Analytic is a useful tool in the context of the learning process, in order to improve the educational environment. In previous works, we have proposed autonomic cycles of learning Analytic tasks, in order to improve the learning process in smart classrooms. One aspect to be considered by the autonomic cycles is their adaptability to the formation of competences, assuming that a student has competences that must be strengthened during the learning process. In this paper, we propose the utilization of competences to guide the adaptation process of a learning environment. Particularly, we propose the extensions of the autonomic cycles for smart classrooms, using the idea of competences. In this case, we define the competences as a service, to help the autonomic cycles in their processes of adaptation.

Keywords: Learning analytics, Smart classroom, Educational competences, Autonomic cycles

Link: https://link.springer.com/chapter/10.1007/978-3-319-67283-0_16

A general framework for learning analytic in a smart classroom

Authors: Jose Aguilar, Priscila Valdiviezo, Jorge Cordero, Guido Riofrio, and Eduardo Encalada

Abstract. In this paper, we propose the utilization of the “Learning Analytics” paradigm in a Smart Classroom, a classroom that integrates artificial intelligence technology on the educational process. Learning Analytics can extract knowledge from the Smart Classroom platform, to better understand students and his/her learning processes. In this way, a Smart Classroom can understand and optimize
the learning process and the teaching environments proposed. The smart classroom can adapt its components to improve students’ performance, among other aspects. Particularly, this paper proposes a framework about how the Learning Analytics paradigm can be used in a Smart Classroom, in order to provide knowledge about the activities taking place within it. The framework is defined like a closed cycle of Learning Analytics tasks, which generate metrics used like feedback to optimize the pedagogical model proposed by the smart Classroom. The metrics evaluate the learning process and pedagogical practice provided by the smart Classroom. So, our main contribution is about how the Learning Analytics paradigm can be used in a Smart Classroom in order to improve the students’
performance.

Keywords: Learning analytics, Smart classroom,  Ambient intelligence, Data mining

Link: http://link.springer.com/chapter/10.1007/978-3-319-48024-4_17

DOI: 10.1007/978-3-319-48024-4_17

Conceptual Design of a Smart Classroom Based on Multiagent Systems

Authors: Jose Aguilar, Priscila Valdiviezo, Jorge Cordero and Manuel Sánchez

Abstract - The smart environments have been used in different domains: home, educational and health centers, etc. Particularly, a smart environment in education must integrate different aspects linked to virtual and presencial education, the profile of the students, to the pedagogical paradigm used, etc., in real time. In this paper we characterize a smart classroom considering these aspects, using the multiagent systems paradigm. Particularly, we define the different components of a smart classroom with their properties. Based on that, we describe these components like agents using MASINA, a methodology to specify multiagent systems. We define two frameworks of agents which describe the different types of components in a smart classroom (of software and of hardware), and give examples of applications of these two frameworks in a device and a software of a smart classroom. Finally, we show an example of conversation in a smart classroom based on our multiagents approach, specifically in a work session.

Keywords: Smart Classroom, Multiagent System, AmI, Middleware

Proceedings on the International Conference on Artificial Intelligence (ICAI): 471-477. Athens: The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp). (2015)

Link: http://search.proquest.com/openview/930c8d0a31e0faf9bbd8a2a44137e85a/1?pq-origsite=gscholar