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 

Intelligent Approaches to identify Student Learning Styles through Emotions in a Classroom

Enfoques Inteligentes para Identificar Estilos de Aprendizaje de los estudiantes mediante las Emociones en un salón de clases
Authors: J Cordero, J Aguilar, K Aguilar

Abstract:

In this article, a hierarchical pattern is proposed to identify the learning style of students in a classroom, which is composed of two levels, one to recognize the emotional state, and another to identify the learning style. Each level is defined by different types of descriptors, which are perceived from a multimodal approach. In addition, using the hierarchical pattern, two approaches are analyzed to model the learning style, which can be visual, auditory, reading/writing, kinesthetic. One of the approaches is based on fuzzy logic, and another based on chronicles, which exploit the idea of recursion and learning in the recognition process. Finally, considering the dynamic environment of a classroom, the approaches are compared, in terms of their abilities to learn and to determine the learning styles; and to communicate that information clearly in a smart classroom, based on the recognized emotions.

Keywords: Hierarchical patterns, learning style, emotion recognition, fuzzy logic, chronicles, dynamic pattern recognition.

Link: https://www.proquest.com/openview/6cd1e500aef16382c3c439403a9a06c1/1?pq-origsite=gscholar&cbl=1006393

Monitoring for the Evaluation Process On-Line Prototype Based on OpenFace Algorithm

 Authors: O Ruiz-Vivanco, A Gonzalez-Eras, J Cordero, L Barba-Guaman

Abstract:
This project was designed to present a prototype of facial authentication system that allows for the recognition of students who are using an online platform to take final evaluations in our Distance Learning Program, with the purpose of detecting fraud and identity theft. It uses the OpenFace algorithm based on neural networks, taking input from two-dimensional images of the student from time to time during the participation on the exam. We present a system for face recognition using an image database of faces in classroom setting to demonstrate the improvement using this OpenFace algorithm for the preprocessing approach. The preliminary results indicate a high accuracy in the recognition of students, in terms of brightness, size and quality of the image of the face.

Keywords: Computational intelligence, Computer vision, Face authentication, Neural networks, OpenFace algorithm

Link: https://link.springer.com/chapter/10.1007/978-3-030-05532-5_37 

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