Due to the COVID-19 pandemic, classes in schools acquired a hybrid learning model. Students took their classes both in person and, at other times, remotely. However, students are currently facing situations that they are not familiar with after a period of two years of confinement due to the COVID-19 pandemic. This emerging model of learning, to which the students had to adapt, not only impacted on their emotions during learning but also influenced their perceptions of their abilities and skills in being able to perform adequately in a situation of uncertainty, which also influenced the degree of academic engagement that they had. This study applied the structural equation modeling technique, using PLS-SEM software, to a sample of 194 students. The results show that their self-efficacy to act in a situation of vulnerability was affected, which is why their negative emotions increased and their positive emotions decreased. This in turn influenced the degree of engagement and effort they invested in developing a school activity.
Intelligent agents are computational entities which have elements that provide them with the ability to perceive and manipulate their environment: sensors and actuators. These are characterized by displaying various properties that adapt and achieve their objectives. Autonomy, learning, collaboration and reasoning are examples of these properties which together make them intelligent artificial entities. This article shows the development of a framework that has made it possible to speed-up the construction of a system of adaptive mobile intelligent agents, called SySAge. The system agents have integrated search and learning techniques for the execution of automated processes focused on solving search, classification and optimization problems. It has been found that through learning, the agents were able to estimate input parameters and apply them in estimating the shortest route in a graph, considering cost and penalty aspects. To determine the choice of search technique, a probabilistic selection was used. The autonomous behavior of each agent was appreciated through the various attempts to solve the search problem and not to focus the information acquired individually on a single agent.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.