Detecção da autoeficácia em ambientes computacionais de aprendizagem - Campos, Kelis Estatiane de (Universidade do Vale do Rio dos Sinos, 2025)
In recent years, there has been an increase in interest in studies focused on the area of Affective Computing, with emphasis on affective states, which comprise studies that relate emotion and learning. Emotions can influence learning both positively and negatively. Thus, this study is directed to the belief of Self-efficacy, which understands the individual's ability to produce certain levels of performance, which implies how the person feels, thinks, is motivated and behaves. Thus, in making use of the Computer Learning Environments (ACA) to assist in the learning process, studies have shown how emotional factors and the relationship between internal (psychic) and external (environmental) variables are fundamental in the teachinglearning process. Thus, the preliminary study carried out in the form of systematic mapping identified a gap pointing to the need for research aimed at detecting self-efficacy or other socioaffective phenomena, since the use of ACA currently presents a significant increase in the number of cases, as a reality present in the different teaching modalities. From this perspective, this study proposed a model for the diagnosis, instruction and monitoring of academic selfefficacy in Computer Learning Environments, in order to promote self-knowledge and contribute to better learning strategies of students. It is hypothesized that the realization of diagnosis, instruction on self-efficacy, as well as follow-up actions when using ACAs can encourage the modification of attitudes of students' studies in order to change the learning path, enabling the improvement of academic performance. Thus, the study included exploratory and descriptive research, based on quantitative and qualitative techniques. The proposed model, called the Self-efficacy Diagnosis, Prevention and Follow-up Model (MDPAA), involved three stages: diagnosis of the level of self-efficacy, from the creation of the student's self-efficacy form; Analysis of student behavior patterns using the Orange tool, and elaboration and evaluation of the Orientation Guide, instructional material created to instruct students about self-efficacy. Based on the results of the experiments performed, it is possible to obtain indicatives of applicability and effectiveness of the MDPAA, being evaluated that the model can enable more assertive choices in relation to the academic trajectory, in order to contribute to the favoring of the teaching-learning process and consequently contribute to the improvement of academic performance, promoting the reduction of dropout and dropout rates in education.
Keywords: Autoeficácia; Ambientes Computacionais de Aprendizagem; Estratégias de aprendizagem; Educação a Distância; Diagnóstico educacional; Self-efficacy; Computer Learning Environment; Learning strategies; Distance Education; Educational diagnosis; ACCNPQ::Ciências Exatas e da Terra::Ciência da Computação