Avaliando a Dimensão Afetiva para Apoio ao Processo de Aprendizagem na Disciplina de Algoritmos: um Estudo de Caso / Evaluating the Affective Dimension to Support the Learning Process in the Discipline of Algorithms: a Case Study
Keywords:
Computação Afetiva, Ensino de Algoritmos, Estado Afetivo de Frustração, Informática na Educação, Sistemas de Apoio à Aprendizagem.Abstract
Este artigo apresenta um estudo visando avaliar aspectos afetivos relacionados aos processos de aprendizagem na área de Algoritmos. A dificuldade apresentada pelos estudantes na aprendizagem de conceitos e técnicas de construção de algoritmos pode levar à frustração, um estado afetivo relacionado aos sentimentos de descontentamento e desesperança. Dois experimentos foram realizados como parte desta pesquisa. No primeiro, um grupo de 58 estudantes foi monitorado enquanto utilizava um sistema de aprendizagem de algoritmos. Quando sentiam-se frustrados na resolução dos exercícios propostos, os alunos podiam indicar este estado ao ambiente de aprendizagem por meio de um botão "Estou Frustrado". Após, um sistema de mineração de dados foi empregado para identificar quais os padrões de interação com o sistema poderiam estar relacionados ao estado de frustração. Estes padrões, representados na forma de regras, foram incorporados no sistema e empregados em um último experimento com um grupo de 6 estudantes com dificuldade de aprendizagem na disciplina. Os resultados da pesquisa mostraram que o sistema foi capaz de prover assistência personalizada aos alunos em momentos em que estes apresentavam dificuldades, auxiliando-os a melhorar seu desempenho.
Abstract
This paper presents a study on the evaluation of affective aspects related to learning processes in the area of Algorithms. Students' difficulties in designing solutions for algorithmic problems may lead to frustration, an affective state related to feelings of disappointment and discouragement. Two experiments were carried out as part of this research. In the first one, a group of 58 students was monitored while using a system for learning algorithms. Whenever the students felt frustrated while working on an algorithmic problem, they could indicate it by pressing a button with the label "I'm frustrated". Later on, a data mining tool was used to identify patterns of student-system interaction that could be related to the state of frustration. These patterns, represented in the form of rules, were then incorporated in the system and used in a last experiment with another group of 6 students who had learning difficulties in the course. Results showed that the system has been able to provide personalized assistance to the students at moments when they were showing difficulties, helping them to improve their performance.
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