Captura e Visualização das Trajetórias de Aprendizagem como Ferramentas para a Análise do Comportamento dos Estudantes em um Ambiente Adaptativo Educacional

Abstract

Adaptive educational environments collect users data to adapt to their characteristics. One of these data represents the users' interaction with educational resources and it is related to the concept of learning trajectories. This work aims to use students' trajectories of a mini-course of algorithms and programming logic to analyze their navigational pattern. Thus, from the data of the students, their trajectories were captured and represented visually. The results show that students prefer to perform linear navigation based on concepts. However, it has not yet been possible to draw a relationship between the educational resources accessed by students and their performance.

Learning Trajectories Visualizations of Students Data on the Computational Thinking Context

“…Borba et al [de Borba et al 2016] analyses 25 different articles in the English language and tackles the several research questions, concluding that in general no visualizations of the learning trajectories are generated. Carmo, Gasparini and Oliveira [Carmo et al 2019] use learning trajectories to analyze students navigational patterns. Some works in the literature [Keim 2002, da Silva 2014, Nascimento and Ferreira 2005, Fouh et al 2012, Yi et al 2007] approach visualization and interaction while others approach learning trajectories [Rich et al 2017, Rich et al 2018, Ramos et al 2015, de Borba et al 2016, Carmo et al 2019] however, the visual aspects of learning trajectories are not approached in majority of papers [de Borba et al 2016].…”

“…Carmo, Gasparini and Oliveira [Carmo et al 2019] use learning trajectories to analyze students navigational patterns. Some works in the literature [Keim 2002, da Silva 2014, Nascimento and Ferreira 2005, Fouh et al 2012, Yi et al 2007] approach visualization and interaction while others approach learning trajectories [Rich et al 2017, Rich et al 2018, Ramos et al 2015, de Borba et al 2016, Carmo et al 2019] however, the visual aspects of learning trajectories are not approached in majority of papers [de Borba et al 2016]. This work aims to fill such gap and differs from the related papers in the visual approach of the learning trajectories, linking data collected from CT teaching into newly generated visualizations of learning trajectories.…”

Avaliação do Comportamento de Estudantes em um Ambiente Educacional Ubíquo

“…O trabalho [do Carmo et al 2019], por exemplo, mostra uma estratégia baseada em mineração de padrões sequenciais para encontrar padrões de comportamento nos registros de logs de um sistema Adaptativo na Web. Com essa análise, os autores descobriram padrões de comportamentos semelhantes em turmas diferentes, os recursos mais utilizados, os tipos de caminhos de navegação mais preferidos e porcentagem de acesso aos recursos.…”