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Master's Dissertation
DOI
https://doi.org/10.11606/D.76.2023.tde-06072023-094213
Document
Author
Full name
Robson Douglas da Silva Martins
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 2023
Supervisor
Committee
Muniz, Sérgio Ricardo (President)
Haar, Ewout ter
Oliveira, Vilma Alves de
Title in Portuguese
Learning Analytics no apoio, planejamento e avaliação de metodologias ativas no Ensino de Física
Keywords in Portuguese
Data science na educação
Ensino de física
Learning analytics
Metodologias ativas
Abstract in Portuguese
Neste trabalho, foi desenvolvida uma análise exploratória de dados relativos às atividades e o desempenho de estudantes do curso de física básica matriculados em cursos de bacharelado do campus da USP de São Carlos. O objetivo da análise foi identificar padrões de correlação associados à aprovação na disciplina que possam auxiliar docentes nas tomadas de decisão em relação ao desenvolvimento da disciplina e no processo de retomada e/ou recuperação dos conteúdos. Foram analisados dados de nove turmas, com um total de 565 estudantes, coletados usando um ambiente virtual de aprendizagem (AVA) e analisados (de forma anonimizada) através de scripts em linguagem Python, com auxílio de ferramentas e técnicas matemáticas, computacionais e estatísticas que formam a base das técnicas utilizadas em Learning Analytics e Data Science aplicados a educação. Foram encontradas correlações positivas entre as notas de provas e aprovação na disciplina bem como poucas correlações entre a presença nas aulas e a nota final. Observou-se também uma relativa evasão de estudantes durante o período letivo, levantando questionamentos para estudos futuro a respeito do estímulo que possa ter levado a tal evasão.
Title in English
Learning analytics in the support, planning and evaluation of active methodologies in physics teaching
Keywords in English
Active methodologies
Educacional data
Learning analytics
Physics teaching
Abstract in English
In this work, exploratory data analysis was conducted on the activities and performance of students enrolled in introductory physics courses at the São Carlos campus of the University of São Paulo (USP). The goal of the analysis was to identify correlation patterns associated with course approval that could assist professors in decision-making regarding the course's development and the process of resuming and/or recovering educational content. In this study, data from nine classes, comprising a total of 565 students, were collected using a virtual learning environment (VLE) and analyzed (anonymized) using Python scripts, aided by mathematical, computational, and statistical tools and techniques that form the foundation of Learning Analytics and Data Science applied to education. Positive correlations were found between exam scores and course approval, while only a small correlation was observed between class attendance and final grade. A relative dropout rate of students was also observed during the academic period, prompting questions for future studies regarding the factors that may have contributed to such dropout.
 
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Publishing Date
2023-07-14
 
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