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Predicting Academic Success: A Comprehensive Analysis using Moodle Log Data and Learning Analytics
| Nome: | Descrição: | Tamanho: | Formato: | |
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| 2.22 MB | Adobe PDF |
Autores
Orientador(es)
Resumo(s)
There has been an advance in computing with the rise of large volumes of data. Education was
one of the fields that has advanced to this point. As new technologies and information are
continually integrated, datasets are now available from students' interactions with
educational software and online learning platforms. Educational software like Moodle
exemplifies e-learning solutions that combine traditional teaching methods with information
technology resources.
In this line of thought, this study investigates the potential use of Learning Analytics and
Moodle log data to predict academic success in higher education, considering the first part
and the whole course. The research uses a variety of machine learning algorithms, including
Random Forest, Logistic Regression, Gradient Boost, Support Vector Machine, and Neural
Networks, to uncover patterns in student behaviour and academic performance. The data
used in the study is related to Moodle log data and sociodemographic information that comes
from NOVA Information Management School's Master of Data Science and Advanced
Analytics program, of three academic years (2021-2023). To achieve the objective a CRISP-DM
methodology was implemented to serve as the base model for Machine Learning models.
The Random Forest model had the highest prediction values; however, overfitting was
detected in all models. Key findings show that student involvement, as measured by
interactions with course materials and demographic parameters such as age and nationality,
has a significant impact on academic success.
The study underlines the importance of early and persistent student involvement with Moodle
and offers instructors techniques to improve student engagement and performance. Future
work will include the development of early warning systems and complete dashboards to offer
instructors real-time knowledge.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
Palavras-chave
Learning Analytics Academic Success Machine Learning Moodle SDG 4 - Quality education