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The Most Potential Decision Tree Technique to Classify the Large Dataset of Students

  • 2021
  • OriginalPaper
  • Chapter
Published in:
Authors
Afiqah Zahirah Zakaria
Ali Selamat
Hamido Fujita
Ondrej Krejcar
Published in
Computational Science and Technology
Publisher
Springer Singapore

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Abstract

The chapter delves into the application of Decision Tree techniques to classify large datasets of students, emphasizing the impact of the number of splits on accuracy. It explores the integration of learning analytics in education, focusing on the roles of administrators, instructors, and students. The research compares various machine learning algorithms, including Decision Trees and Ensemble Classifiers, to determine the most effective method for classifying large datasets. The study uses a dataset from Kaggle, comprising 378,005 records, to evaluate the performance of different techniques. The results show that Boosted Tree, an ensemble classifier, achieves the highest accuracy of 99.6%, demonstrating its potential for handling big data problems in education. The chapter concludes by highlighting the need for further improvements in algorithm speed and training time to enhance the practicality of these techniques in educational settings.
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previous chapter A Review on Deep Learning Approaches to Forecasting the Changes of Sea Level
Title
The Most Potential Decision Tree Technique to Classify the Large Dataset of Students
Authors
Afiqah Zahirah Zakaria
Ali Selamat
Hamido Fujita
Ondrej Krejcar
Copyright Year
2021
Publisher
Springer Singapore
Book
Computational Science and Technology

Print ISBN: 978-981-334-068-8

Electronic ISBN: 978-981-334-069-5

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-33-4069-5_47
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