Research Title |
Efficiency of Decision Tree Depth to Diagnose Mathematical Procedures in Number and Algebra for Seventh-grade Students |
Date of Distribution |
3 July 2024 |
Conference |
Title of the Conference |
The 39th International Technical Conference on Circuits/Systems, Computers, and Communications (ITC-CSCC2024) |
Organiser |
Institute of Science and Technology Graduate University (OIST) |
Conference Place |
Institute of Science and Technology Graduate University (OIST) in Okinawa, Japan. |
Province/State |
Okinawa, Japan |
Conference Date |
2 July 2024 |
To |
5 July 2024 |
Proceeding Paper |
Volume |
2567 |
Issue |
- |
Page |
1-74 |
Editors/edition/publisher |
IEEE |
Abstract |
This study is dedicated to design and compare the efficiency of predictive models with different depths of decision trees for diagnosing mathematical
procedures in number and algebra. Leveraging a dataset comprising responses
from 509 seventh-grade students across 4 regions in Thailand, the investigation
applies the Python programming language with decision tree methodologies to
construct predictive models capable of accurately determining students'
proficiency in mathematical procedures. The findings of the research elucidate
that model efficiency is contingent upon the depth of the decision tree. Notably,
the model with a tree depth of 5 surpasses others in efficiency, evidencing an
accuracy rate of 0.87 and an error rate of 0.37. Meanwhile, the model with a
tree depth of 4 demonstrates slightly lower efficacy, with an accuracy rate of
0.76 and an error rate of 0.51. These results underscore the critical influence of decision tree depth on the performance of predictive models within theeducational domain. |
Author |
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Peer Review Status |
มีผู้ประเมินอิสระ |
Level of Conference |
นานาชาติ |
Type of Proceeding |
Full paper |
Type of Presentation |
Oral |
Part of thesis |
true |
ใช้สำหรับสำเร็จการศึกษา |
ไม่เป็น |
Presentation awarding |
false |
Attach file |
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Citation |
0
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