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

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F21%3A50017992" target="_blank" >RIV/62690094:18450/21:50017992 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007%2F978-981-33-4069-5_47" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-981-33-4069-5_47</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-981-33-4069-5_47" target="_blank" >10.1007/978-981-33-4069-5_47</a>

Result language
angličtina
Original language name
The Most Potential Decision Tree Technique to Classify the Large Dataset of Students
Original language description
Education is one of the important fields in this challenging world. The researchers come out with the new perceptive, which is learning analytics that is a new invention for helping out the instructors, learners, and administrators. The use of learning analytics can be the medium for increasing the productivity of education for producing capable leaders in the future. Machine learning comes out with any type of techniques such as Decision Tree, Support Vector Machine, Naïve Bayes, and Ensemble Classifiers. However, both Decision Tree and Ensemble Classifiers are chosen as the best potential machine learning techniques to cope with the large database of students. The Boosted Tree of Ensemble Classifiers managed to get 99.6% accuracy of training 378,005 data of students regarding the Virtual Learning Environment (VLE). © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Czech name
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Czech description
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Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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