Comparison of Machine Learning Methods for Early Detection of Student Dropouts

Esra Siler Karabacak, Yusuf Yaslan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

One of the major issues in Educational Artificial Intelligence is detecting university student dropouts. Student dropout prediction is a growing research area since dropouts have financial, social, and national consequences. To deal with this issue, we examined the performance of ten machine learning algorithms with different feature sets in predicting university student dropout. Used algorithms are: Decision Trees, K-Nearest Neighbors, Naïve Bayes, Logistic Regression, Stacking Classifier, Adaboost, XGBoost, Random Forest, Support Vector Machines, and Multi Layer Perceptrons. The Synthetic Minority Oversampling Technique (SMOTE) balancing algorithm is used in the training process, to balance training data since the used dataset has an imbalanced nature. On the student dataset from Tecnologico de Monterrey in Mexico, we obtained 92 % accuracy on the XGBoost algorithm with a sub-feature set. We showed that the selected data features are as important as the selected method. Considering different performance metrics other than accuracy for imbalanced data is important. Having economic and historical score data increases accuracy. We also have seen that XGBoost and Random Forrest algorithms were the best for this task.

Original languageEnglish
Title of host publicationUBMK 2023 - Proceedings
Subtitle of host publication8th International Conference on Computer Science and Engineering
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages376-381
Number of pages6
ISBN (Electronic)9798350340815
DOIs
Publication statusPublished - 2023
Event8th International Conference on Computer Science and Engineering, UBMK 2023 - Burdur, Turkey
Duration: 13 Sept 202315 Sept 2023

Publication series

NameUBMK 2023 - Proceedings: 8th International Conference on Computer Science and Engineering

Conference

Conference8th International Conference on Computer Science and Engineering, UBMK 2023
Country/TerritoryTurkey
CityBurdur
Period13/09/2315/09/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • educational data mining
  • machine learning
  • student dropout prediction
  • university dropout

Fingerprint

Dive into the research topics of 'Comparison of Machine Learning Methods for Early Detection of Student Dropouts'. Together they form a unique fingerprint.

Cite this