A Machine Learning Approach to Identifying Students at Risk of Dropout: A Case Study

被引：0

作者：

Lottering, Roderick ^{[1
]}

Hans, Robert ^{[1
]}

Lall, Manoj ^{[1
]}

机构：

[1] Tshwane Univ Technol, Dept Comp Sci, Gauteng, South Africa

来源：

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS | 2020年 / 11卷 / 10期

基金：

新加坡国家研究基金会; 芬兰科学院;

关键词：

EDM; student dropout; binary classification; ensemble method; KDD;

D O I：

10.14569/IJACSA.2020.0111052

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The increase in students' dropout rate is a huge concern for institutions of higher learning. In this article, classification techniques are applied to determine students "atrisk" of dropping out of their registered qualifications. Being able to identify such students timeously will be beneficial to both the students and the institutions with which they are registered. This study makes use of Random Forest, Support Vector Machines, Decision Trees, Naive Bayes, K-Nearest Neighbor, and Logistic Regression for classification purposes. The selected algorithms were applied on a dataset of 4419 student records obtained from the institutional database related to Diploma students enrolled in the Faculty of Information, Communication and Technology. The results reveal that the overall accuracy rate of Random Forest (94.14%) was better than the other algorithms in identifying students at risk of dropout.

引用

页码：417 / 422

页数：6

共 50 条

[1] APPLICATION OF MACHINE LEARNING TECHNIQUES IN AN ACADEMIC CONTEXT WITH A FOCUS ON IDENTIFYING DROPOUT AND DROPOUT STUDENTS
Cavina Piovesan Soares, Leandra Cristina
Ronzani, Robson Aparecido
de Carvalho, Rafael Lima
Rossini da Silva, Alexandre Tadeu
HUMANIDADES & INOVACAO, 2020, 7 (08): : 223 - 235
[2] USING MACHINE LEARNING TO ANALYZE UNIVERSITY STUDENTS' DROPOUT RATE - A CASE STUDY
Nedeva, Veselina
Pehlivanova, Tanya
INTERNATIONAL JOURNAL ON INFORMATION TECHNOLOGIES AND SECURITY, 2020, 12 (03): : 37 - 50
[3] A Learning Analytics Approach to Identify Students at Risk of Dropout: A Case Study with a Technical Distance Education Course
Queiroga, Emanuel Marques
Lopes, Joao Ladislau
Kappel, Kristofer
Aguiar, Marilton
Araujo, Ricardo Matsumura
Munoz, Roberto
Villarroel, Rodolfo
Cechinel, Cristian
APPLIED SCIENCES-BASEL, 2020, 10 (11):
[4] Predicting Dropout for Nontraditional Undergraduate Students: A Machine Learning Approach
Huo, Huade
Cui, Jiashan
Hein, Sarah
Padgett, Zoe
Ossolinski, Mark
Raim, Ruth
Zhang, Jijun
JOURNAL OF COLLEGE STUDENT RETENTION-RESEARCH THEORY & PRACTICE, 2023, 24 (04) : 1054 - 1077
[5] A machine learning approach to Predict the Engineering Students at risk of dropout and factors behind: Bangladesh Perspective
Ahmed, Sheikh Arif
Khan, Shahidul Islam
2019 10TH INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION AND NETWORKING TECHNOLOGIES (ICCCNT), 2019,
[6] A Study on Dropout Prediction for University Students Using Machine Learning
Cho, Choong Hee
Yu, Yang Woo
Kim, Hyeon Gyu
APPLIED SCIENCES-BASEL, 2023, 13 (21):
[7] A Hybrid Approach for Dropout Prediction of MOOC Students using Machine Learning
Alsolami, Fawaz J.
INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY, 2020, 20 (05): : 54 - 63
[8] Identifying At-Risk Students for Early Intervention-A Probabilistic Machine Learning Approach
Nimy, Eli
Mosia, Moeketsi
Chibaya, Colin
APPLIED SCIENCES-BASEL, 2023, 13 (06):
[9] A stacking ensemble machine learning method for early identification of students at risk of dropout
Juan Andrés Talamás-Carvajal
Héctor G. Ceballos
Education and Information Technologies, 2023, 28 : 12169 - 12189
[10] A stacking ensemble machine learning method for early identification of students at risk of dropout
Talamas-Carvajal, Juan Andres
Ceballos, Hector G.
EDUCATION AND INFORMATION TECHNOLOGIES, 2023, 28 (09) : 12169 - 12189

← 1 2 3 4 5 →