TY - GEN
T1 - Machine Learning Models for Predicting Student Dropout—a Review
AU - Salinas-Chipana, José
AU - Obregon-Palomino, Luz
AU - Iparraguirre-Villanueva, Orlando
AU - Cabanillas-Carbonell, Michael
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2024
Y1 - 2024
N2 - Student dropout is a worldwide problem that affects an entire society; thus, being of great concern for academic institutions that seek to retain their students through different strategies, machine learning is the most used for the early detection of students at risk. For this reason, in the present work, an exhaustive systematic literature review study of manuscripts related to the prediction of student dropout was carried out. The articles were obtained from six databases, which were searched using the PRISMA methodology. A total of 88 manuscripts were selected from which 4 questions were posed. Finally, we obtained as an answer to the questions that the most used model is the random forest, with an accuracy of between 73 and 99% for predicting student dropout. For this, aspects such as academic, demographic, economic, and health aspects must be considered. Meanwhile, the technological tool for the models was the Python language according to this systematic review.
AB - Student dropout is a worldwide problem that affects an entire society; thus, being of great concern for academic institutions that seek to retain their students through different strategies, machine learning is the most used for the early detection of students at risk. For this reason, in the present work, an exhaustive systematic literature review study of manuscripts related to the prediction of student dropout was carried out. The articles were obtained from six databases, which were searched using the PRISMA methodology. A total of 88 manuscripts were selected from which 4 questions were posed. Finally, we obtained as an answer to the questions that the most used model is the random forest, with an accuracy of between 73 and 99% for predicting student dropout. For this, aspects such as academic, demographic, economic, and health aspects must be considered. Meanwhile, the technological tool for the models was the Python language according to this systematic review.
KW - Machine learning
KW - Model
KW - Prediction
KW - School dropout
KW - Student dropout
UR - https://www.scopus.com/pages/publications/85174730153
U2 - 10.1007/978-981-99-3043-2_83
DO - 10.1007/978-981-99-3043-2_83
M3 - Conference contribution
AN - SCOPUS:85174730153
SN - 9789819930425
T3 - Lecture Notes in Networks and Systems
SP - 1003
EP - 1014
BT - Proceedings of 8th International Congress on Information and Communication Technology - ICICT 2023
A2 - Yang, Xin-She
A2 - Sherratt, R. Simon
A2 - Dey, Nilanjan
A2 - Joshi, Amit
T2 - 8th International Congress on Information and Communication Technology, ICICT 2023
Y2 - 20 February 2023 through 23 February 2023
ER -