Investigation of Student Dropout through Data Mining and Machine Learning: A Systematic Mapping
DOI:
https://doi.org/10.5753/rbie.2024.3466Keywords:
Prediction, Classification, Student dropout, Machine learning, Data miningAbstract
Dropping out of students in schools and universities is a recurrent problem in education, it is both harmful for the student in terms of learning, and generates financial expenses for educations institutions, whether public or private. Studies using data mining (DM) and machine learning (ML) techniques to investigate problems in education are on the rise. Student dropout is one such problem. Through these techniques, it is possible to identify patterns in individuals or groups that may drop out of studies. This article aims to systematically map state-of-the-art articles on the application of DM and ML in data classification in studies on school dropout. The search was carried out in 5 bibliographic databases, ACM Digital Library, IEEE Xplore, Scopus, ScienceDirect, and Web of Science, and returned a total of 336 primary studies. After applying the exclusion and inclusion criteria, 71 relevant studies remained. After extracting data from these studies, it was identified that the experiences with higher education students and in the face-to-face modality are the most recurrent in these articles, the year that most stood out in terms of publication was 2020, and the most frequently used algorithms for building the classification models are algorithms based on decision trees.
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