Prediction and Prevention of School Dropout through A.I.: A Review to Identify Models and Relevant Factors
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Abstract
School dropout is a pressing concern in educational institutions, as per statistics from the Ministry of Education of Colombia, which report that 473,786 children and young students have discontinued their studies between November 2022 and May 2023. This issue is especially prominent in Science, Technology, Engineering, and Mathematics (STEM) academic programs. Addressing this challenge requires the integration of Information Technology (IT) tools that provide effective and timely monitoring to the academic control departments. The purpose of this literature review is to explore the variables related to academic dropout and find suitable predictive models for data processing while also identifying variables and models previously used in the field. To achieve this, research is proposed using academic search platforms such as Lens.org and Google Scholar. After conducting the research, relevant variables in the national context are identified, such as academic performance, age, gender, family status, and psychological aspects, among others, as they are considered crucial for accurate prediction. The C4.5 decision tree model was chosen due to its excellent performance in research, widespread usage in the field, and low computational cost.
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