Prediction of Academic Success in a University and Improvement Using Lean Tools

The pandemic of COVID-19 caused several essential challenges for humanity. In the educational sector, mechanisms had to be quickly implemented to migrate in-person activities to complete virtuality. Academic institutions and society faced a paradigm shift since modifying the conditions of the teaching-learning system produced changes in the quality of education and student approval rates. This scientific article evaluates three classification models built by collecting data from a public Higher Education Institution to predict its approval based on different exogenous variables. The results show that the highest performance was obtained with the Random Forest algorithm, which has an accuracy of 61.3% and allows us to identify students whose initial conditions generate a high probability of failing a virtual course before it starts. In addition, this research collected information to detect opportunities for improving the prediction model, including restructuring the questions in the surveys and including new variables. The results suggest that the leading cause of course failure is the lack of elementary knowledge and skills students should have acquired during their secondary education. Finally, to mitigate the problem, a readjustment of the study program is proposed along with lean support tools to measure the results of these modifications.