This quantitative research analyzes the impact of the Web Application for the Educational Process on Compound Interest (WAEPCI) considering the machine learning and data science. The sample is composed of 46 students who studied the Financial Mathematics course in a Mexican university during the 2017 school year. WAEPCI presents the calculation of the Compound Interest and Compound Amount over a period of four years through the data simulation. The results of the machine learning (linear regression) indicate that WAEPCI positively influences the assimilation of knowledge and development of mathematical skills on the Compound Interest and Compound Amount. Data science establishes 4 predictive models on the use of WAEPCI in the educational process by means of the decision tree technique. The construction of web applications facilitates the active role of students, improves the assimilation of knowledge and allows the development of skills. Finally, WAEPCI improves the teaching-learning conditions on Financial Mathematics through the data simulation.
Technology,, higher education, web application, machine learning, data science