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Using the Monte Carlo Method to Estimate Student Motivation in Scientific Computing

Authors:
Isaac Caicedo-Castro
Oswaldo Vélez-Langs
Rubby Castro-Púche

Keywords: higher education; motivation; regression; Monte Carlo method

Abstract:
In this study, we investigate students' motivation to learn scientific computing in the undergraduate systems engineering program at the University of Córdoba (Colombia). Scientific computing is often a challenging subject for college students; therefore, motivation plays a crucial role in succeeding in these courses. To quantify the factors that potentially impact student motivation, we conducted a survey of 117 students, relying on their perceptions. Using an F-test, we selected 15 independent variables from the original set of factors. With this dataset, we applied multidimensional linear regression to identify a function that captures the regular patterns associating motivation with its influencing factors. This prediction function quantifies a student's motivation -- our target variable -- based on the values of the independent variables. Using this function and the Monte Carlo method, we explored the multidimensional space of independent variables to estimate the probability that a student attains one of ten motivation levels, ranging from completely demotivated to highly motivated to learn scientific computing. Our findings indicate that students are most likely to achieve moderate motivation levels, specifically the 4th (26.86%), 5th (45.03%), and 6th (21.21%) levels. However, we also found that implementing effective policies and strategies (e.g., enhancing student satisfaction) may increase the probability of achieving higher motivation levels, particularly the 7th (36.98%) and 8th (61.04%) levels.

Pages: 15 to 22

Copyright: Copyright (c) IARIA, 2025

Publication date: April 6, 2025

Published in: conference

ISSN: 2308-3557

ISBN: 978-1-68558-263-0

Location: Valencia, Spain

Dates: from April 6, 2025 to April 10, 2025