TY - GEN
T1 - Prospección de matricula mediante la aplicación del modelo de dinámica de sistemas en una institución de educación superior
AU - Michel Legal Hernández, J.
AU - de Luna, Abel Muñoz
AU - Escamilla Garcia, Pablo E.
AU - Aceves Hernández, Francisco J.
N1 - Publisher Copyright:
© 2020 Latin American and Caribbean Consortium of Engineering Institutions. All rights reserved.
PY - 2020
Y1 - 2020
N2 - This paper highlights the results obtained by designing a prediction model aimed at estimating the enrolment of students per subject in an institution of higher education from Mexico. To perform the estimation, the system dynamics technique was applied using software VenSim, version 7.2a. The model evaluated the period from August to December 2018 to forecast three subjects: Linear Algebra (AL), Applied Linear Programming (PLA) and Networks and Simulation (RYS). The model included the following variables: number of classrooms and teachers available; number of students who failed and passed in ordinary and extraordinary term; rate of student desertion. The main results showed a prediction accuracy percentage of 97% for AL, 89% for PLA and 97% for RYS. The prognostic error represented 3.4 groups in contrast to the prediction certainty of 72.3 groups. This research provides a model to forecast reliable data for decision making in order to optimize resources.
AB - This paper highlights the results obtained by designing a prediction model aimed at estimating the enrolment of students per subject in an institution of higher education from Mexico. To perform the estimation, the system dynamics technique was applied using software VenSim, version 7.2a. The model evaluated the period from August to December 2018 to forecast three subjects: Linear Algebra (AL), Applied Linear Programming (PLA) and Networks and Simulation (RYS). The model included the following variables: number of classrooms and teachers available; number of students who failed and passed in ordinary and extraordinary term; rate of student desertion. The main results showed a prediction accuracy percentage of 97% for AL, 89% for PLA and 97% for RYS. The prognostic error represented 3.4 groups in contrast to the prediction certainty of 72.3 groups. This research provides a model to forecast reliable data for decision making in order to optimize resources.
KW - Enrollment
KW - Prediction
KW - Probability
KW - System Dynamic
UR - http://www.scopus.com/inward/record.url?scp=85096794917&partnerID=8YFLogxK
U2 - 10.18687/LACCEI2020.1.1.74
DO - 10.18687/LACCEI2020.1.1.74
M3 - Contribución a la conferencia
AN - SCOPUS:85096794917
T3 - Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
BT - 18th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology
PB - Latin American and Caribbean Consortium of Engineering Institutions
T2 - 18th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology: "Engineering, Integration, and Alliances for a Sustainable Development" "Hemispheric Cooperation for Competitiveness and Prosperity on a Knowledge-Based Economy", LACCEI 2020
Y2 - 27 July 2020 through 31 July 2020
ER -