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Using the Belbin method and models for predicting the academic performance of engineering students
Author(s) -
Gutiérrez Luis,
Flores Víctor,
Keith Brian,
Quelopana Aldo
Publication year - 2019
Publication title -
computer applications in engineering education
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.478
H-Index - 29
eISSN - 1099-0542
pISSN - 1061-3773
DOI - 10.1002/cae.22092
Subject(s) - team role inventories , computer science , random forest , metric (unit) , test (biology) , artificial intelligence , classifier (uml) , machine learning , psychology , teamwork , engineering , operations management , management , paleontology , economics , biology
This paper describes the process of generating a predictive model of students’ academic performance in different engineering subjects at Universidad Católica del Norte (UCN). It aims to analyze the importance of variables influencing the final average grade of the UCN students in projects related to different subjects, focusing on the dimensions resulting from the Belbin test. The main objective of this work is to provide evidence of the real impact of the Belbin test outcomes on the final performance of a team of students, using as a metric of variable importance the one provided by a Random Forest model, supplied by the scikit‐learn library. As a result, the final classifier presented an accuracy of 80%, and one of the most influential variables according to this model was Covered Roles 2, which represents the number of roles covered in each group. Future research lines are proposed to validate these outcomes, mostly concerned with the acquisition of more data across several future semesters.