Use of Machine Learning in Engineering Students’ Trajectories (72485)

Session Information: Educational Research, Development & Publishing
Session Chair: Elisha Markus

Friday, 22 September 2023 16:00
Session: Session 4
Room: Gotic
Presentation Type:Oral Presentation

All presentation times are UTC + 2 (Europe/Madrid)

Students’ trajectories show the student path in the educational system from the beginning to the end of their studies. There are several statistical tools to achieve its understanding and subsequent decision making by the institution. Each stage of the student’s trajectory can be described by educational, socio-economic, demographic and cultural variables. The purpose of the research is to apply the machine learning techniques Principal Component Analysis and k-means at the first interpretation of students’ trajectories. It allows to set up clusters and prioritisation variables that organise the academic trajectory characterisation. Techniques were applied to a population with 92 Surveying students of the Engineering School in the University of the Republic (Uruguay), with admissions between 2018 and 2022. For the database processing, the statistical software R was used through RStudio, modelling five variables. In this population, data can be represented by combinations of the original variables after Principal-Component-Analysis application. The variables that hold the highest level of importance corresponded to: Engineering School admission age and progress level determined with the obtained credits and expected credits ratio. Both variables are describing the 57% of the population. On the other hand, k-means clustering has shown three groups of interest generated according to both importance variables obtained with the Principal-Component-Analysis tool. The application of machine learning techniques made it possible to plan and systematise the subsequent qualitative analysis, which included the surveys and interviews.

Martín Pratto Burgos, University of the Republic, Uruguay
Daniel Alessandrini, University of the Republic, Uruguay
Fernando Fernández, University of the Republic, Uruguay
Ximena Otegui, University of the Republic, Uruguay

About the Presenter(s)
Professor Martín Pratto Burgos is a University Assistant Professor/Lecturer at University of the Republic in Uruguay

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Posted by Clive Staples Lewis

Last updated: 2023-02-23 23:45:00