Business Methodology for the Application in University Environments of Predictive Machine Learning Models Based on an Ethical Taxonomy of the Student’s Digital Twin

Luis Miguel Garay Gallastegui and Ricardo Francisco Reier Forradellas
Additional contact information
Luis Miguel Garay Gallastegui: Department of Marketing, ESIC University, Pozuelo de Alarcón, 28223 Madrid, Spain
Ricardo Francisco Reier Forradellas: DEKIS Research Group, Department of Economics, Catholic University of Ávila, 05005 Avila, Spain

Administrative Sciences, 2021, vol. 11, issue 4, 1-24

Abstract: Educational institutions are undergoing an internal process of strategic transformation to adapt to the challenges caused by the growing impact of digitization and the continuous development of student and labor market expectations. Consequently, it is essential to obtain more accurate knowledge of students to improve their learning experience and their relationship with the educational institution, and in this way also contribute to evolving those students’ skills that will be useful in their next professional future. For this to happen, the entire academic community faces obstacles related to data capture, analysis, and subsequent activation. This article establishes a methodology to design, from a business point of view, the application in educational environments of predictive machine learning models based on Artificial Intelligence (AI), focusing on the student and their experience when interacting physically and emotionally with the educational ecosystem. This methodology focuses on the educational offer, relying on a taxonomy based on learning objects to automate the construction of analytical models. This methodology serves as a motivating backdrop to several challenges facing educational institutions, such as the exciting crossroads of data fusion and the ethics of data use. Our ultimate goal is to encourage education experts and practitioners to take full advantage of applying this methodology to make data-driven decisions without any preconceived bias due to the lack of contrasting information.

Keywords: competences; higher education; machine learning (search for similar items in EconPapers)
JEL-codes: L M M0 M1 M10 M11 M12 M14 M15 M16 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2076-3387/11/4/118/pdf (application/pdf)
https://www.mdpi.com/2076-3387/11/4/118/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jadmsc:v:11:y:2021:i:4:p:118-:d:659393

Access Statistics for this article

Administrative Sciences is currently edited by Ms. Nancy Ma

More articles in Administrative Sciences from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().