Abstract

NAVARRO-ACOSTA, J. Alejandro et al. Machine learning approaches for psychological assessment of mexican professors and students during COVID-19 pandemic. Ing. invest. y tecnol. [online]. 2021, vol.22, n.4, e1805.  Epub Jan 31, 2022. ISSN 2594-0732.  https://doi.org/10.22201/fi.25940732e.2021.22.4.026.

This work describes the validation of the results of a psychological test applied to teachers and students in isolation due to the COVID-19 pandemic in the state of Coahuila, Mexico. The objective of this work is to apply machine learning techniques to validate an instrument that measures negative emotions and feelings, as well as cognitive bias or deviation of thinking about education and the pandemic in isolation. For the fulfillment of the objective, an instrument was applied in electronic format that was disseminated in the state of Coahuila, the users respond and the database is generated, which, after its pre-processing, is analyzed using the combination of Random Forest (RF) and Support Vector Machines (SVM); obtaining as a result the relevance or not of some of the items from the tests, thereby giving an internal validity to the instrument. The experimental results show that the proposed methodology is capable of selecting the most relevant predictor variables. In this way, satisfactory results were obtained in the classification and prediction of psychological diagnoses. On the other hand, although the implemented techniques are robust and reliable, they present limitations in terms of the observation of the other types of validity: construct, external, among others; which could limit its use. Although, in the field of psychometry there are various classic strategies, the proposed methodology based on the combination of machine learning techniques for the analysis and validation of this type of tests, favors the growth of options to improve diagnoses and consequently the treatment of psychological ailments.

Keywords : Psychometry; validity; classification; prediction; machine learning.

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