2021 |
Navarro-Acosta J.A., Soto-Mendoza Saucedo-Zendejo Guajardo-Espinoza Rivera-Morales V F R J M Evaluación psicológica de profesores y alumnos mexicanos durante la pandemia de COVID-19 mediante técnicas de Machine learning Journal Article Ingeniería Investigación y Tecnología, 22 (4), pp. 1-15, 2021, ISSN: 2594-0732. Abstract | Links | BibTeX | Etiquetas: classification, machine learning., prediction, Psychometry, validity @article{v22n4-02, title = {Evaluación psicológica de profesores y alumnos mexicanos durante la pandemia de COVID-19 mediante técnicas de Machine learning}, author = {Navarro-Acosta, J.A.,Soto-Mendoza, V.,Saucedo-Zendejo, F.R.,Guajardo-Espinoza, J.M.,Rivera-Morales}, url = {https://doi.org/10.22201/fi.25940732e.2021.22.4.026}, doi = {10.22201}, issn = { 2594-0732}, year = {2021}, date = {2021-04-21}, journal = {Ingeniería Investigación y Tecnología}, volume = {22}, number = {4}, pages = {1-15}, abstract = {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 = {classification, machine learning., prediction, Psychometry, validity}, pubstate = {published}, tppubtype = {article} } 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. |
Publicaciones
2021 |
Evaluación psicológica de profesores y alumnos mexicanos durante la pandemia de COVID-19 mediante técnicas de Machine learning Journal Article Ingeniería Investigación y Tecnología, 22 (4), pp. 1-15, 2021, ISSN: 2594-0732. |