Abstract
A strategy that supports the student’s academic and personal formation is that university consider tutoring as a mechanism that supports with favorable results to fight against the desertion of students. However, there are related problems in performing student segmentation and conducting psychological interventions. The objective was to formulate a classification model for intervention programs in university students based on unsupervised algorithms. For this, we carried out a non-experimental, simple descriptive study on a population of 60 university students; we carried out the data extraction process through a chatbot that applied the BarOn ICE test. After we obtained the data, the unsupervised k-means algorithm was used to group the students into sets determined based on the closest mean value obtained from the psychological test. We built a model for classifying students based on their answers to the BarOn ICE test based on K-means, with which we obtained five groups. The model classifies students by applying a different mathematical method to that used by the models applied by psychologists.
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References
Acosta, F.P., Clavero, F.H.: La influencia de las emociones sobre el rendimiento académico. Ciencias Psicológicas 11(1), 29–39 (2017). https://doi.org/10.22235/CP.V11I2.1344
del Barrios-Hernández, K.C., Olivero-Vega, E.: Relación universidad-empresa-estado. Un análisis desde las instituciones de educación superior de Barranquilla-Colombia, para el desarrollo de su capacidad de innovación. Formación universitaria 13(2), 21–28 (2020). https://doi.org/10.4067/S0718-50062020000200021
Coronado, D.M.: El rol de las universidades peruanas frente a la investigación y el desarrollo tecnológico. Propósitos y Representaciones 6(2), 703–737 (2018). https://doi.org/10.20511/PYR2018.V6N2.244
Chacon, M.D.: Acción tutorial en el fortalecimiento del perfil profesional universitario: aportes en el desarrollo de competencias a partir de la educación virtual. Espacios 42(5), 66–77 (2021). https://doi.org/10.48082/espacios-a21v42n05p05
Silva, P.A.P., Hernández, M.O.: Proceso de tutoría psicopedagógica. Acercamiento a la Universidad Técnica Estatal de Quevedo, Ecuador 18(2), 302–314 (2020). https://mendive.upr.edu.cu/index.php/MendiveUPR/article/view/1886
Guzmán, S.E.Y., del Marín, G.J.H.: Tutoría en la educación superior: análisis de la percepción de profesionales y estudiantes en una universidad pública. RIDE. Rev. Iberoam. Investig. Desarro. 9(18), 717–747 (2019). https://doi.org/10.23913/ride.v9i18.443
García, J.L.A.: La tutoría universitaria como práctica docente: fundamentos y métodos para el desarrollo de planes de acción tutorial en la universidad. Pro-Posições 30 (2019). https://doi.org/10.1590/1980-6248-2017-0038
del Cabezas, P..P.S., Álvarez, H.E.L., Rodríguez del Rey, M.M.L.: La tutoría en la educación superior y su integración en la actividad pedagógica del docente universitario. Conrado 15(70), 300–305 (2019). https://conrado.ucf.edu.cu/index.php/conrado/article/view/1140
Alonso-García, S., Rodríguez-García, A.M., Cáceres-Reche, M.P.: Analysis of the tutorial action and its impact on the overall development of the students: the case of the University of Castilla La Mancha, Spain. Formacion Universitaria 11(3), 63–72 (2018). https://doi.org/10.4067/S0718-50062018000300063
Alvites-Huamaní, C.G.: Estrés docente y factores psicosociales en docentes de Latinoamérica, Norteamérica y Europa. Propósitos y Representaciones 7(3), 141 (2019). https://doi.org/10.20511/pyr2019.v7n3.393
Mascarenhas, H., Rodrigues Dias, T.M., Dias, P.: Academic mobility of doctoral students in Brazil: an analysis based on lattes platform. Iberoamerican J. Sci. Meas. Commun. 1(3), 1–15 (2021). https://doi.org/10.47909/IJSMC.53
Islas Torres, C.: La implicación de las TIC en la educación: Alcances, Limitaciones y Prospectiva. RIDE. Rev. Iberoam. Investig. Desarro. 8(15), 861–876 (2018). https://doi.org/10.23913/ride.v8i15.324
Poveda-Pineda, D.F., Cifuentes-Medina, J.E.: Incorporación de las tecnologías de información y comunicación (TIC) durante el proceso de aprendizaje en la educación superior. Formación universitaria 13(6), 95–104 (2020). https://doi.org/10.4067/S0718-50062020000600095
Rodríguez, L.M.P.: Factores individuales y familiares asociados al bajo rendimiento académico en estudiantes universitarios 24(80), 173–195 (2019). https://www.comie.org.mx/revista/v2018/rmie/index.php/nrmie/article/view/1242
Montenegro Ordoñez, J.: La calidad en la docencia universitaria. Una aproximación desde la percepción de los estudiantes. Educación 29(56), 116–145 (2020). https://doi.org/10.18800/educacion.202001.006
Ocaña-Fernández, Y., Valenzuela-Fernández, L., Morillo-Flores, J.: La competencia digital en el docente universitario. Propósitos y Representaciones 8(1), e455 (2020). https://doi.org/10.20511/pyr2020.v8n1.455
Gontijo, M.C.A., Hamanaka, R.Y., Araujo, R.F. de: Research data management: a bibliometric and altmetric study based on Dimensions. Iberoamerican J. Sci. Meas. Commun. 1(3), 1–19 (2021). https://doi.org/10.47909/IJSMC.120
Vega-Hernández, M.C., Patino-Alonso, M.C., Galindo-Villardón, M.P.: Multivariate characterization of university students using the ICT for learning. Comput. Educ. 121, 124–130 (2018). https://doi.org/10.1016/j.compedu.2018.03.004
Casas-Huamanta, E.R.: Acceso a recursos tecnológicos y rendimiento académico en tiempos de pandemia y aislamiento social obligatorio. Revista científica de sistemas e informática 2(1), e296 (2022). https://doi.org/10.51252/RCSI.V2I1.296
Chen, M., Yan, Z., Meng, C., Huang, M.: The supporting environment evaluation model of ICT in Chinese university teaching. In: Proceedings - 2018 International Symposium on Educational Technology, ISET 2018, pp. 99–103 (2018). https://doi.org/10.1109/ISET.2018.00030
del Bárcenas, M.C.M., Morales, U.C.: Herramientas tecnológicas en el proceso de enseñanza-aprendizaje en estudiantes de educación superior. RIDE. Rev. Iberoam. Investig. Desarro. 10(19), e005 (2019). https://doi.org/10.23913/ride.v10i19.494
Gargallo Castel, A.F.: La integración de las TIC en los procesos educativos y organizativos. Educar em Revista. 34(69), 325–339 (2018). https://doi.org/10.1590/0104-4060.57305
Adakawa, M.I.: D-Space, makerspace, and hackerspace in cyberspace: cybersecurity strategies for digital preservation of library resources in the post-Covid-19 pandemic. Adv. Notes Inf. Sci. 1, 59–89 (2022). https://doi.org/10.47909/ANIS.978-9916-9760-0-5.98
Liang, W.: Development trend and thinking of artificial intelligence in education. In: 2020 International Wireless Communications and Mobile Computing, IWCMC 2020, pp. 886–890 (2020). https://doi.org/10.1109/IWCMC48107.2020.9148078
Khin, N.N., Soe, K.M.: University Chatbot using artificial intelligence markup language. In: 2020 IEEE Conference on Computer Applications, ICCA 2020, pp. 1–5 (2020). https://doi.org/10.1109/ICCA49400.2020.9022814
De-La-Hoz, E.J., De-La-Hoz, E.J., Fontalvo, T.J.: Metodología de Aprendizaje Automático para la Clasificación y Predicción de Usuarios en Ambientes Virtuales de Educación. Información tecnológica. 30(1), 247–254 (2019). https://doi.org/10.4067/S0718-07642019000100247
Debao, D., Yinxia, M., Min, Z.: Analysis of big data job requirements based on K-means text clustering in China. PLoS ONE 16(8), e0255419 (2021). https://doi.org/10.1371/JOURNAL.PONE.0255419
Latipa Sari, H., et al.: Integration K-means clustering method and elbow method for identification of the best customer profile cluster. In: IOP Conference Series: Materials Science and Engineering, p. 012017. https://doi.org/10.1088/1757-899X/336/1/012017
Marisa, F., Ahmad, S.S.S., Yusof, Z.I.M., Hunaini, F., Aziz, T.M.A.: segmentation model of customer lifetime value in small and medium enterprise (SMEs) using K-means clustering and LRFM model. Int. J. Integr. Eng. 11(3), 169–180 (2019). https://doi.org/10.30880/ijie.2019.11.03.018
Rodríguez Chávez, M.H.: Sistemas de tutoría inteligente y su aplicación en la educación superior. RIDE. Rev. Iberoam. Investig. Desarro. 11(22), e175 (2021). https://doi.org/10.23913/RIDE.V11I22.848
Omolewa, O.T., Oladele, A.T., Adeyinka, A.A., Oluwaseun, O.R.: Prediction of student’s academic performance using k-means clustering and multiple linear regressions. J. Eng. Appl. Sci. 14 22), 8254–8260 (2019). https://doi.org/10.36478/JEASCI.2019.8254.8260
Song, D., Oh, E.Y., Rice, M.: Interacting with a conversational agent system for educational purposes in online courses. In: Proceedings - 2017 10th International Conference on Human System Interactions, HSI 2017, pp. 78–82 (2017). https://doi.org/10.1109/HSI.2017.8005002
Marutho, D., Hendra Handaka, S., Wijaya, E., Muljono: the determination of cluster number at k-mean using elbow method and purity evaluation on headline news. In: Proceedings - 2018 International Seminar on Application for Technology of Information and Communication: Creative Technology for Human Life, iSemantic 2018, pp. 533–538 (2018). https://doi.org/10.1109/ISEMANTIC.2018.8549751
Hernandez-Cruz, N.: Mapping the thematic evolution in Communication over the first two decades from the 21st century: a longitudinal approach. Iberoamerican J. Sci. Meas. Commun. 1(3), 1–10 (2021). https://doi.org/10.47909/IJSMC.88
Idrogo Zamora, D.I., Asenjo-Alarcón, J.A.: Relación entre inteligencia emocional y rendimiento académico en estudiantes universitarios peruanos. Revista de Investigación Psicológica (26), 69–79 (2021). https://doi.org/10.53287/RYFS1548JS42X
Lee, L.K., et al.: Using a multiplatform chatbot as an online tutor in a university course. In: Proceedings - 2020 International Symposium on Educational Technology, ISET 2020, pp. 53–56 (2020). https://doi.org/10.1109/ISET49818.2020.00021
Shamrat, F.M.J.M., Tasnim, Z., Mahmud, I., Jahan, N., Nobel, N.I.: Application of k-means clustering algorithm to determine the density of demand of different kinds of jobs. Int. J. Sci. Technol. Res. 9(2), 2550–2557 (2020)
Liu, F., Deng, Y.: Determine the number of unknown targets in open world based on elbow method. IEEE Trans. Fuzzy Syst. 29(5), 986–995 (2021). https://doi.org/10.1109/TFUZZ.2020.2966182
Yuan, C., Yang, H.: Research on k-value selection method of k-means clustering algorithm. J. Multidisc. Sci. J. 2(2), 226–235 (2019). https://doi.org/10.3390/J2020016
Nainggolan, R., Perangin-Angin, R., Simarmata, E., Tarigan, A.F.: Improved the performance of the k-means cluster using the sum of squared error (SSE) optimized by using the elbow method. In: Journal of Physics: Conference Series, p. 012015 (2019). https://doi.org/10.1088/1742-6596/1361/1/012015
Br, R.W., Berahmana, S., Mohammed, A., Chairuang, K., Jimbaran, B.: Customer segmentation based on RFM model using K-means, K-medoids, and DBSCAN methods. Lontar Komputer : Jurnal Ilmiah Teknologi Informasi 11(1), 32–43 (2020). https://doi.org/10.24843/LKJITI.2020.V11.I01.P04
Kansal, T., Bahuguna, S., Singh, V., Choudhury, T.: Customer segmentation using k-means clustering. In: Proceedings of the International Conference on Computational Techniques, Electronics and Mechanical Systems, CTEMS 2018, pp. 135–139 (2018). https://doi.org/10.1109/CTEMS.2018.8769171
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Huamán, B., Gómez, D., Lévano, D., Valles-Coral, M., Navarro-Cabrera, J.R., Pinedo, L. (2022). Classification Model Based on Chatbot and Unsupervised Algorithms to Determine Psychological Intervention Programs in Peruvian University Students. In: Pinto, A.L., Arencibia-Jorge, R. (eds) Data and Information in Online Environments. DIONE 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 452. Springer, Cham. https://doi.org/10.1007/978-3-031-22324-2_15
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