SIATECH Student Analytics System: An integrated approach to descriptive, correlational, multivariate and predictive analytics
DOI:
https://doi.org/10.37431/conectividad.v6i2.303Keywords:
Machine learning, Prediction, Academic performance, Student satisfaction, Education, TeachingAbstract
The SIATECH study addresses the need to assess and improve academic satisfaction and performance in education by implementing advanced statistical and machine learning techniques. Using the importance-performance analysis (IPA) model as a basis, the study aims to develop a student analytics system to perform descriptive, correlational, multivariate and predictive analyses, thus providing a comprehensive tool to improve educational processes. This predictive approach offers the possibility of customizing educational interventions, thus improving individual academic outcomes, providing instructors with a clear understanding of student expectations and allowing them to adjust their teaching methods to improve learning. However, from a statistical know-how point of view, the lack of training in data analysis among some teachers may limit the ability to interpret and effectively use the information obtained, highlighting the need to integrate statistical training into teacher training programs, however, SIATECH devises a dynamic platform that is very easy to use and interpret. In summary, SIATECH proves to be a valuable tool for improving educational practices, emphasizing the importance of a data-driven approach to educational decision-making.
References
Arteaga, P., Batanero, C., & Gea, M. (2017). La componente mediacional del conocimiento didáctico-matemático de futuros profesores sobre Estadística: Un estudio de evaluación exploratorio. 1, 54-75. https://doi.org/10.24116/EMD25266136V1N12017A03
Bhumika Banswal, A. (2023). Analysing and Predicting Student's Performance Using their Surrounding Data. 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1-7. https://doi.org/10.1109/ICCCNT56998.2023.10307525
Carrasquilla-Batista, A., Chacón-Rodríguez, A., Núñez-Montero, K., Gómez-Espinoza, O., Valverde, J., Guerrero-Barrantes, M., Carrasquilla-Batista, A., Chacón-Rodríguez, A., Núñez-Montero, K., Gómez-Espinoza, O., Valverde, J., & Guerrero-Barrantes, M. (2016). Regresión lineal simple y múltiple: Aplicación en la predicción de variables naturales relacionadas con el crecimiento microalgal. Revista Tecnología en Marcha, 29, 33-45. https://doi.org/10.18845/tm.v29i8.2983
Chen, Y. C. (2017). Applying importance-performance analysis to assess student employability in Taiwan. Journal of Applied Research in Higher Education, 10, 76-86. https://doi.org/10.1108/JARHE-10-2017-0118
Cladera, M. (2020). An application of importance-performance analysis to students' evaluation of teaching. Educational Assessment, Evaluation and Accountability, 33, 701-715. https://doi.org/10.1007/s11092-020-09338-4
Cosentino, V., Luis, J., & Cabot, J. (2016). Findings from GitHub: Methods, datasets and limitations. Proceedings of the 13th International Conference on Mining Software Repositories, 137-141. https://doi.org/10.1145/2901739.2901776
Fernández, A., García García, J. I., Arredondo, E. H., & López Calvario, C. (2019). Comprensión de una tabla y un gráfico de barras por estudiantes universitarios. Areté: Revista Digital del Doctorado en Educación de la Universidad Central de Venezuela, 5(10), 145-162.
Flores, J., & Flores, R. (2018). La Enseñanza del Diagrama de Caja y Bigotes para Mejorar su Interpretación. Revista Bases de la Ciencia, 3(1), Article 1. https://doi.org/10.33936/rev_bas_de_la_ciencia.v3i1.1107
Gozá-León, O., Fernández-Águila, M., Rodríguez-Garcel, R. H., Ojito-Magaz, E., Gozá-León, O., Fernández-Águila, M., Rodríguez-Garcel, R. H., & Ojito-Magaz, E. (2020). Aplicación del Análisis de Componentes Principales en el proceso de purificación de un biofármaco. Vaccimonitor, 29(1), 5-13.
Jaramillo, H. A. L., Pinos, C. A. E., Sarango, A. F. H., & Román, H. D. O. (2023). Histograma y distribución normal: Shapiro-Wilk y Kolmogorov Smirnov aplicado en SPSS: Histogram and normal distribution: Shapiro-Wilk and Kolmogorov Smirnov applied in SPSS. LATAM Revista Latinoamericana de Ciencias Sociales y Humanidades, 4(4), Article 4. https://doi.org/10.56712/latam.v4i4.1242
Liu, Q., & Wang, L. (2021). T-Test and ANOVA for data with ceiling and/or floor effects. Behavior Research Methods, 53(1), 264-277. https://doi.org/10.3758/s13428-020-01407-2
Lugo-Armenta, J. G., & Pino-Fan, L. R. (2022). Niveles de Razonamiento Inferencial para el Estadístico t-Student. Bolema: Boletim de Educação Matemática, 35, 1776-1802. https://doi.org/10.1590/1980-4415v35n71a25
Martínez Ortega, R. M., Tuya Pendás, L. C., Martínez Ortega, M., Pérez Abreu, A., & Cánovas, A. M. (2009). EL COEFICIENTE DE CORRELACION DE LOS RANGOS DE SPEARMAN CARACTERIZACION. Revista Habanera de Ciencias Médicas, 8(2), 0-0.
McLeay, F., Robson, A., & Yusoff, M. (2017). New Applications for Importance-Performance Analysis (IPA) in Higher Education: Understanding Student Satisfaction. Journal of Management Development, 36, 780-800. https://doi.org/10.1108/JMD-10-2016-0187
Noronha, A., Nunes, M. F. O., & Ambiel, R. A. M. (2007). Importance and knowledge in psychological assessments: A study with Psychology students. 17, 231-244. https://doi.org/10.1590/S0103-863X2007000200007
Oyedeji, A., Salami, A. M., Folorunsho, O., & Abolade, O. R. (2020). Analysis and Prediction of Student Academic Performance Using Machine Learning. JITCE (Journal of Information Technology and Computer Engineering). https://doi.org/10.25077/jitce.4.01.10-15.2020
Pito, D. U., Moreno, E. C., & Becerra, M. M. (2020). Bodega de datos con alta capacidad de análisis para el desempeño académico de Universidades. 8, 102-118. https://doi.org/10.17081/INVINNO.8.3.4707
Zaborras, R., Martín, C. R., & Castellà, C. O. i. (2020). Análisis del comportamiento informacional de los estudiantes posgraduados de la Facultad de Educación de la Universidad de Barcelona. Revista Interuniversitaria de Formación del Profesorado, 34, 167-186. https://doi.org/10.47553/rifop.v34i2.79612
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