Notice: Function _load_textdomain_just_in_time was called incorrectly. Translation loading for the ultimate-member domain was triggered too early. This is usually an indicator for some code in the plugin or theme running too early. Translations should be loaded at the init action or later. Please see Debugging in WordPress for more information. (This message was added in version 6.7.0.) in /home/uapaedu/public_html/jornadacientifica.uapa.edu.do/wp-includes/functions.php on line 6114

Notice: Function _load_textdomain_just_in_time was called incorrectly. Translation loading for the updraftplus domain was triggered too early. This is usually an indicator for some code in the plugin or theme running too early. Translations should be loaded at the init action or later. Please see Debugging in WordPress for more information. (This message was added in version 6.7.0.) in /home/uapaedu/public_html/jornadacientifica.uapa.edu.do/wp-includes/functions.php on line 6114

Notice: Function _load_textdomain_just_in_time was called incorrectly. Translation loading for the wpforms-lite domain was triggered too early. This is usually an indicator for some code in the plugin or theme running too early. Translations should be loaded at the init action or later. Please see Debugging in WordPress for more information. (This message was added in version 6.7.0.) in /home/uapaedu/public_html/jornadacientifica.uapa.edu.do/wp-includes/functions.php on line 6114

Notice: Function _load_textdomain_just_in_time was called incorrectly. Translation loading for the hello-elementor domain was triggered too early. This is usually an indicator for some code in the plugin or theme running too early. Translations should be loaded at the init action or later. Please see Debugging in WordPress for more information. (This message was added in version 6.7.0.) in /home/uapaedu/public_html/jornadacientifica.uapa.edu.do/wp-includes/functions.php on line 6114

Notice: Function _load_textdomain_just_in_time was called incorrectly. Translation loading for the wordpress-seo domain was triggered too early. This is usually an indicator for some code in the plugin or theme running too early. Translations should be loaded at the init action or later. Please see Debugging in WordPress for more information. (This message was added in version 6.7.0.) in /home/uapaedu/public_html/jornadacientifica.uapa.edu.do/wp-includes/functions.php on line 6114
Willy Argenis Martínez De león - IX Jornada Científica
Tipo de trabajo: Ponencia - Jornada de Investigación Científica
Correo: wamd199324@gmail.com

Trabajos publicados:

Prevención del Bajo Rendimiento Académico Mediante el uso de Big Data

Resumen:

El objetivo de este estudio es desarrollar estrategias de prevención para el bajo rendimiento académico a través del análisis de grandes volúmenes de datos educativos. El problema científico radica en la identificación temprana de patrones que puedan predecir un desempeño académico deficiente, permitiendo así implementar intervenciones oportunas y eficaces. La metodología utilizada combina técnicas de minería de datos y aprendizaje automático para analizar datos históricos y actuales de los estudiantes, tales como asistencia, calificaciones, y participación en actividades académicas. Los principales resultados alcanzados muestran que es posible identificar factores predictivos del bajo rendimiento académico con un alto grado de precisión. Estos factores incluyen, entre otros, la frecuencia de participación en clases, la entrega de tareas a tiempo, y la interacción con los recursos educativos disponibles en línea. Además, se ha desarrollado un modelo predictivo que permite generar alertas tempranas para los estudiantes en riesgo, facilitando así la toma de decisiones informadas por parte de los educadores. El uso de Big Data en el ámbito educativo no solo permite una mejor comprensión de los factores que influyen en el rendimiento académico, sino que también proporciona herramientas prácticas para mejorar los resultados de los estudiantes. Este estudio demuestra la viabilidad y la importancia de incorporar tecnologías avanzadas en la gestión educativa para prevenir el bajo rendimiento académico de manera efectiva.

Palabras Clave:

 

Palabras Clave:

Big Data, rendimiento académico, minería de datos, aprendizaje automático, prevención, educación.

 

 

 

 

 

 

 

 

 

 

AbstracT:

The objective of this study is to develop prevention strategies for low academic performance through the analysis of large volumes of educational data. The scientific problem lies in the early identification of patterns that can predict poor academic performance, allowing timely and effective interventions. The methodology used combines data mining and machine learning techniques to analyze historical and current student data, such as attendance, grades, and participation in academic activities. The main results achieved show that it is possible to identify predictive factors of low academic performance with a high degree of accuracy. These factors include, among others, frequency of class participation, timely submission of assignments, and interaction with available online educational resources. Additionally, a predictive model has been developed to generate early warnings for students at risk, thus facilitating informed decision-making by educators.The use of Big Data in education not only allows a better understanding of the factors influencing academic performance but also provides practical tools to improve student outcomes. This study demonstrates the feasibility and importance of incorporating advanced technologies in educational management to effectively prevent low academic performance.

 

KeyWords:

Big Data, academic performance, data mining, machine learning, prevention, education.

Documento Adjunto
Autores:
  • Willy Argenis Martínez De león