Time-Dependent Performance Prediction System for Early Insight in Learning Trends
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http://hdl.handle.net/10045/107154
Título: | Time-Dependent Performance Prediction System for Early Insight in Learning Trends |
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Autor/es: | Villagrá-Arnedo, Carlos-José | Gallego-Durán, Francisco J. | Llorens Largo, Faraón | Satorre Cuerda, Rosana | Compañ, Patricia | Molina-Carmona, Rafael |
Grupo/s de investigación o GITE: | Grupo de Investigación en Tecnologías Inteligentes para el Aprendizaje (Smart Learning) |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial |
Palabras clave: | E-learning | Education | Learning Analytics | Learning Management Systems | Prediction | Support Vector Machine |
Área/s de conocimiento: | Ciencia de la Computación e Inteligencia Artificial |
Fecha de publicación: | 27-may-2020 |
Editor: | Universidad Internacional de La Rioja |
Cita bibliográfica: | International Journal of Interactive Multimedia and Artificial Intelligence. 2020, 6(2): 112-124. doi:10.9781/ijimai.2020.05.006 |
Resumen: | Performance prediction systems allow knowing the learning status of students during a term and produce estimations on future status, what is invaluable information for teachers. The majority of current systems statically classify students once in time and show results in simple visual modes. This paper presents an innovative system with progressive, time-dependent and probabilistic performance predictions. The system produces by-weekly probabilistic classifications of students in three groups: high, medium or low performance. The system is empirically tested and data is gathered, analysed and presented. Predictions are shown as point graphs over time, along with calculated learning trends. Summary blocks are with latest predictions and trends are also provided for teacher efficiency. Moreover, some methods for selecting best moments for teacher intervention are derived from predictions. Evidence gathered shows potential to give teachers insights on students' learning trends, early diagnose learning status and selecting best moment for intervention. |
URI: | http://hdl.handle.net/10045/107154 |
ISSN: | 1989-1660 |
DOI: | 10.9781/ijimai.2020.05.006 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | Creative Commons Attribution license |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.9781/ijimai.2020.05.006 |
Aparece en las colecciones: | INV - Smart Learning - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
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Villagra-Arnedo_etal_2020_IJIMAI.pdf | 1,36 MB | Adobe PDF | Abrir Vista previa | |
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