Time-Dependent Performance Prediction System for Early Insight in Learning Trends

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/107154
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Title: Time-Dependent Performance Prediction System for Early Insight in Learning Trends
Authors: Villagrá-Arnedo, Carlos-José | Gallego-Durán, Francisco J. | Llorens Largo, Faraón | Satorre Cuerda, Rosana | Compañ, Patricia | Molina-Carmona, Rafael
Research Group/s: Grupo de Investigación en Tecnologías Inteligentes para el Aprendizaje (Smart Learning)
Center, Department or Service: Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial
Keywords: E-learning | Education | Learning Analytics | Learning Management Systems | Prediction | Support Vector Machine
Knowledge Area: Ciencia de la Computación e Inteligencia Artificial
Issue Date: 27-May-2020
Publisher: Universidad Internacional de La Rioja
Citation: International Journal of Interactive Multimedia and Artificial Intelligence. 2020, 6(2): 112-124. doi:10.9781/ijimai.2020.05.006
Abstract: 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
Language: eng
Type: info:eu-repo/semantics/article
Rights: Creative Commons Attribution license
Peer Review: si
Publisher version: https://doi.org/10.9781/ijimai.2020.05.006
Appears in Collections:INV - Smart Learning - Artículos de Revistas

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