Pbkp_redicting academic performance of university students from multi-sources data in blended learning

Abstract:

In this paper, we propose to pbkp_redict academic performance of university students from multi-sources data in multimodal and blended learning environments using data fusion and data mining. We have gathered data from 65 university students and different variables from four different sources. Firstly, we apply data fusion and preprocessing for creating a summary dataset in numerical and categorical format. Then, we have applied different white box classification algorithms provided by Weka data mining tool in order to select the best algorithm. Finally, we show the best pbkp_redicting model in order to help instructor to take remedial actions with students at risk of dropout or failing.

Año de publicación:

2019

Keywords:

• Multi-source data
• Pbkp_redicting student performance
• Educational Data Mining
• data fusion

Fuente:

scopus