Moodle Plug-in to Predict Student Performance Using Data Mining

ID: 49969 Type: Virtual Paper
  1. Igor Moreira Félix, Ana Paula Ambrósio, Joyce Siqueira, and Priscila Silva Neves Lima, Universidade Federal de Goiás, Brazil
  2. Jacques Duilio Brancher, Universidade Estadual de Londrina, Brazil

Moodle registers a vast amount of data about students’ behavior, including information about interaction between students and environment; students and content; and students and other students. This information can be used to discover behavior that can predict failure, so teachers can act in order to prevent students from dropping out or having low outcomes. However, manual analysis of this data can be time consuming and sometimes impossible. To explore all this information, data mining techniques have been used. This paper proposes the development of a Moodle plug-in for predicting student performance and generate alerts for teachers and tutors inside the learning management system.


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