Mining student data to assess the impact of moodle activities and prior knowledge on programming course success

Abstract

In this paper, Educational Data Mining and Learning Analytics are used in order to find out what impacts Programming 1 success most, since increase in the passing rate has been detected. The research is conducted on the dataset compounded of two parts: the first part is extracted from the Learning Management System (LMS) Moodle logs, while the second part is related to prior knowledge and students' preparation for the study. Classification methods are used to detect connections between prior knowledge and Moodle course activity in relation to final grades.