Online Fault Detection Methodology of Question Moodle Database Using Scan Statistics Method

Information and Software Technologies: 23rd International Conference, ICIST 2017: Proceedings. Communications in Computer and Information Science. Vol.756 2017
Aleksejs Jurenoks, Svetlana Jurenoka, Leonīds Novickis

This paper describes the methodology for creating the intelligent, user adapted testing system that has been developed using LMS Moodle. The integration of the intelligent processes into the existing training systems will prevent the draw-backs of the existing knowledge assessment systems and will make it possible to assess the learners’ ability automatically disable problematics or incorrect ques-tions from database question set. The methodology to provide fast online fault detection in Moodle question data-base using scan statistics method is described. Scan statistics have long been used to detect statistically significant bursts of events. This research of student faults in time enables to detect the most problematic topics of educational pro-cess, check the efficiency of the decisions taken to select the education strategy.

Keywords
E-learning, Moodle, Scan statistics
DOI
10.1007/978-3-319-67642-5_40
Hyperlink
https://link.springer.com/chapter/10.1007/978-3-319-67642-5_40

Jurenoks, A., Jurenoka, S., Novickis, L. Online Fault Detection Methodology of Question Moodle Database Using Scan Statistics Method. In: Information and Software Technologies: 23rd International Conference, ICIST 2017: Proceedings. Communications in Computer and Information Science. Vol.756, Lithuania, Druskininkai, 12-14 October, 2017. Cham: Springer Nature, 2017, pp.478-486. ISBN 978-3-319-67641-8. e-ISBN 978-3-319-67642-5. ISSN 1865-0929. e-ISSN 1865-0937. Available from: doi:10.1007/978-3-319-67642-5_40

Publication language
English (en)