International Journal of
Information and Education Technology

Editor-In-Chief: Prof. Jon-Chao Hong
Frequency: Monthly
ISSN: 2010-3689 (Online)
E-mali: editor@ijiet.org
Publisher: IACSIT Press
 

OPEN ACCESS
3.9
CiteScore

IJIET 2019 Vol.9(5): 337-341
doi: 10.18178/ijiet.2019.9.5.1223

Predicting Student Performance from Their Behavior in Learning Management Systems

Parisa Shayan , Menno van Zaanen

Abstract

Nowadays, Information and Communication Technology (ICT) provides an opportunity to discover new knowledge and create a desirable learning environment. That is why the influence of ICT on education is irrefutable. Technology has changed the learning styles: the way people prefer to learn and improve the quality of their learning. Physical and online classes can be held concurrently so that lecturers and students can interact via learning management systems. A Learning Management System (LMS) is an application software that plays a significant role in educational technology. Such software can be designed to augment and facilitate instructional activities including registration and management of education courses, analyzing skill gaps, reporting, and delivery of electronic courses concurrently. Since all information and corresponding data are recorded and monitored in the LMS, it can provide an accurate insight into student’s online behavior. In general, measuring student performance is an important part of the education system. The fields of learning analytics and educational data mining both emphasize the analysis of educational data in order to improve teaching and learning styles as well as to predict student performance. In the current study, we use data from the Moodle LMS from a collection of courses from a single institution to identify weak/strong students during the course. The result has to be interpretable and understandable as the aim is to give this information to lecturers, who may use the information to improve their course and identify students who may need special attention.

Keywords

  • Data mining
  • clustering
  • decision tree
  • rule-based
  • student
1223-FM3004

How to Cite

Copied

Parisa Shayan and Menno van Zaanen, "Predicting Student Performance from Their Behavior in Learning Management Systems," International Journal of Information and Education Technology, vol. 9, no. 5, pp. 337-341, 2019. https://doi.org/10.18178/ijiet.2019.9.5.1223

Copyright & License

Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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