Mining Moodle Data to Detect the Inactive and Low-performance Students during the Moodle Course-Reference-Cited by-同舟云学术

Mining Moodle Data to Detect the Inactive and Low-performance Students during the Moodle Course

Published:2018 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2nd International Conference on Big Data Research - ICBDR 2018
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Author:

Hussain Mushtaq¹,Hussain Sadiq²,Zhang Wu¹,Zhu Wenhao¹,Theodorou Paraskevi³,Abidi Syed Muhammad Raza¹

Affiliation:

1. School of Computer Engineering and Science, Shanghai University, Shanghai, China

2. System Administrator, Dibrugharh University, Dibrugarh, Assam, India

3. National Center for Scientific Research "Demokritos", Agia, Paraskevi, Attica, Greece

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Publisher

ACM Press

Reference28 articles.

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3. Bovo, A., Sanchez, S., Heguy, O., and Duthen, Y. 2013. Clustering moodle data as a tool for profiling students. In Proceeding of Second International Conference on E-Learning and E-Technologies in Education (ICEEE), 121--126. doi:10.1109/icelete.2013.6644359

4. Bharara, S., Sabitha, S., and Bansal, A. 2018. Application of learning analytics using clustering data mining for students' disposition analysis. Education and Information Technologies 23(2), 957--984. doi:10.1007/s10639-017-9645

5. Burgos, C., Campanario, M.L., Peña, D., Lara, J. A et al. 2018. Data mining for modeling students' performance: A tutoring action plan to prevent academic dropout. Computers and Electrical Engineering 66, 541--556.

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