Data mining for student assessment in e-leaming: A survey

Yuniarti, Wenty Dwi and Winarko, Edi and Musdholifah, Aina (2020) Data mining for student assessment in e-leaming: A survey. In: 5th International Conference on Informatics and Computing, ICIC 2020.

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Abstract

The diffusion of technology in learning is increasingly massive, marked by the rapid transfer of learning into online environments such as e-Learning. Assessment is an important element of education. Assessment in e-Learning requires methods to be efficient and effective. Data mining is a method of analysis to reveal and recognize hidden patterns in educational databases. Deepening data mining for assessment in e-Learning is both an interesting and a challenge for teachers and institutions to find the right method and make a significant contribution in this area. Therefore, we conducted a literature review and presented state-of-the-art data mining for student assessment in e-Learning from relevant literature publishing from 2016 to 2020. We specifically focus on student assessment research in e-Learning, namely the scope of utilizing data mining, a comparison of several methods, and an analysis of several aspects related to assessment. This study also sheds light on future research directions. We identify the process mining approach as a data mining sub-discipline for the current trend assessment.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Cited by: 5
Uncontrolled Keywords: Data mining; E-Learning; Student assessment
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > Electronics > Computer engineering. Computer hardware
Divisions: Faculty of Engineering > Electrical and Information Technology Department
Depositing User: Sri JUNANDI
Date Deposited: 08 Oct 2025 03:12
Last Modified: 08 Oct 2025 03:12
URI: https://ir.lib.ugm.ac.id/id/eprint/22082

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