Applying Data Mining for Anomalies Detection on the Academic Performance of Student

DOI

10.2991/978-2-38476-198-2_105How to use a DOI?

Keywords

Data Mining; Student; Academic Performance; Anomalies Detection

Abstract

One of the crucial problems in online learning is the difficulty of monitoring student academic performance by teachers. However, research to find a solution to this problem is not much. On the other hand, the change in the educational paradigm during the corona pandemic resulted in large amounts of data stacks, especially student data. Data mining is often used to identify patterns in large datasets that can be used to train AI models. So, Data mining can be applied to student data to find knowledge or information that can be used to create a better educational environment. Therefore, our research focuses on the application of data mining to overcome the difficulties of monitoring student performance. The method used is based on density. Furthermore, our research detects an anomaly that occurs in student academic performance which produces information about students whose academic performance is different from the majority of other students. This knowledge is very important for teachers to prevent student failure in achieving academic performance.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Yuni Yamasari
AU  - Anita Qoiriah
AU  - Naim Rochmawati
AU  - Aditya Prapanca
AU  - Agus Prihanto
AU  - I Made Suartana
AU  - Ricky Eka Putra
PY  - 2024
DA  - 2024/02/06
TI  - Applying Data Mining for Anomalies Detection on the Academic Performance of Student
BT  - Proceedings of the 5th Vocational Education International Conference (VEIC-5 2023)
PB  - Atlantis Press
SP  - 767
EP  - 771
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-198-2_105
DO  - 10.2991/978-2-38476-198-2_105
ID  - Yamasari2024
ER  -