International Journal of Computer Applications |
Foundation of Computer Science (FCS), NY, USA |
Volume 186 - Number 5 |
Year of Publication: 2024 |
Authors: Shaimaa Mohsen Hassan, Nesrine Ali AbdelAzim, Nagy Ramadan |
10.5120/ijca2024923369 |
Shaimaa Mohsen Hassan, Nesrine Ali AbdelAzim, Nagy Ramadan . Survey on Assessing Students’ Performance using Data Mining Techniques. International Journal of Computer Applications. 186, 5 ( Jan 2024), 5-12. DOI=10.5120/ijca2024923369
Finding patterns and other relevant information from huge datasets is a process known as data mining, also referred to as Knowledge Discovery in Database (KDD). Large data sets are sorted through in data mining in order to find patterns and relationships that may be used in data analysis to assist solve business challenges. Enterprises can forecast future trends and make more educated business decisions due to data mining techniques and technologies. Data science uses cutting-edge analytics techniques to find important information in data sets, and data mining is an essential part of data analytics as a whole and one of the core areas of data science. At a more granular level, data mining is a step in KDD process, a data science methodology for gathering, processing and analyzing data [31]. Data Mining techniques can be used in educational field which is called Educational Data Mining; it is an emerging discipline concerned with developing methods. These methods are used for exploring unique and increasingly large-scale data that come from educational settings in order to better understand students, and the settings which they learn in. [33]. This study presents a literature survey upon the various data mining tasks used in the prediction of the students’ performance.