Predicting Student Performance by Using Data Mining Methods for Classification

By Dorina Kabakchieva. 2013

316 citations recorded by the source corpus.

Abstract Data mining methods are often implemented at advanced universities today for analyzing available data and extracting information and knowledge to support decision-making. This paper presents the initial results from a data mining research project implemented at a Bulgarian university, aimed at revealing the high potential of data mining applications for university management.

Subfields: Artificial Intelligence, Computer Science Applications, Information Systems

View Paper: https://www.semanticscholar.org/paper/debbac80fc55892c907bedd26779eeac55468bf0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Raheela Asif
2. Agathe Merceron
3. Syed Abbas Ali
4. Najmi Ghani Haider

In Computer Science Applications

1. Raheela Asif
2. Agathe Merceron
3. Syed Abbas Ali
4. Najmi Ghani Haider
5. M. Yağcı

In Information Systems

1. Raheela Asif
2. Agathe Merceron
3. Syed Abbas Ali
4. Najmi Ghani Haider
5. M. Yağcı

Citations in the corpus

• Analyzing undergraduate students' performance using educational data mining (2017, methodology): “The literature review also reveals that there is no classi fi er that does better than the others in all contexts, though decision trees is often quoted to give good results (Kabakchieva, 2013; Kabakchieva et al., 2011; Oskouei & Askari, 2014; Yehuala, 2015).”