A Data Mining Approach to Predict Undergraduate Students' Performance

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

Martins, MPG (Author) Other; Vera L. Miguéis (Author) FEUP; Fonseca, DSB (Author) Other

Conference proceedings International

Title: Iberian Conference on Information Systems and Technologies, CISTI

Pages: 1-7

13th Iberian Conference on Information Systems and Technologies, CISTI 2018

13 June 2018 through 16 June 2018

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 4 Citations

Other information

Authenticus ID: P-00P-1J5

DOI: 10.23919/cisti.2018.8399175

Abstract (EN): This paper presents a methodology based on random forest algorithm to predict the undergraduate academic performance of students from a polytechnic institution. The approach followed enabled to select 11 explanatory variables, starting from an initial set of around fifty, which allow to obtain a good predictive performance (R-2=0.79). These variables reveal crucial aspects for the definition of management strategies focused on promoting academic success.

Language: Portuguese

Type (Professor's evaluation): Scientific

No. of pages: 7