top
logo

Login Form



Visitors Counter

mod_vvisit_counterToday29
mod_vvisit_counterYesterday73
mod_vvisit_counterThis week524
mod_vvisit_counterThis month1637
mod_vvisit_counterAll159436

Who's Online

We have 17 guests online

Home Members Ioannis E. Livieris Forecasting students’ performance using an ensemble SSL algorithm
Error
  • Error loading feed data.
  • Error loading feed data.
Forecasting students’ performance using an ensemble SSL algorithm PDF Print E-mail

.E. Livieris, V. Tampakas, N. Kyriakidou, T. Mikropoulos and P. Pintelas. Forecasting students’ performance using an ensemble SSL algorithm. In IEEE 1st International Conference on Technology and Innovation in Learning, Teaching and Education, 2018.

 

 

Abstract - Educational data mining is a growing academic research area which aims to gain significant insights on student behavior, interactions
and performance by applying data mining methods on educational data. During the last decades, a variety of accurate models has been developed to monitor students' future progress, while most of these studies are based on supervised classification methods. In this work, we propose an ensemble semi-supervised algorithm for the prediction of students' performance in the final examinations at the end of academic year. The experimental results demonstrate the efficiency and robustness of the proposed algorithm compared to some classical classification algorithms, in terms of accuracy.

 

Search Engines




bottom
top

Department of Mathematics

Educational Software News

Call for papers

Newest Education Titles


bottom

Designed by Ioannis E. Livieris. | Validate XHTML | CSS