Predicting student performance: an application of data mining methods with an educational Web-based system
By Behrouz Minaei-Bidgoli, Deborah Kashy, Gerd Kortemeyer, William F. Punch III. 33rd Annual Frontiers in Education, 2003. FIE 2003., 2003
372 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Science Applications, Information Systems
View Paper: https://www.semanticscholar.org/paper/f7cff163298ca7ea43b1e0099fe545f5fb1bcb37
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
In Computer Science Applications
- Cristóbal Romero
- Sebastián Ventura
- Ben Kai Daniel
- Anastasios A. Economides
- Z. Papamitsiou
- Alejandro Peña Ayala
- Rebecca Ferguson
- Ling Li
In Information Systems
- Sebastián Ventura
- Cristóbal Romero
- Ben Kai Daniel
- Anastasios A. Economides
- Z. Papamitsiou
- Alejandro Peña Ayala
- Rebecca Ferguson
- Ling Li
Citations in the corpus
- Artificial Intelligence and Reflections from Educational Landscape: A Review of AI Studies in Half a Century (2021, background): “As such, AI has already been utilize i predicting students’ achievement [13], identifying atisk tud nts in earli r stages [14,15], determining the main factors tha will affect students’ performance [16], conducting performance evaluation, providing descriptive information about them and contributing”
- Educational Data Mining: A Review of the State of the Art (2010, background): “on features extracted from logged data [180]; and to predict university students’ academic performance [128].”