Work in progress - Prediction of students' academic performance in an introductory engineering course

Proceedings - Frontiers in Education Conference, FIE

Volumn , Issue , 2011, Pages

  Huang, Shaobo ^a   Fang, Ning ^a  

^a UTAH STATE UNIVERSITY   (United States)

Author keywords

Engineering dynamics; multivariate linear regression; neural networks; support vector machines

Indexed keywords

ACADEMIC PERFORMANCE; DATA POINTS; INTRODUCTORY ENGINEERING COURSE; MULTILAYER PERCEPTRON NEURAL NETWORKS; MULTIVARIATE LINEAR REGRESSIONS; PREDICTION ACCURACY; PREDICTIVE MODELS; PROACTIVE MEASURES; RADIAL BASIS FUNCTION NEURAL NETWORKS; STUDENT LEARNING; SUPPORT VECTOR MACHINE (SVM); UNDERGRADUATE STUDENTS; WORK IN PROGRESS;

CURRICULA; DYNAMICS; EDUCATION COMPUTING; FORECASTING; INNOVATION; NEURAL NETWORKS; RADIAL BASIS FUNCTION NETWORKS; SUPPORT VECTOR MACHINES; TECHNICAL PRESENTATIONS;

STUDENTS;

EID: 84863272451     PISSN: 15394565     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/FIE.2011.6142729     Document Type: Conference Paper

References (4)

1. Imbrie, P. K., J. J. Lin, K. Reid, and A. Malyscheff. "Using Hybrid Data to Model Student Success in Engineering with Artificial Neural Networks," Proceedings of the Research in Engineering Education Symposium, July 7-10, 2008, Davos, Switzerland.
2. Huang, S. and N. Fang. "Prediction of Student Academic Performance in an Engineering Dynamics Course: Development and Validation of Multivariate Regression Models." International Journal of Engineering Education, 2010, Vol. 26, pp. 1008-1017.
3. Han, J. and M. Kamber. Data Mining: Concepts and Techniques. 2007, San Fransisco, CA: Morgan Kaufmann publishers.
4. Schlkopf, B. and A. J. Smola. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. 2001, Boston, MA: The MIT Press.