Using data mining to predict secondary school student performance

15th European Concurrent Engineering Conference 2008, ECEC 2008 - 5th Future Business Technology Conference, FUBUTEC 2008

Volumn , Issue , 2008, Pages 5-12

  Cortez, Paulo ^a   Silva, Alice ^a  

^a UNIVERSITY OF MINHO   (Portugal)

Author keywords

Business intelligence in education; Classification and regression; Decision trees; Random forest

Indexed keywords

CONCURRENT ENGINEERING; DATA MINING; DECISION TREES; EDUCATION; EDUCATION COMPUTING; FORESTRY; INFORMATION ANALYSIS; POPULATION STATISTICS; SET THEORY;

ALCOHOL CONSUMPTION; HIGH LEVEL KNOWLEDGE; PORTUGUESE LANGUAGES; PORTUGUESE POPULATION; PREDICTIVE ACCURACY; QUALITY OF EDUCATION; RANDOM FORESTS; STUDENT ACHIEVEMENT;

STUDENTS;

ARTIFICIAL INTELLIGENCE; DATA PROCESSING; DECISION MAKING; EDUCATION; PORTUGAL; STATISTICS;

EID: 84961731164     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (15)

1. Breiman L., 2001. Random Forests. Machine Learning, 45, no. 1, 5-32.
2. Breiman L.; Friedman J.; Ohlsen R.; and Stone C., 1984. Classification and Regression Trees. Wadsworth, Monterey, CA.
3. Cortez P., In press. RMiner: Data Mining with Neural Networks and Support Vector Machines using R. In R. Rajesh (Ed.), Introduction to Advanced Scientific Softwares and Toolboxes.
4. Eurostat, 2007.Early school-leavers. http://epp.eurostat.ec.europa.eu/.
5. Flexer A., 1996. Statistical Evaluation of Neural Networks Experiments: Minimum Requirements and Current Practice. In Proceedings of the 13th European Meeting on Cybernetics and Systems Research. Vienna, Austria, vol. 2, 1005-1008.
6. Hastie T.; Tibshirani R.; and Friedman J., 2001. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer-Verlag, NY, USA.
7. Kotsiantis S.; Pierrakeas C.; and Pintelas P., 2004. Predicting Students' Performance in Distance Learning Using Machine Learning Techniques. Applied Artificial Intelligence (AAI), 18, no. 5, 411-426.
8. Luan J., 2002. Data Mining and Its Applications in Higher Education. New Directions for Institutional Research, 113, 17-36.
9. Ma Y.; Liu B.; Wong C.; Yu P.; and Lee S., 2000. Targeting the right students using data mining. In Proc. of 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Boston, USA, 457-464.
10. Minaei-Bidgoli B.; Kashy D.; Kortemeyer G.; and Punch W., 2003. Predicting student performance: An application of data mining methods with an educational web-based system. In Proc. of IEEE Frontiers in Education. Colorado, USA, 13-18.
11. Pardos Z.; Heffernan N.; Anderson B.; and Heffernan C., Using Fine-Grained Skill Models to Fit Student Performance with Bayesian Networks. In Proc. of 8th Int. Conf on Intelligent Tutoring Systems. Taiwan.
12. Pritchard M. and Wilson S., 2003. Using Emotional and Social Factors To Predict Student Success. Journal of College Student Development, 44, no. 1, 18-28.
13. R Development Core Team, 2006. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, ISBN 3900051-00-3, http://www.R-project.org.
14. Turban E.; Sharda R.; Aronson J.; and King D., Business Intelligence, A Managerial Approach. Prentice-Hall.
15. Witten I. and Frank E., 2005. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco, CA.