Open Access
Subscription Access
Open Access
Subscription Access
Best Practices for Adaptation of Data Mining Techniques in Education Sector
Subscribe/Renew Journal
Best practices help to make the business processes smooth. As best practices are always recommended and not forced, the authors have recommended few best practices to be followed in Educational institutes so that the activities related to educational data mining becomes easy to implement. The best practices suggested are with the objective to gain and maintain data quality; as quality data leads to correct analysis.
Keywords
Educational Data Mining, Best Practices, Data Quality
Subscription
Login to verify subscription
User
Font Size
Information
- Anjewierden, A., Kolloffel B., and Hulshof, C. (2007). Towards educational data mining: Using data mining methods for automated chat analysis to understand and support inquiry learning processes, In Proceeding of International Workshop on Applying Data Mining in e-Learning (ADML’07), pp.23-32.
- Baker, R. and Carvalho, D. (2008). Labeling Student Behavior Faster and More Precisely with Text Replays, In Proceedings of the 1st International Conference on Educational Data Mining, pp.38-47.
- Ben-Zadoki, G., et. al. (2009). Examining online learning processes based on log files analysis: A case study, In Research, Reflections and Innovations in Integrating ICT in Education (Ed. A. Méndez-Vilas,et.al.), FORMATEX, pp.55-59.
- Chapman, P., et. al. CRISP-DM 1.0, Step-by-step data mining guide, SPSS, CRISPDM Consortium, http://www.crisp-dm.org/download.htm, accessed on Oct 2009.
- Eckerson, W., Data Quality and the Bottom Line, TDWI Report Series, 2002.
- Eckerson, W., Excerpt from TDWI’s Research Report - Data Quality and the Bottom Line, Business Intelligence Journal, Dec 2001,
- http://www.tdwi.org/research/display.aspx?ID=6589 accessed on Dec 2009. 7. Examination Reforms and Continuous and Comprehensive Evaluation (CCE) in CBSE, http://www.cbse.nic.in/cce/index.html, accessed on Dec 2009.
- Han, J. and Kamber M. (2001). Data Mining: Concepts and Techniques, San Francisco, Morgan Kaufmann.
- Jeong, H., and Biswas, G.(2008). Mining Student Behavior Models in Learning by- Teaching Environments, In Proceedings of the 1st International Conference on Educational Data Mining, pp.127-136.
- Lloyd, N., Heffernan, N. and Ruiz C. (2007). Predicting student engagement in intelligent tutoring systems using teacher expert knowledge, Supplementary Proceedings of the 13th International Conference of Artificial Intelligence in Education.pp.40-49.
- Mavrikis, M. (2008). Data-driven modelling of students' interactions in an ILE, In Proceedings of the 1st International Conference on Educational Data Mining, pp.87- 96.
- Sacin, C., Agapito J., et.al.(2009). Recommendation in Higher Education Using Data Mining Techniques, Proceedings of 2nd International Conference on Educational Data Mining, Spain
- Sheth, J., Patel B., and Bhatti, D. (2010). Improper Internet Usage: Controlling through Policy Model and Identifying through Data Mining, National Journal of Computer Science & Technology, Vol. 02(1), pp.16-21
- Srivastava, J., Cooleyz, R., et. al. (2000). Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data, ACM SIGKDD, Vol. 01(2), pp.12-22. 15. Tang, Z., Maclennan, J. (2005). Data mining with SQL Server 2005, Wiley Publications.
Abstract Views: 451
PDF Views: 2