Creating an Educational Roadmap for Engineering Students via an Optimal and Iterative Yearly Regression Tree using Data Mining

Marie Khair, Chady El Moucary, Walid Zakhem

Abstract

Targeting high academic standards is required in engineering studies. Advisors usually play an important role in helping students keeping good records and transcripts along their educational path by helping them choose their courses and keeping track of their grades. However, performance in some courses in the curriculum embodies determining repercussions and might inadvertently jeopardize the overall students’ Grade Point Average (GPA) in an irreversible manner. The purpose of this paper is to draw an educational roadmap that helps advisors and students being aware of the turning points that decisively affect their overall cumulative GPA and act upon a current outcome. This roadmap is based on Classification and Regression Trees where nodes and branches denote the aforementioned courses and students’ performance, respectively, with the ultimate outcome being the overall student’s GPA upon graduation. The tree is constructed based on a relatively large number of records with 10-fold cross-validation and testing. Moreover, the tree is produced on a yearly basis with a twofold objective. The first is to secure a high level of precision by applying it over a short period of time and the second is to allow for injecting each-year computed GPA with the remaining courses as to reflect the actual situation with maximum vraisemblance. This iterative and recursive tree achieves a very close tracking of students’ performance and provide a powerful tool to rectify courses’ track and grades for each student individually while aiming at a predefined final GPA. Furthermore, the choice of the optimal tree was carefully examined in the light of the relatively elevated number of attributes. In this context, diverse models were created and performance and/or precision were computed in terms of different values of “pruning levels” and “splitting criteria”. The choice of the best tree to be adopted for advising is thoroughly explained. Besides, it is shown, in this context, that the structure of the tree remains highly versatile in the sense that it can be revisited at any point for further assessment, adjustment, and expansion. Finally, yet importantly, simulation results were carried out using Matlab CART and demonstrated high efficiency and reasonably precise results.

References

  1. Kovacic, Z., Green, J., 2010, Research in Higher Education Journal, Predictive working tool for early identification of 'at risk' students, Open Polytechnic, Wellington, New Zealand.
  2. Sembiring, S., Zarlis, M., Hartama, D., Ramliana, S., Wani, E., 2011, International Conference on Management and Artificial Intelligence, Prediction of Student Academic Performance by an Application of Data Mining Techniques, IACSIT Press vol. 6.
  3. Wook, M., Yahaya, Y., Wahab, N., Isa, M., Awang, N. F., Seong, H. Y., 2009, In Proceedings of the Second International Conference on Computer and Electrical Engineering, Predicting NDUM Student's Academic Performance Using Data Mining Techniques, IEEE computer society.
  4. Al-Radaideh, Q., Al-Shawakfa, E., Al-Najjar, M. I., 2006, The 2006 International Arab Conference on Information Technology, ACIT'2006, Mining Student Data Using Decision Trees.
  5. Kovacic, Z., 2010, Proceedings of Informing Science & IT Education Conference (InSITE). Early Prediction of Student Success: Mining Students Enrolment Data
  6. Portnoi, L. M., & Kwong, T. M., 2011, Journal of Student Affairs Research and Practice, volume 48, No. 4., Enhancing the academic experiences of firstgeneration master's students.
  7. Oladokun, V. O., Adebanjo, A. T., Charles-Owaba, O. E., 2008, The Pacific Journal of Science and Technology, Volume 9. Number 1., Predicting Students' Academic Performance using Artificial Neural Network: A Case Study of an Engineering Course
  8. Chadha, A., Kumar, V., 2011, International Journal of Advanced Computer Science and Applications, Vol. 2, No. 3., An Empirical Study of the Applications of Data Mining Techniques in Higher Education
  9. Pal, S., Baradwaj, B. K., 2011, International Journal of Advanced Computer Science and Applications, Vol. 2, No. 6., Mining Educational Data to Analyze Students' Performance
  10. Eshghi, A., Haughton, D., Li, M., Senne, L., Skaletsky M., Woolford, S., 2011, Journal of Institutional Research, vol. 16, No.2., Enrolment Management in Graduate Business Programs: Predicting Student Retention
  11. Dowdy, S. Wearden, S., 1983, Statistics for Research, Wiley. ISBN 0471086029 pp 230.
  12. Domingos, P., 2007, Proceedings of Data Min. Knowledge and Discovery, 21-28, Volume 15, Issue 1., Toward Knowledge-Rich Data Mining
  13. Fayyad, U. M.; Piaetsky-Shapiro, G.; Smyth, P., 1996, Advances In Knowledge Discovery and Data Mining, From Data Mining to Knowledge Discovery: An Overview. AAAI/MIT press, Cambridge mass.
  14. Witten, I. H., Frank, E., Hall, M. A., 2011, Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Third Edition.
  15. J., Han, M., Kamber, J., Pei, 2011, Data Mining: Concepts and Techniques, Morgan Kaufmann, Third Edition.
  16. Nock, R., Nielsen, F., 2006, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 28, No. 8, On Weighting Clustering, August 2006, p. 1-13.
  17. Agrawal; R., Imielinski; T., Swami, A., 1993, Proceedings of the ACM SIGMOD International Conference on Management of Data, Mining Association Rules between Sets of Items in Large Databases
  18. Piatetsky-Shapiro, G., 1991, Knowledge Discovery in Databases, Discovery, analysis, and presentation of strong rules, in G. Piatetsky-Shapiro & W. J. Frawley, AAAI/MIT Press, Cambridge, MA.
  19. Denning, D., 1986, IEEE Transactions on Software Engineering - Special issue on computer security and privacy, An Intrusion-Detection Model,
  20. Getoor, L., 2003, ACM SIGKDD Explorations, Vol.5, No.1. , Linking Mining: A New Data Mining Challenge
  21. Silverstein, C., Brin, S., Motwani, R., Ullman, J., 2000, Data Mining and Knowledge Discovery, Volume 4, Numbers 2-3, Scalable Techniques for Mining Causal Structures
  22. Quinlan, J. R., 1986, Machine Learning, 1:81-106, Induction of Decision Trees
  23. Thabtah, F., 2006, Journal of Digital Information Management, vol. 4, no. 3, pp. 197-202., Pruning Techniques in Associative Classification: Survey and Comparison.
  24. Baker, R. S. J. d., Yacef, K., 2009, Journal of Educational Data Mining, vol. 1, issue 1, pp. 3-17., The State of Educational Data Mining in 2009: A Review and Future Visions
  25. Al-Radaideh, Q., Al Ananbeh, A., Al-Shawakfa, E., 2011, IJRAS volume 8, issue 2., A classification model for predicting the suitable study track for school students
  26. Dekker, G., Pechenizki, M., Vleeshouwers, J., 2009, Proceedings of the Educational Data mining. Predicting Students Drop Out: A Case Study
  27. Bardawaj, B. K., Pal, S., 2011, International Journal of Advanced Computer Science and Applications, vol 2, issue 6. Mining Educational Data to Analyze Student's Performance
  28. Kabra R., Bichkar, R., 2011, International Journal of Computer Applications, volume 36, No 11, December. Performance Prediction of Engineering Students using Decision Trees
  29. Noori, S., Bagherpour, M., Zareei, A., 2008, World Applied Sciences Journal 3(4). Applying Fuzzy Control Chart in Earned Value Analysis: A New Application
  30. Hong-yuan, F., Chuang-bin, H., Lang, Z., 2007, Journal of Chongking University. Quality Earned Value Method Based on Quality Evaluation
  31. Camplell, S., 2008, Association of American Colleges and Universities Peer Review, winter 2008, vol. 10, No 1. Academic Advising in the new global Century: Supporting Student Engagement and Learning Outcomes Achievement
  32. Tinto, V., 2012, Taking Student Retention Seriously, retrieved from hxxp://faculty.soe.syr.edu/vtinto/ Files/Taking Student Retention Seriously.pdf, July 2012.
  33. Werghi, N., Kamoun, F., 2010, International Journal of Business Information Systems, Vol. 5, No. 1, pp 1-18. A Cecision-Tree Based System for Student Academic Advising and Planning in Information Systems Programmes
  34. Leu S., Lin Y., 2008, Journal of Construction Engineering and Management. Project Performance Evaluation Based on Statistical Process Control Techniques
  35. Henderson K., 2004, The Measurable News. Further Developments in Earned Schedule
  36. Wu X., Kumar V., 2009, The Top Ten Algarithms in Data Mining, CRC Press, Taylor & Francis Group.
Download


Paper Citation


in Harvard Style

Khair M., El Moucary C. and Zakhem W. (2012). Creating an Educational Roadmap for Engineering Students via an Optimal and Iterative Yearly Regression Tree using Data Mining . In Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2012) ISBN 978-989-8565-30-3, pages 43-52. DOI: 10.5220/0004130300430052


in Bibtex Style

@conference{keod12,
author={Marie Khair and Chady El Moucary and Walid Zakhem},
title={Creating an Educational Roadmap for Engineering Students via an Optimal and Iterative Yearly Regression Tree using Data Mining},
booktitle={Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2012)},
year={2012},
pages={43-52},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004130300430052},
isbn={978-989-8565-30-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2012)
TI - Creating an Educational Roadmap for Engineering Students via an Optimal and Iterative Yearly Regression Tree using Data Mining
SN - 978-989-8565-30-3
AU - Khair M.
AU - El Moucary C.
AU - Zakhem W.
PY - 2012
SP - 43
EP - 52
DO - 10.5220/0004130300430052