Using Students’ Data to Improve the Quality of the Education in Moroccan Institution

Mohammed Aitdaoud, Khalil Namir, Mohssine Bentaib, Rachida Ihya, Soufiane Bouiti, Mohammed Talbi

Abstract


The goal of every company and public sector organization is to provide quality service to their customers and make them satisfied. However, as we move to-wards a more connected world where technology has been integrated into the business process, handling data has become more complicated. Today, businesses and High School Institutions (HSI) face one of the biggest challenge, which is characterized by the exponential growth of data storage in various formats such as plain text, relational database, etc.

This massive data can be used to improve decision making and management, which requires proper extracting and cleaning methods. For that reason, data warehousing has become a major step in the knowledge discovery in databases (KDD) process which can guarantee a solid description of concepts and methods for transforming transactional data into analytical data formats. The aim of this paper is to provide a way to support and understand the educational processes of a HSI by offering a new description to data and making it more venerable using visualization techniques. We used four different datasets for this study throughout the years (from 2012 to 2016), which was collected from a HSI Enterprise resource planning (ERP) database.


Full Text:

PDF

References


M. Chalaris, S. Gritzalis, M. Maragoudakis, C. Sgouropoulou, and A. Tsolakidis, “Improving quality of edu-cational processes providing new knowledge using data min-ing techniques,” Procedia-Soc. Behav. Sci., vol. 147, pp. 390–397, 2014.

R. Baker, “Data mining for education,” Int. Encycl. Educ., vol. 7, no. 3, pp. 112–118, 2010.

S. L. Prabha and A. M. Shanavas, “Educational data mining applications,” Oper. Res. Appl. Int. J., vol. 1, no. 1, pp. 23–29, 2014.

C. Romero and S. Ventura, “Educational data mining: A sur-vey from 1995 to 2005,” Expert Syst. Appl., vol. 33, no. 1, pp. 135–146, 2007.

Y. Ma, B. Liu, C. K. Wong, P. S. Yu, and S. M. Lee, “Target-ing the right students using data mining,” in Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, 2000, pp. 457–464.

A. El-Halees, “Mining students data to analyze e-Learning behavior: A Case Study,” Dep. Comput. Sci. Islam. Univ. Ga-za PO Box, vol. 108, 2009.

W. H. Inmon, Building the data warehouse. John wiley & sons, 2005.

B. Minaei-Bidgoli, D. A. Kashy, G. Kortemeyer, and W. F. Punch, “Predicting student performance: an application of data mining methods with an educational web-based system,” in Frontiers in education, 2003. FIE 2003 33rd annual, 2003, vol. 1, p. T2A–13.

K. Shyamala and S. Rajagopalan, “Data mining model for a better higher educational system,” Inf. Technol. J., vol. 5, no. 3, pp. 560–564, 2006.

P. A. Bernstein and L. M. Haas, “Information integration in the enterprise,” Commun. ACM, vol. 51, no. 9, pp. 72–79, 2008.

R. Kimball and M. Ross, The Kimball Group Reader: Relent-lessly Practical Tools for Data Warehousing and Business In-telligence Remastered Collection. John Wiley & Sons, 2015.

R. Kimball and M. Ross, The data warehouse toolkit: the complete guide to dimensional modeling. John Wiley & Sons, 2011.

M. Binder et al., “On analyzing process compliance in skin cancer treatment: An experience report from the evidence-based medical compliance cluster (ebmc2),” in International Conference on Advanced Information Systems Engineering, 2012, pp. 398–413.

J. Mundy and W. Thornthwaite, The Microsoft data ware-house toolkit: with SQL Server 2008 R2 and the Microsoft Business Intelligence toolset. John Wiley & Sons, 2011.

P. Vassiliadis, A. Simitsis, and S. Skiadopoulos, “Conceptual modeling for ETL processes,” in Proceedings of the 5th ACM international workshop on Data Warehousing and OLAP, 2002, pp. 14–21.

M. Levene and G. Loizou, “Why is the snowflake schema a good data warehouse design?,” Inf. Syst., vol. 28, no. 3, pp. 225–240, 2003.

P. Stephenson, Official (ISC) 2® Guide to the CCFP CBK. CRC Press, 2014.

S. Simoff, M. H. Böhlen, and A. Mazeika, Visual data min-ing: theory, techniques and tools for visual analytics, vol. 4404. Springer Science & Business Media, 2008.


Refbacks

  • There are currently no refbacks.


Abava  Absolutech IT-EDU 2019

ISSN: 2307-8162