International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
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Tracing Students Performance by Intervention of the Academic Advisor

  • Mohamed, Abdelmoneim Ali (Department of Mathematics, College of Science and Humanities at Alghat, Majmaah University) ;
  • Nafie, Faisal Mohammed (Department of Computer Science, College of Science and Humanities at Alghat, Majmaah University)
  • 투고 : 2021.12.05
  • 발행 : 2021.12.30
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초록

Data mining technique was used to track student's performance during years studding in college and determine the impact of GPA_SEC on the GPA student rates according to the current academic advising method used on student's status. The study utilized a sample of 5436 individuals were drawn from two colleges in Majmaah University, KSA during 2013-2018 period. The results showed that the student's completion status in terms of graduation, dropout, Stumbling or dismissed was classified according to the average grades of admission from secondary school GPA_SEC. The results show the effect of the current academic advising that most of students gain less grades comparing with GPA_SEC in addition that the higher GPA_SEC was the higher graduation, dropout and dismissed decreased when GPA_SEC was high.. Therefore, the study recommends tracking students academically to evaluate their results of each semester to find out the causes of the deficiencies and addressing them within the departments.

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과제정보

The authors would like to thank the Deanship of Scientific Research at Majmaah University for supporting this work under project number No. (R-2021-292).

참고문헌

  1. F. M., Nafie, and A. A. M. Hamed, "Using Data Mining Techniques in Building a Model to Determine the Factors Affecting Academic Data for Undergraduate Students", IJCSNS, vol.21, pp.306-312, 2021.
  2. M. H. Mohamed, & Waguih, H. M., "A proposed academic advisor model based on data mining classification techniques",International Journal of Advanced Computer Research, vol.8, pp.129-136,2018 https://doi.org/10.19101/ijacr.2018.836003
  3. A. V. Manjarres, L. G. M. Sandoval, and M. S. Suarez, "Data mining techniques applied in educational environments: Literature review," Digit. Educ. Rev., vol. 33, pp. 235-266, 2018.
  4. C. Romero and S. Ventura, "Educational data mining: a review of the state of the art," IEEE Trans. Syst. Man, Cybern. Part C (Applications Rev., vol. 40, pp. 601-618, 2010. https://doi.org/10.1109/TSMCC.2010.2053532
  5. B. K. Baradwaj and S. Pal, "Mining educational data to analyze students' performance," arXiv Prepr. arXiv1201.3417, vol.2, pp.63-69, 2012.
  6. M. Letseka and S. Maile, "High university drop-out rates: A threat to South Africa's future", Human Sciences Research Council Pretoria, 2008.
  7. L. Aulck, N. Velagapudi, J. Blumenstock, and J. West, "Predicting student dropout in higher education," arXiv Prepr. arXiv1606.06364, vol.4, pp.16-20, 2016. https://doi.org/10.48550/arXiv.1606.06364
  8. I. Madjar, E. A. McKinley, M. Deynzer, and A. der Merwe, "Stumbling blocks or stepping stones? Students' experience of transition from low-mid decile schools to university," 2010.
  9. F., Ahmad, N. Ismail, and A.A. Aziz, "The prediction of students' academic performance using classification data mining techniques". Applied Mathematical Sciences, vol. 9, pp. 6415-6426, 2015. https://doi.org/10.12988/ams.2015.53289
  10. L., Aulck, N., Velagapudi, J., Blumenstock, & J., West, "Predicting student dropout in higher education", arXiv preprint arXiv: 1606.06364, pp.16-20, 2016. https://doi.org/10.48550/arXiv.1606.06364
  11. R. Simpson, "Changing how we think about advising online students: One-stop student service advising model," Coll. Univ., vol. 93, pp. 2-9, 2018.
  12. A., Shahiri, M. and W. Husain, "A review on predicting student's performance using data mining techniques", Procedia Computer Science, vol.72, pp. 414-422, 2015 https://doi.org/10.1016/j.procs.2015.12.157
  13. V., Dahiya, "A survey on educational data mining", International Journal of Research in Humanities, Arts and Literature, vol. 6, 23-30, 2018