体育赛事投注记录

体育赛事投注记录advertisement

Data Mining Model for Better Admissions in Higher Educational Institutions (HEIs)—A Case Study of Bahrain

  • Subhashini Sailesh BhaskaranEmail author
  • Mansoor Al Aali
Conference paper
  • 88 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1141)

Abstract

体育赛事投注记录data mining has been used for a variety of objectives for improving the quality of higher education institutions and especially for improving students’ performance and institution quality. the use of data mining for assessing prior learning and for improving the admission criteria has not been addressed extensively. guiding applicants to select the correct and most suitable degree based on their prior learning at their previous institution is of great importance. we present in this paper our approach of using data mining for guiding applicants to decide the correct and most suitable degree based on their prior learning at their previous institution, and the results demonstrate the success of this method and confirm the expected benefits for the students and the institutions. the c4.5 decision tree algorithm is applied on successfully graduated student’s prior learning data along with the gpa and programme in hei in order to predict the programme of new applicants/students of similar prior learning characteristics. the outcome of the decision tree predicted the list of appropriate programmes with the gpa expected if registered in that programme, for the applicants from similar prior learning attributes. the decision rules present a list of choices of programmes to which new students can enrol with a hint of the success level expected in terms of gpa, which gives a forecast/projection on the success level that can be expected at the end of the study tenure. furthermore, this knowledge can be used by advisors in preventing a student from enrolling to an inappropriate programme which would make the student fail from graduating.

Keywords

Admissions Data mining Higher Educational Institutions (HEIs) 

References

  1. 1.
    Koslowski III, F.A.: Quality and assessment in context: a brief review. Qual. Assur. Educ. 14(3), 277–288 (2006)
  2. 2.
    Hill, F.M.: Managing service quality in higher education: the role of the student as primary consumer. Qual. Assur. Educ. 3(3), 1–11 (1995)
  3. 3.
    Russell, M.: Marketing education: a review of service quality perceptions among international students. Int. J. Contemp. Hosp. Manag. 17(1), 65–77 (2005)
  4. 4.
    Mizikaci, F.: A system approach to program Evaluation model for quality in higher education. Qual. Assur. Educ. 14(1), 37–53 (2006)
  5. 5.
    Snipes, R.L., Thomson, N.L., Oswald, S.L.: Gender bias in customer evaluation of service quality: an empirical investigation. J. Serv. Mark. 24(4), 274–284 (2006)
  6. 6.
    Andersson, P., Harris, J.: Re-theorising the Recognition of Prior Learning. National Institute of Adult Continuing Education, Leicester, UK (2006)
  7. 7.
    Kane, M.T.: Validation. In: Brennan, R.L. (ed.) Educational Measurement, 4th edn, pp. 17–64. The National Council on Measurement in Education & the American Council on Education, Washington, DC (2006)
  8. 8.
    Arsad, P.M., Buniyamin, N., Manan, J.-L.A., Hamzah, N.: Proposed academic students’ performance prediction model: a Malaysian case study. In: 3rd International Congress on Engineering Education (ICEED) (2011)
  9. 9.
    Mohsin, M.F.M., Norwawi, N.M., Hibadullah, C.F., Wahab, M.H.A.: Mining the student programming performance using rough set. In: Paper Presented at the International Conference on Intelligent Systems and Knowledge Engineering (ISKE), 15–16 Nov 2010
  10. 10.
    Akinola, O.S., Akinkunmi, B.O., Alo, T.S.: A data mining model for predicting computer programming proficiency of computer science undergraduate students. Afr. J. Comput. ICT 5(1), 43–52 (2012)
  11. 11.
    Cortez, P., Silva, A.: Using data mining to predict secondary school student performance. In: Proceedings of 5th Future Business Technology Conference, Oporto, Portugal (2008)
  12. 12.
    Ramaswami, M., Bhaskaran, R.: A CHAID based performance prediction model in educational data mining. Int. J. Comput. Sci. Iss. (IJCSI) 7(1), 10–18 (2010)
  13. 13.
    Adelman, C.: Answers in the Tool Box: Academic Intensity, Attendance Patterns, and Bachelor’s Degree Attainment. U.S. Dept. of Education Office of Educational Research and Improvement, Washington, DC (1999)
  14. 14.
    Berkner, L., He, S., Forrest, E.: Descriptive Summary of 1995–96 Beginning Post Secondary Students: Six Years Later, NCES 2003-151. U.S. Department of Education, National Center for Education Statistics, Washington, DC (2002)
  15. 15.
    Astin, A.W.: How “good” is your institution’s retention rate? Res. High. Educ. 38(6), 647–658 (1997)
  16. 16.
    Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From Data Mining to Knowledge Discovery in Databases. American Association for Artificial Intelligence, p. 3754 (1997)
  17. 17.
    Baker, R.S.J.D.: Data mining for education. In: McGaw, B., Peterson, P., Baker, L. (ed.) International Encyclopedia of Education, 3rd ed. Elsevier, Oxford (2009)
  18. 18.
    Romero, C., Ventura, S.: Educational data mining: a survey from 1995 to 2005. Expert. Syst. Appl. 33(1), 135–146 (2007)

Copyright information

© Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  • Subhashini Sailesh Bhaskaran
    • 1
    Email author
  • Mansoor Al Aali
    • 1
  1. 1.Ahlia UniversityManamaBahrain

Personalised recommendations