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Reseach Article

Using KNN Method for Educational and Vocational Guidance

by Essaid El Haji, Abdellah Azmani, Mohamed El Harzli
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 100 - Number 12
Year of Publication: 2014
Authors: Essaid El Haji, Abdellah Azmani, Mohamed El Harzli

Essaid El Haji, Abdellah Azmani, Mohamed El Harzli . Using KNN Method for Educational and Vocational Guidance. International Journal of Computer Applications. 100, 12 ( August 2014), 24-30. DOI=10.5120/17578-8335

@article{ 10.5120/17578-8335,
author = { Essaid El Haji, Abdellah Azmani, Mohamed El Harzli },
title = { Using KNN Method for Educational and Vocational Guidance },
journal = { International Journal of Computer Applications },
issue_date = { August 2014 },
volume = { 100 },
number = { 12 },
month = { August },
year = { 2014 },
issn = { 0975-8887 },
pages = { 24-30 },
numpages = {9},
url = { },
doi = { 10.5120/17578-8335 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:29:48.101386+05:30
%A Essaid El Haji
%A Abdellah Azmani
%A Mohamed El Harzli
%T Using KNN Method for Educational and Vocational Guidance
%J International Journal of Computer Applications
%@ 0975-8887
%V 100
%N 12
%P 24-30
%D 2014
%I Foundation of Computer Science (FCS), NY, USA

This paper presents a decision support tool for educational and vocational guidance, based on the supervised classification method k-nearest neighbors (KNN). This method consists in determining, for each new observation to be classified, the list of nearest neighbors of the observations already classified. The use of the KNN method requires choosing a distance and the most classical one is the Euclidean distance. In the context of this work, two functions were tested to measure resemblance as far as similarity and dissimilarity are concerned.

  1. Davies, S. , & Guppy, N. (1997). Fields of study, college selectivity, and student inequalities in higher education. Social Forces, 75 (4), 1417–1438.
  2. Simpson, J. C. (2001). Segregated by subject: racial differences in the factors influencing academic major between European Americans, Asian Americans, and African, Hispanic, and native Americans. The Jthisnal of Higher Education, 72(1), 63–100.
  3. Galotti, K. M. (1999). Making a "Major"real-life decision: college students choosing an academic major. Jthisnal of Educational Psychology, 91(2), 379–387.
  4. Coperthwaite, C. A. (1994). The effect of self-esteem, locus of control, and background factors on college students' choice of an academic major. Unpublished doctoral dissertation, The University of Connecticut, Connecticut, USA.
  5. Betz, N. E. , & Rottinghaus, P. J. (2006). Current research on parallel measures of interests and confidence for basic dimensions of vocational activity. Jthisnal of Career Assessment, 14(1), 56–76.
  6. Lounsbury, J. W. , Smith, R. M. , Levy, J. J. , Leon, F. T. , & Gibson, L. W. (2009). Personality characteristics of business majors as de fined by the Big Five and narrow personality traits. Jthisnal of Education for Business, 84(4), 200–206.
  7. Leppel, K. (2001). Race, Hispanic ethnicity, and the future of the college business major in the United Sates. Jthisnal of Education for Business, 76(4), 209–215.
  8. Ware, N. C. , Steckler, N. A. , & Leserman, J. (1985). Undergraduate woman: who chooses a science major?Jthisnal of Higher Education, 56(1), 73–84.
  9. ELHAJI. E, AZMANI. A, ELHARZLI. M, Expert system design form educational and vocational guidance, using a multi-agent system, proceeding of the 4th IEEE conference ICMCS'14, 14-16 April 2014 –Marrakech- Morocco.
  10. Francisco Rivas, Tecnología informática en asesoramiento vocacional, Psicothema, 2005
  11. S. A. I. O. – Rectorat de Versailles, Les logiciels d'aide à l'orientation, 2007.
  12. Roger Chappat, "L'informatique comme aide à l'orientation", LE BULLETIN DE L'EPI N° 65.
  13. K. Ming Leung, k-Nearest Neighbor Algorithm for Classi?cation, 2007
  14. Jérôme Azé, K-plus proches voisins, 2007
  15. Eve MATHIEU-DUPAS, Algorithme des K plus proches voisins pondérés (WKNN) et Application en diagnostic, nria-00494814, version 1 - 24 Jun 2010
  16. Klaus Hechenbichler, and Klaus Schliep, "Weighted k-Nearest-Neighbor Techniques and Ordinal Classi?cation", Sonderforschungsbereich 386, Paper 399 (2004).
  17. Jianping Gou and all, "A New Distance-weighted k-nearest Neighbor Classier", Jthisnal of Information & Computational Science 9: 6 (2012) 14291436
  18. J. M. Keller, M. R. Gray, and J. A. Givens. A fuzzy k-nn neighbor algorithm. IEEE Trans. Syst. Man Cybern. , SMC-15(4):580–585, 1985.
  19. COVER T. M. , HART P. E. , Nearest neighbthis pattern classification, IEEE Trans. On Inform. Theory, Vol 13(1) pp. 21-27, 1967.
  20. S. A. Dudani, The Distance-weighted k-Nearest Neighbor Rule. IEEE Trans. Syst. Man Cybern. , 6 (4) (1976), 325 – 327.
  21. Jianping GOU and all, " Weighted K-nearest Centroid Neighbor Classification", thisnal of Computational Information Systems 8: 2 (2012) 851–860.
  22. Yu Takigawa and all, "Pattern classification using weigted average paternnes of categorical K-Nearest Neighbors", Departement of computer ind Information Sciences, Nagasaki University.
  23. DONOEUX T, A k-nearest neighbthis classification rule based on Dempster-Shaffer theory, IEEE Trans. on Systems, Man and Cybernetics, Vol 25(5), pp. 804-813, 1995.
  24. Y. Zeng, Y. Yang, L. Zhao, Pseudo nearest neighbor rule for pattern classi?cation, Expert Systems with Applications, 36 (2009), 3587-3595
  25. Bruno Taconet and all, Classification des k-ppv par sous-voisinages emboîtés, Equipe GED – Université du Havre.
  26. María Luisa López González, "la toma de decisiones en los sistemas de autoayuda y asesoramiento vocacional (sav-r y savi-2000): propuesta y validación de un modelo de decisión vocacional", memoria para optar al grado de doctor , Universidad Complutense de Madrid, Facultad de Psicología, Departamento de Psicología Evolutiva y de la Educación
  27. E. Lebarbier, T. Mary-Huard, Classification non supervisee.
  28. Fatou Kamara-Sangaré, Contribution à la recherche d'information : Une fonction de correspondance, Actes des 1ères Rencontres Jeunes Chercheurs en Recherche d'Information.
  29. Dina Guglielmi, Franco Fraccaroli and Maria Luisa Pombeni, The Professional Interests According to Holland's Hexagonal Model Structures and Gender Difference", OSP, 33/3|2004, p. 409-427.
  30. Biljana Stevanovic and Nicole Mosconi, « Les représentations des métiers des adolescent(e-s) scolarisé(e-s) dans l'enseignement secondaire », Revue française de pédagogie [En ligne], 161 | octobre-décembre 2007.
  31. Lisa M. Larson, Patrick J. Rottinghaus, and Fred H. Borgen, Meta-analyses of Big Six Interests and Big Five Personality Factors, Jthisnal of Vocational Behavior 61, 217–239 (2002).
  32. ELHAJI. E, AZMANI. A, ELHARZLI. M, A pairing individual-trades system, using KNN method: the educational and vocational guidance as a case study, IEEE Conference CIST'14, 20-22 October 2014, Tetuan, Morocco (Paper accepted).
Index Terms

Computer Science
Information Sciences


Educational and vocational guidance RIASEC pairing k-nearest neighbors similarity dissimilarity.