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International Journal of Information Technology

, Volume 10, Issue 4, pp 511–518 | Cite as

Identification of essential parameters for post graduate students’ job placement in computer applications in India

  • Chirag Patel
Original Research
  • 802 Downloads

Abstract

India is considered as the youngest country in the world as it is having the highest numbers of young people in the age range of 20–40 years. It is very important for a young student to get the job after finishing the study. After the announcement of Digital India, the Post Graduate (PG) students in computer applications have enormous opportunities in getting job placement in this domain. To get absorbed in these companies easily, a student must acquire certain essential attributes. In this research, we have collected data of students of computer applications discipline who have completed their PG course and those who are doing the study in the final semester of PG course. These data contain the various details such as whether the student is placed or not, salary package of the placed student, apart from a PG degree any additional certificate obtained by that student and many other related parameters. A survey through Google form is also conducted to extract the fundamental knowledge of the placed and the un-placed students. So these data are processed and essential parameters for the job placement of a PG student are extracted and unwanted parameters are filtered. In the paper all the possible parameters or attributes to get the job are discussed and essential parameters are selected based on the processed data.

Keywords

Job placement CGPA Under graduation (UG) Post graduation (PG) Computer applications 

Notes

Acknowledgements

The authors thank the Charotar University of Science and Technology (CHARUSAT) for providing the necessary resources to accomplish this research.

References

  1. 1.
    PM Arsad, N Buniyamin, and J-l Ab Manan (2013) Prediction of engineering students’ academic performance using artificial neural network and linear regression: a comparison. IEEE 5th conference on engineering education (ICEED), pp 43–48Google Scholar
  2. 2.
    ElDen AS, Moustafa MA, Harb HM, Emara AH (2013) AdaBoost ensemble with simple genetic algorithm for student prediction model. Int J Comput Sci Inf Technol (IJCSIT) 5(2):73–85Google Scholar
  3. 3.
    Chirumamilla Vikas, Bhagya ST, Velpula S, Sunkara I (2014) A Novel approach to predict student placement chance with decision tree induction. Int J Syst Technol 7(1):78–88Google Scholar
  4. 4.
    JR Castillo, A Ghadah, HM Fardoun (2013) Towards improved student placement and preparation methods on information technologies post-secondary education. Federated conference on computer science and information systems, pp 689–693Google Scholar
  5. 5.
    ON Pratiwi (2013) Teknik Informatika, predicting student placement class using data mining. IEEE international conference on teaching, assessment and learning for engineering (TALE), pp 618–621Google Scholar
  6. 6.
    AS Sharma, S Prince, S Kapoor, K Kumar (2014) PPS—placement prediction system using logistic regression. IEEE international conference on MOOC, innovation and technology in education (MITE), pp 337–341Google Scholar
  7. 7.
    Pal AK, Pal S (2014) Classification model of prediction for placement of students. Int J Modern Educ Comput Sci 5(11):49–56Google Scholar
  8. 8.
    Ramanathan L, Swarnalatha P, Ganesh Gopal D (2014) Mining educational data for students’ placement prediction using sum of difference method. Int J Comput Appl 99(18):36–39Google Scholar
  9. 9.
    Freund Y, Schapire RE (1999) A Short Introduction to Boosting. J Japanese Soc Artif Intell 14(5):771–780Google Scholar
  10. 10.
    K Lin, R Yan, H Duan, J Yao, C Zhou (2008) Objective classification using advanced adaboost algorithm. Fifth international conference on fuzzy systems and knowledge discovery, pp 525–529Google Scholar
  11. 11.
    T-K An, M-H Kim (2010) A new diverse adaboost classifier. International conference on artificial intelligence and computational intelligence, pp 359–363Google Scholar
  12. 12.
    Worcester Polytechnic Institute. [Online]. https://www.assistments.org/. Accessed 17 Aug 2017
  13. 13.
    Silva E, Almeida J, Martins A, Baptista JP, Neves BC (2014) Master’s in autonomous systems: an overview of the robotics curriculum and outcomes at ISEP, Portugal. IEEE Trans Educ 56(1):98–102CrossRefGoogle Scholar
  14. 14.
    Dietrich Suzanne W, Goelman Don, Borro Connie M, Crook Sharon M (2015) An animated introduction to relational databases for many majors. IEEE Trans Educ 8(2):81–89CrossRefGoogle Scholar
  15. 15.
    Bharadwaj BK, Pal S (2011) Mining educational data to analyze students’ performance. Int J Adv Comput Sci Appl (IJACSA) 2(6):63–69Google Scholar
  16. 16.
    Lawrence F, Katz LF, Krueger AB, Levitt S, Poterba J (2007) What does performance in graduate school predict? Graduate economics education and student outcomes. Am Econ Rev 97(2):512–518CrossRefGoogle Scholar
  17. 17.
    Anupama Kumar S, Vijayalakshmi MN (2011) Implication of classification techniques in predicting student’s recital. Int J Data Mining Knowl Manag Process (IJDKP) 1(5):41–51CrossRefGoogle Scholar
  18. 18.
    Nandeshwar A, Chaudhari S (2009) Enrollment prediction models using data mining. http://nandeshwar.info/wpcontent/uploads/2008/11/DMWVU_Project.pdf. Accessed 12 Aug 2017
  19. 19.
    Sudheep E, Idikkula SM, Alexander J (2011) A generalized data mining framework for placement chance prediction problems. Int J Comput Appl 31(3):40–47Google Scholar
  20. 20.
    MK Pilotte, D Bairaktarova (2014) Engineering person-thing orientation: comparisons between first-year students and practicing engineers with implications for retention and professional placement. Frontiers in education conference on computer science and information systems, pp 75–77Google Scholar
  21. 21.
    Jeevalatha T, Ananthi N, Saravana Kumar D (2014) Performance analysis of undergraduate students placement selection using decision tree algorithms. Int J Comput Appl 108(15):27–31Google Scholar

Copyright information

© Bharati Vidyapeeth's Institute of Computer Applications and Management 2018

Authors and Affiliations

  1. 1.Smt. Chandaben Mohanbhai Patel Institute of Computer Applications, CHARUSATChanga, AnandIndia

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