The impact of economic plans on the Chinese education system: a machine learning approach

Titolo Rivista CADMO
Autori/Curatori Wenjun Lin, Xuefu Xu, Francesco Dell’Anna
Anno di pubblicazione 2018 Fascicolo 2018/1
Lingua Inglese Numero pagine 13 P. 37-49 Dimensione file 204 KB
DOI 10.3280/CAD2018-001005
Il DOI è il codice a barre della proprietà intellettuale: per saperne di più clicca qui

Qui sotto puoi vedere in anteprima la prima pagina di questo articolo.

Se questo articolo ti interessa, lo puoi acquistare (e scaricare in formato pdf) seguendo le facili indicazioni per acquistare il download credit. Acquista Download Credits per scaricare questo Articolo in formato PDF

Anteprima articolo

FrancoAngeli è membro della Publishers International Linking Association, Inc (PILA)associazione indipendente e non profit per facilitare (attraverso i servizi tecnologici implementati da CrossRef.org) l’accesso degli studiosi ai contenuti digitali nelle pubblicazioni professionali e scientifiche

This paper investigates the effects of Chinese economic plans on the academic education system. To quantitatively illustrate the outcomes of investment plans on the Chinese contemporarily society, the conducted study proposes a mathematical model for the employability of graduated students. The proposed technique consists in an averaging intermodel ensemble of several neural networks trained on a labeled dataset categorized as follows: age and gender, study program and academic achievements, willingness to work in the considered industry sectors and, public investments on the considered industry fields. The supervisory signal chosen for the analysis is the ratio between graduated and employed students. The attained models clearly jibe on the importance of investments in each working sector as the most impactful parameter affecting the student employability.

Keywords:Planned economic growth; Chinese education system; employ¬ability analysis; supervised machine learning; inter-model ensemble.

  1. Aksoy, S., Haralick, R. (2001), “Feature Normalization and Likelihood-based Similarity Measures for Image Retrieval”, Pattern Recognition Letters, 22 (5), pp. 563-582.
  2. Cameron, A.C., Windmeijer, F.A.G. (1997). “An R-squared Measure of Goodness of fit for Some Common Nonlinear Regression Models”, Journal of Econometrics, 77 (2), pp. 329-342.
  3. Chongqing investment plan (2013), -- http://www.cqtj.gov.cn/tjnj/2014/indexch.htm, accessed 7/2/2018.
  4. Chongqing investment plan (2014), -- http://www.cqtj.gov.cn/tjnj/2015/indexch.htm, accessed 7/2/2018.
  5. Chongqing investment plan (2015),-- http://www.cqtj.gov.cn/tjnj/2016/indexch.htm, accessed 7/2/2018.
  6. Chongqing investment plan (2016), -- http://www.cqtj.gov.cn/tjnj/zk/2016/indexch, accessed 7/2/2018.
  7. Ephraim, Y., Malah, D. (1984), “Speech Enhancement using a Minimum-mean Square Error Short-time Spectral Amplitude Estimator”, IEEE Transactions on Acoustics, Speech, and Signal Processing, 32 (6), pp. 1109-1121.
  8. Jantawan, B., Tsai, C.F. (2013), The Application of Data Mining to build Classification Model for predicting Graduate Employment,-- https://arxiv.org/abs/1312.7123.
  9. Brown, M. (2009), “Chongqing, the World’s Fastest Growing City”, The Telegraph, -- https://www.telegraph.co.uk/news/worldnews/northamerica/usa/6207204/Chongqing-the-worlds-fastest-growing-city.html, accessed 7/2/2018.
  10. Mishra, T., Kumar, D., Gupta, S. (2016), “Students’ Employability Prediction Model through Data Mining”, International Journal of Applied Engineering Research, 11 (4), pp. 2275-2282.
  11. Rahman, N.A.A., Tan, K.L, Lim, C.K (2017), “Predictive Analysis and Data Mining among the Employment of Fresh Graduate Students in HEI”, in AIP Conference Proceedings 1891, 020007. 1, https://doi.org/10.1063/1.5005340.
  12. Rasul, M.S., Abd Rauf, R.A., Mansor, A.N., Yasin, R.M., Mahamod, Z. (2013), “Graduate Employability for Manufacturing Industry”, Procedia-Social and Behavioral Sciences, 102, pp. 242-250.
  13. Rasul, M.S., Rauf, R.A.A., Mansor, A.N., Yasin, R.M., Mahamod, Z. (2012), “Perceived Employability and Competence Development”, Procedia-Social and Behavioral Sciences, 69, pp. 1191-1197.
  14. Sapaat, M.A., Mustapha, A., Ahmad, J., Chamili, K., Muhamad, R. (2011), “A Data Mining Approach to Construct Graduates Employability Model in Malaysia”, International Journal of New Computer Architectures and their Applications (IJNCAA), 1 (4), pp. 1086-1098.
  15. Sung, A.H. (1998), “Ranking Importance of Input Parameters of Neural Networks”, Expert Systems with Applications, 15 (3-4), pp. 405-411. Thakar, P., Mehta, A., Manisha et al. (2017), “A Unified Model of Clustering and Classification to improve Students’ Employability Prediction”, International Journal of Intelligent Systems and Applications, 9 (9), p. 10.
  16. Wijayapala, M.P., Premaratne, L, Jayamanne, I.T. (2016), “Employability and Related Context Prediction Framework for University Graduands: A Machine Learning Approach”, ICTer, 9 (2), -- http://journal.icter.org/index.php/ICTer/article/view/217.

Wenjun Lin, Xuefu Xu, Francesco Dell’Anna, The impact of economic plans on the Chinese education system: a machine learning approach in "CADMO" 1/2018, pp 37-49, DOI: 10.3280/CAD2018-001005