Full-text resources of PSJD and other databases are now available in the new Library of Science.
Visit https://bibliotekanauki.pl

PL EN


Preferences help
enabled [disable] Abstract
Number of results
2016 | 129 | 5 | 1071-1076

Article title

Key Courses of Academic Curriculum Uncovered by Data Mining of Students' Grades

Content

Title variants

Languages of publication

EN

Abstracts

EN
Learning is a complex cognitive process that depends not only on an individual capability of knowledge absorption but it can be also influenced by various group interactions and by the structure of an academic curriculum. We have applied methods of statistical analyses and data mining (principal component analysis and maximal spanning tree) for anonymized students' scores at Faculty of Physics, Warsaw University of Technology. A slight negative linear correlation exists between mean and variance of course grades, i.e. courses with higher mean scores tend to possess a lower scores variance. There are courses playing a central role, e.g. their scores are highly correlated to other scores and they are in the centre of corresponding maximal spanning trees. Other courses contribute significantly to students' score variance as well to the first principal component and they are responsible for differentiation of students' scores. Correlations of the first principal component to courses' mean scores and scores variance suggest that this component can be used for assigning ECTS points to a given course. The analysis is independent of declared curricula of considered courses. The proposed methodology is universal and can be applied for analysis of students' scores and academic curriculum at any faculty.

Keywords

EN

Year

Volume

129

Issue

5

Pages

1071-1076

Physical description

Dates

published
2016-05

Contributors

author
  • Faculty of Physics, Center of Excellence for Complex Systems Research, Warsaw University of Technology, Koszykowa 75, PL-00662 Warsaw, Poland
  • Faculty of Physics, Center of Excellence for Complex Systems Research, Warsaw University of Technology, Koszykowa 75, PL-00662 Warsaw, Poland
author
  • Faculty of Physics, Center of Excellence for Complex Systems Research, Warsaw University of Technology, Koszykowa 75, PL-00662 Warsaw, Poland
  • ITMO University, 19, Kronverkskiy av., 197101 Saint Petersburg, Russia

References

  • [1] J. Mulhern, A History of Education: A Social Interpretation, 2nd ed., Ronald Press Co., New York 1959
  • [2] C. Marquez-Vera, A. Cano, C. Romero, A.Y.M. Noaman, H. Mousa Fardoun, S. Ventura, Expert Syst. 33, 107 (2016), doi: 10.1111/exsy.12135
  • [3] C. Romero, S. Ventura, IEEE Trans. Syst. Man Cybern. Central Appl. Rev. 40, 601 (2010), doi: 10.1109/TSMCC.2010.2053532
  • [4] J. Kay, P. Reimann, E. Diebold, B. Kummerfeld, IEEE Intell. Syst. 28, 70 (2013), doi: 10.1109/MIS.2013.66
  • [5] University Study-Oriented System USOS (on-line; accessed 2016.03.07) http://usos.edu.pl
  • [6] G. Wang, C. Xie, S. Chen, J. Yang, M. Yang, Phys. A 392, 3715 (2013), doi: 10.1016/j.physa.2013.04.027
  • [7] J. Speth, S. Drożdż, F. Grümmer, Nucl. Phys. A 844, 30c (2010), doi: 10.1016/j.nuclphysa.2010.05.010
  • [8] T.K. Dal'Maso Peron, F.A. Rodrigues, Euro Phys. Lett. 96, 48004 (2011), doi: 10.1209/0295-5075/96/48004
  • [9] A.Z. Górski, S. Drożdż, J. Kwapień, Euro Phys. Lett. B 66, 91 (2008), doi: 10.1140/epjb/e2008-00376-5
  • [10] M. Tumminello, T. Aste, T. Di Matteo, R.N. Mantegna, Proc. Natl. Acad. Sci. 102, 10421 (2005), doi: 10.1073/pnas.0500298102
  • [11] J.-P. Onnela, A. Chakraborti, K. Kaski, J. Kertész, Euro Phys. Lett. B 30, 285 (2002), doi: 10.1140/epjb/e2002-00380-9
  • [12] A. Giuliani, J.P. Zbilut, F. Conti, C. Manetti, A. Miccheli, Physica A 337, 157 (2004), doi: 10.1016/j.physa.2004.01.053
  • [13] M.J. Disney, J.D. Romano, D.A. Garcia-Appadoo, A.A. West, J.J. Dalcanton, L. Cortese, Nature 455, 1082 (2008), doi: 10.1038/nature07366
  • [14] B.A. Suslick, L. Feng, K.S. Suslick, Anal. Chem. 82, 2067 (2010), doi: 10.1021/ac902823w
  • [15] J.D. Power, D.A. Fair, B.L. Schlaggar, S.E. Petersen, Neuron 67, 735 (2010), doi: 10.1016/j.neuron.2010.08.017
  • [16] W.P. Goh, D. Kwek, D. Hogan, S.A. Cheong, EPJ Data Sci. 3, 36 (2014), doi: 10.1140/epjds/s13688-014-0034-9
  • [17] R.V. Hogg, E.A. Tanis, D.L. Zimmerman, Probability and Statistical Inference, 9th ed., Pearson Education Ltd., Upper Saddle River 2015
  • [18] J.B. Kruskal, Proc. Am. Math. Soc. 7, 48 (1956), doi: 10.1090/S0002-9939-1956-0078686-7
  • [19] T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, New York 2013
  • [20] I.H. Witten, E. Frank, M.A. Hall, Data Mining. Practical Machine Learning Tools and Techniques, Morgan Kaufmann Publ., Burlington 2011

Document Type

Publication order reference

Identifiers

YADDA identifier

bwmeta1.element.bwnjournal-article-appv129n532kz
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.