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JACIII Vol.22 No.6 pp. 943-955
doi: 10.20965/jaciii.2018.p0943
(2018)

Paper:

Analysis of Influence Factors for Learning Outcomes with Bayesian Network

Kazushi Okamoto

Department of Informatics, Graduate School of Informatics and Engineering, The University of Electro-Communications
1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan

Received:
February 26, 2018
Accepted:
July 23, 2018
Published:
October 20, 2018
Keywords:
Bayesian network, learning outcome, causal relationship, conditional probability
Abstract

This study identifies and analyzes the influence factors for learning outcomes at a university with a Bayesian network. It is based on a fact-finding survey on university student life and learning. Suitable constraints and a score metric for the Bayesian network learning are determined via cross-validation, and the learning outcome variables are categorized into subsets according to six abilities: cooperativeness, expressiveness, foreign language, collecting and organizing information, logical thinking, and sociability. The learned network suggests that two to seven factors influence each ability. In addition, it is confirmed that the probability distributions of all most of the identified factors shift to high agreement/experience levels, as self-knowledge levels for the acquired abilities increase, i.e., positive effects exist for most factors for each identified ability.

Cite this article as:
K. Okamoto, “Analysis of Influence Factors for Learning Outcomes with Bayesian Network,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.6, pp. 943-955, 2018.
Data files:
References
  1. [1] Department of University Management and Policy Studies, The University of Tokyo, “Nationwide University Student Survey,” http://ump.p.u-tokyo.ac.jp/crump/cat77/cat82/post-6.html [accessed July 13, 2017] (in Japanese)
  2. [2] Social Science Japan Data Archive, “Fact-finding Survey on University Student Life and Learning,” http://ssjda.iss.u-tokyo.ac.jp/Direct/gaiyo.php?lang=eng&eid=0721 [accessed July 13, 2017]
  3. [3] National Institute for Educational Policy Research, “Survey on University Student Learning,” http://www.nier.go.jp/04_kenkyu_annai/pdf/gakushu-jittai_2014.pdf [accessed July 13, 2017] (in Japanese)
  4. [4] K. Shima, K. Oyamada, S. Nakayama, and M. Yoshimasu, “Daigakukyoiku no Shitsuteki Jujitsuka – Jun-Specialist no Haishutsu ni Mukete –,” Policy Forum 2015, Inter-university Seminar for the Future of Japan, 2015 (in Japanese).
  5. [5] H. Yasuda, “Gender Differences in Grades and Efforts to Class,” The J. of Tokyo Keizai University: Economics, Vol.289, pp. 85-101, 2016 (in Japanese).
  6. [6] J. Pearl, “Bayesian Networks: A Model of Self-Activated Memory for Evidential Reasoning,” Proc. of 7th Annual Conf. of the Cognitive Science Society, 1985.
  7. [7] G. F. Cooper and E. Herskovits, “A Bayesian Method for the Induction of Probabilistic Networks from Data,” Machine Learning, Vol.9, No.4, pp. 309-347, 1992.
  8. [8] D. Margaritis, “Learning Bayesian Network Model Structure from Data,” Ph.D. thesis, School of Computer Science, Carnegie-Mellon University, 2003.
  9. [9] D. M. Chickering, “Learning Bayesian Networks is NP-Complete,” Learning from Data, Lecture Notes in Statistics, Vol.112, pp. 121-130, 1996.
  10. [10] P. Pumpuang, A. Srivihok, P. Praneetpolgrang, and S. Numprasertchai, “Using Bayesian Network for Planning Course Registration Model for Undergraduate Students,” Proc. of 2nd IEEE Int. Conf. on Digital Ecosystems and Technologies, pp. 492-496, 2008.
  11. [11] A. Sharabiani, F. Karim, A. Sharabiani, M. Atanasov, and H. Darabi, “An Enhanced Bayesian Network Model for Prediction of Students’ Academic Performance in Engineering Programs,” Proc. of 2014 IEEE Global Engineering Education Conf., pp. 832-837, 2014.
  12. [12] A. Fernández, M. Morales, C. Rodríguez, and A. Salmerón, “A System for Relevance Analysis of Performance Indicators in Higher Education using Bayesian Networks,” Knowledge and Information Systems, Vol.27, No.3, pp. 327-344, 2011.
  13. [13] E. Millán, T. Loboda, and J. L. Pérez-de-la-Cruz, “Bayesian Networks for Student Model Engineering,” Computers & Education, Vol.55, No.4, pp. 1663-1683, 2010.
  14. [14] A. Grubišić, S. Stankov, and I. Peraić, “Ontology Based Approach to Bayesian Student Model Design,” Expert Systems with Applications, Vol.40, No.13, pp. 5363-5371, 2013.
  15. [15] M. Xenos, “Prediction and Assessment of Student Behaviour in Open and Distance Education in Computers using Bayesian Networks,” Computers & Education, Vol.43, No.4, pp. 345-359, 2004.
  16. [16] P. García, A. Amandi, S. Schiaffino, and M. Campo, “Evaluating Bayesian Networks’ Precision for Detecting Students’ Learning Styles,” Computers & Education, Vol.49, No.3, pp. 794-808, 2007.
  17. [17] M. Watanabe, Y. Tsuchida, K. Kimura, and M. Tsubaki, “Analysis of the Educational Effectiveness Considering Individual Differences using Bayesian Network,” The European Conf. on Educational Research 2009, 492, 2009.
  18. [18] G. Schwarz, “Estimating the Dimension of a Model,” The Annals of Statistics, Vol.6, No.2, pp. 461-464, 1978.
  19. [19] D. Heckerman, D. Geiger, and D. M. Chickering, “Learning Bayesian Networks: The Combination of Knowledge and Statistical Data,” Machine Learning, Vol.20, No.3, pp. 197-243, 1995.
  20. [20] M. Scutari, “An Empirical-Bayes Score for Discrete Bayesian Networks,” Proc. of 8th Int. Conf. on Probabilistic Graphical Models, Vol.52, pp. 438-448, 2016.

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Last updated on Nov. 15, 2018