Knowledge Structure Analysis System for Critical Learning Pathway

결정적 학습 경로를 위한 지식 구조 분석 시스템

Lee, Sanghoon;Moon, Seung-jin

  • Received : 2015.03.24
  • Accepted : 2015.06.09
  • Published : 2015.12.31


Knowledge space theory is a theory that provides a guidelines for human learners' possible education decisions and has been used in various educational environment. However, traditional methodologies using the knowledge space theory have always depended on handwork system and it is necessary to learn programming language such as Visual Basic and R, causing time consuming situations. In order to overcome those issues on the environment of education we propose a new Knowledge Structure Analysis System that not just analyzes learners' knowledge structures automatically but to provide critical learning path for the learners based on knowledge space theory. Proposed system is implemented by using rApache generating critical learning path computing Chi-square value. This provides an automatic way of analyzing knowledge structure in learners' knowledge space and shows systematic reviews for the knowledge space.


Knowledge space theory;Knowledge structure analysis;Education environment;Automatic analysis system


  1. Doignon, J.-P.; Falmagne, J.-Cl., "Spaces for the assessment of knowledge", International Journal of Man-Machine Studies, vol. 23, no. 2, pp. 175-196, 1985.
  2. Sitthisak, Onjira, Lester Gilbert, and Dietrich Albert. "Adaptive Learning Using an Integration of Competence Model with Knowledge Space Theory." In Advanced Applied Informatics (IIAIAAI), 2013 IIAI International Conference on, pp. 199-202. IEEE, 2013.
  3. Chen, Yang. "Adaptive Non-graded Assessment Based on Knowledge Space Theory." In ICALT, pp. 63-64. 2013.
  4. R project Available in:
  5. A Unlu and S. Anatol, "DAKS: An R Package for Data Analysis Methods in Knowledge Space Theory." Journal of Statistical Software, vol. 37, no. 2, pp. 1-31, 2010.
  6. Meyer D and Hornik K, "Generalized and Customizable Sets in R", Journal of Statistical Software, vol. 31, no. 2, pp. 1-27, 2009.
  7. Hornik K and Meyer D, "relations: Data Structures and Algorithms for Relations. R package version 0.5-8,
  8. Gentry J, Long L, Gentleman R, Falcon S, Hahne F, Sarkar D, Hansen K, "Rgraphviz:How To Plot A Graph Using Rgraphviz," R package version 1.26.0.
  9. S. Christina, "Knowledge Space Theory", 2011.
  10. Project rApache, Available in:
  11. J.-P. Doignon and J.-C. Falmagne,"Knowledge Spaces," Springer Verlag, Heidelberg, 1999.
  12. J. Alsup andH. Stillson, "Smart ALEKS ... or not? Teaching Basic Algebra using an online interactive learning system," Mathematics and Computer Education, vol. 37 no. 3, pp. 329-336, 2003.
  13. M. Villano, "Probabilistic student models: Bayesian belief networks and knowledge space theory," In Proceedings of the 2nd International Conference on Intelligent Tutoring Systems. Heidelberg, 1992, pp. 491-498.
  14. D2.1 Specification of ECAAD Methodology V1, 2011.
  15. P. Reimann, M. Kickmeier-Rust, and D. Albert, "Problem solving learning environments and assessment: A knowledge space theory approach,"Computers & Education vol. 64, pp. 183-193, 2013.
  16. A. Spoto, L. Stefanutti, and G. Vidotto, "Knowledge space theory, formal concept analysis, and computerized psychological assessment," Behavior research methods, vol. 42, no. 1, pp. 342-350, 2010.