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Annals of Computer Science and Information Systems, Volume 15

Proceedings of the 2018 Federated Conference on Computer Science and Information Systems

Automatic Assessment of Student Understanding Level using Virtual Reality

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DOI: http://dx.doi.org/10.15439/2018F268

Citation: Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 15, pages 3945 ()

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Abstract. The improvement of the efficiency in teaching re- quires knowing the understanding level of each student. However, it is difficult due to limited time in a class. We propose a Virtual Reality (VR) space imposing assignments on students, to know their understanding level from their behavior which comes from cognitive loads during their answering. The VR space presents a student an assignment and a working space to answer it. In general, students solve assignments, using elements on their short term memory. When students solve same kind of assignments many times, they build generalized solution methods in their long term memory. When they engage in such assignments, their cognitive load is low enough to make them watch only the working spaces, keeping their hands working. On the other hand, when students have no solution pattern, their short term memory works hard. Their high cognitive load often stop their hands, because of confusion. They also look assignments and the working space many times, to reconsider solutions. Since answering behavior of students exposes their cognitive load, a VR space is ideal to estimate cognitive load. We conducted an experiment to evaluate the ability of the method to estimate the cognitive load. We examined the movement of the hand and the edit distance of student's answer from the correct sentence during their answering. We confirmed a fair correlation of the hands ' stagnation with the confidence in students of good scores. We also found a relationship of eye movement with the change of the edit distance. The experiment result implies the possibility to estimate the cognitive load. The estimation would enable teachers to know students'understanding faults, which leads to education according to the understanding level.


  1. Kalyuga, S., Chandler, P., Tuovinen, J., & Sweller, J. (2001). When problem solving is superior to studying worked examples. Journal of Educational Psychology, 93, 579-588
  2. Schnotz, W., & Kürschner, C. (2007). A reconsideration of cognitive load theory. Educational Psychology Review, 19(4), 469-508.
  3. Sweller, J., van Merriënboer, J. J. G., & Paas, F. G. W. C. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251-296.
  4. Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63, 81-97.
  5. Peterson, L., & Peterson, M. (1959). Short-term retention of individual verbal items. Journal of Experimental Psychology, 58, 193-198.
  6. Ericsson, K. A., & Kintsch, W. (1995). Long-term working memory. Psychological Review, 102, 211-245
  7. Sweller, J. (2005). Implications of cognitive load theory for multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 19-30). New York: Cambridge University Press
  8. Sweller, J., & Chandler, P. (1994). Why some material is difficult to learn. Cognition and Instruction, 12(3), 185-233.
  9. Sweller, J., van Merriënboer, J. J. G., & Paas, F. G. W. C. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251-296.
  10. Marcus, N., Cooper, M., & Sweller, J. (1996). Understanding instructions. Journal of Educational Psychology, 88, 49-63.
  11. Fred Paas , Alexander Renkl & John Sweller (2003) Cognitive Load Theory and Instructional Design: Recent Developments, Educational Psychologist, 38:1, 1-4, http://dx.doi.org/10.1207/S15326985EP3801_1
  12. Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24, 87-185
  13. S. Tachi, M. Sato, M. Hirose(2010),“Science of Virtual Reality (バーチャルリアリティ学)”, The virtual reality society of japan
  14. Dutt, Ashish, Maizatul Akmar Ismail, and Tutut Herawan. “A systematic review on educational data mining.” IEEE Access 5 (2017): 15991-16005.
  15. V. Ivancevic, M. Celikovic, I. Lukovic, “The individual stability of student spatial deployment and its implications”, Int. Symp. Comput. Edu. (SIIE), pp. 1-4, Oct. 2012.
  16. K. Nakamura, K. Kakusho, M. Murakami, and M. Minoh(2010). “Estimating Learners’ Subjective Impressions of the Difficulty of Course Materials by Observing Their Faces in e-Learning” The IEICE Transactions on Information and Systems(Japanese Edition) Vol.J93-D No.5 pp.568-578
  17. Richard A. Monty and John W. Senders. 1976. Eye Movements and Psychological Processes. Lawrence Erlbaum Associates, Hillsdale, NJ.
  18. FOVE. FOVE Eye Tracking Virtual Reality Headset. Retrieved September 19, 2017 from https://www.getfove.com/
  19. Mathieu Nancel, Olivier Chapuis, Emmanuel Pietriga, Xing-Dong Yang, Pourang P. Irani, and Michel Beaudouin-Lafon. 2013. High-precision pointing on large wall displays using small handheld devices. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems - CHI’13, 831. https://doi.org/10.1145/2470654.2470773
  20. Marcos Serrano, Barrett Ens, Xing-Dong Yang, and Pourang Irani. 2015. Gluey: Developing a Head-Worn Display Interface to Unify the Interaction Experience in Distributed Display Environments. In Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services - MobileHCI’15, 161–171. https://doi.org/10.1145/2785830.2785838
  21. Kruger, Justin; Dunning, David (1999). “Unskilled and Unaware of It: How Difficulties in Recognizing One’s Own Incompetence Lead to Inflated Self-Assessments”. Journal of Personality and Social Psychology 77 (6): 1121-34