Advertisement

3D Virtual World BPM Training Systems: Process Gateway Experimental Results

  • Michael LeyerEmail author
  • Ross Brown
  • Banu Aysolmaz
  • Irene Vanderfeesten
  • Oktay Turetken
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11483)

Abstract

It is important for companies that their operational employees have profound knowledge of the processes in which their work is embedded. 3D virtual world (VW) environments are promising for learning, especially for complex processes that have deviations from the standard flow. We design a 3D VW process training environment to improve process learning, particularly for complex processes with alternative flows, represented with gateways in process models. We adopt the method of loci, which suggests the mental traversal of routines for improving learning. Our experiment with 145 participants compares the level of knowledge acquired for a sample process with our 3D VW environment and a 2D depiction. We found that the 3D VW environment significantly increases the level of process knowledge acquired across the typical gateways in processes. Our results contribute to our understanding of how individuals learn knowledge of processes via 3D environments. With a low initial investment, practitioners are encouraged to invest in 3D training systems for processes, since these can be set up once and reused multiple times for various employees.

Keywords

Training Process model Virtual worlds Gateways Experiment 

References

  1. 1.
    Babić-Hodović, V., Mehić, E., Arslanagić, M.: The influence of quality practices on BH companies’ business performance. Int. J. Manag. Cases 14, 305–316 (2012)CrossRefGoogle Scholar
  2. 2.
    Leyer, M., Hirzel, A.-K., Moormann, J.: Achieving sustainable behavioral changes of daily work practices. The effect of role plays on learning process-oriented behavior. Bus. Process. Manag. J. 24, 1050–1068 (2018)CrossRefGoogle Scholar
  3. 3.
    Ong, S.K., Yuan, M.L., Nee, A.Y.C.: Augmented reality applications in manufacturing. A survey. Int. J. Prod. Res. 46, 2707–2742 (2008)CrossRefGoogle Scholar
  4. 4.
    Dumas, M., La Rosa, M., Mendling, J., Reijers, H.A.: Fundamentals of Business Process Management. Springer, Heidelberg (2018).  https://doi.org/10.1007/978-3-662-56509-4CrossRefGoogle Scholar
  5. 5.
    Figl, K., Laue, R.: Influence factors for local comprehensibility of process models. Int. J. Hum Comput Stud. 82, 96–110 (2015)CrossRefGoogle Scholar
  6. 6.
    Figl, K.: Comprehension of procedural visual business process models. Bus. Inf. Syst. Eng. 59, 41–67 (2018)CrossRefGoogle Scholar
  7. 7.
    Brown, R., Rinderle-Ma, S., Kriglstein, S., Kabicher-Fuchs, S.: Augmenting and assisting model elicitation tasks with 3D virtual world context metadata. In: Meersman, R., Panetto, H., Dillon, T., Missikoff, M., Liu, L., Pastor, O., Cuzzocrea, A., Sellis, T. (eds.) OTM 2014. LNCS, vol. 8841, pp. 39–56. Springer, Heidelberg (2014).  https://doi.org/10.1007/978-3-662-45563-0_3CrossRefGoogle Scholar
  8. 8.
    Harman, J., Brown, R., Johnson, D., Rinderle-Ma, S., Kannengiesser, U.: Augmenting process elicitation with visual priming. An empirical exploration of user behaviour and modelling outcomes. Inf. Syst. 62, 242–255 (2016)CrossRefGoogle Scholar
  9. 9.
    Ghanbarzadeh, R., Ghapanchi, A.H., Blumenstein, M., Talaei-Khoei, A.: A decade of research on the use of three-dimensional virtual worlds in health care. A systematic literature review. J. Med. Internet Res. 16, e47 (2014)CrossRefGoogle Scholar
  10. 10.
    Dresler, M., et al.: Mnemonic training reshapes brain networks to support superior memory. Neuron 93, 1227–1235 (2017)CrossRefGoogle Scholar
  11. 11.
    Huttner, J.-P., Robbert, K.: The role of mental factors for the design of a virtual memory palace. In: Twenty-Fourth AMCIS, pp. 2015–2019 (2018)Google Scholar
  12. 12.
    Ralby, A., Mentzelopoulos, M., Cook, H.: Learning languages and complex subjects with memory palaces. In: Beck, D., et al. (eds.) iLRN 2017. CCIS, vol. 725, pp. 217–228. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-60633-0_18CrossRefGoogle Scholar
  13. 13.
    Brown, J.S., Collins, A., Duguid, P.: Situated cognition and the culture of learning. Educ. Res. 18, 32–34 (1989)CrossRefGoogle Scholar
  14. 14.
    Godden, D., Baddeley, A.: When does context influence recognition memory? Br. J. Psychol. 71, 99–104 (1980)CrossRefGoogle Scholar
  15. 15.
    Godden, D.R., Baddeley, A.D.: Context-dependent memory in two natural environments. On land and underwater. Br. J. Psychol. 66, 325–331 (1975)CrossRefGoogle Scholar
  16. 16.
    Burgess, N., Hockley, W.E., Hourihan, K.L.: The effects of context in item-based directed forgetting. Evidence for “one-shot” context storage. Mem. Cogn. 45, 745–754 (2017)CrossRefGoogle Scholar
  17. 17.
    Yates, F.A.: The Art of Memory. Routledge & Kegan Paul, London (1966)Google Scholar
  18. 18.
    Huttner, J.-P., Pfeiffer, D., Robra-Bissantz, S.: Imaginary versus virtual loci. Evaluating the memorization accuracy in a virtual memory palace. In: Bui, T. (ed.) Proceedings of the 51st HICSS, pp. 274–282. University of Hawai’i at Manoa Honolulu (2018)Google Scholar
  19. 19.
    Worthen, J.B., Hunt, R.R.: Mnemonology. Psychology Press, New York (2011)Google Scholar
  20. 20.
    Gould, N.F., et al.: Performance on a virtual reality spatial memory navigation task in depressed patients. Am. J. Psychiatry 164, 516–519 (2007)CrossRefGoogle Scholar
  21. 21.
    Harman, J., Brown, R., Johnson, D.: Improved memory elicitation in virtual reality: new experimental results and insights. In: Bernhaupt, R., Dalvi, G., Joshi, A., Balkrishan, D.K., O’Neill, J., Winckler, M. (eds.) INTERACT 2017. LNCS, vol. 10514, pp. 128–146. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-67684-5_9CrossRefGoogle Scholar
  22. 22.
    van Der Aalst, W.M., Ter Hofstede, A.H., Kiepuszewski, B., Barros, A.P.: Workflow patterns. Distrib. Parallel Databases 14, 5–51 (2003)CrossRefGoogle Scholar
  23. 23.
    Bell, M.W.: Toward a definition of “virtual worlds”. J. Virtual Worlds Res. 1, 1–5 (2008)CrossRefGoogle Scholar
  24. 24.
    Bailenson, J., Patel, K., Nielsen, A., Bajscy, R., Jung, S.-H., Kurillo, G.: The effect of interactivity on learning physical actions in virtual reality. Media Psychol. 11, 354–376 (2008)CrossRefGoogle Scholar
  25. 25.
    Dalgarno, B., Lee, M.J.W.: What are the learning affordances of 3-D virtual environments? Br. J. Educ. Technol. 41, 10–32 (2010)CrossRefGoogle Scholar
  26. 26.
    Okita, S.Y., Turkay, S., Kim, M., Murai, Y.: Learning by teaching with virtual peers and the effects of technological design choices on learning. Comput. Educ. 63, 176–196 (2013)CrossRefGoogle Scholar
  27. 27.
    Choi, B., Baek, Y.: Exploring factors of media characteristic influencing flow in learning through virtual worlds. Comput. Educ. 57, 2382–2394 (2011)CrossRefGoogle Scholar
  28. 28.
    Petrakou, A.: Interacting through avatars. Virtual worlds as a context for online education. Comput. Educ. 54, 1020–1027 (2010)CrossRefGoogle Scholar
  29. 29.
    Barab, S., Thomas, M., Dodge, T., Carteaux, R., Tuzun, H.: Making learning fun. Quest Atlantis, a game without guns. Educ. Technol. Res. Dev. 53, 86–107 (2005)CrossRefGoogle Scholar
  30. 30.
    Leyer, M., Stumpf-Wollersheim, J., Pisani, F.: The influence of process-oriented organizational design on operational performance and innovation. Int. J. Prod. Res. 55, 5259–5270 (2017)CrossRefGoogle Scholar
  31. 31.
    Figl, K., Mendling, J., Strembeck, M.: The influence of notational deficiencies on process model comprehension. J. AIS 14, 312–338 (2013)Google Scholar
  32. 32.
    Poppe, E., Brown, R., Recker, J., Johnson, D., Vanderfeesten, I.: Design and evaluation of virtual environments mechanisms to support remote collaboration on complex process diagrams. Inf. Syst. 66, 59–81 (2017)CrossRefGoogle Scholar
  33. 33.
    Krokos, E., Plaisant, C., Varshney, A.: Spatial mnemonics using virtual reality. In: Proceedings of the 2018 10th ICCAE, pp. 27–30. ACM, Brisbane (2018)Google Scholar
  34. 34.
    Huttner, J.-P., Pfeiffer, D., Robra-Bissantz, S.: Imaginary versus virtual loci: evaluating the memorization accuracy in a virtual memory palace (2018)Google Scholar
  35. 35.
    Huttner, J.-P., Robra-Bissantz, S.: An immersive memory palace: supporting the method of loci with virtual reality. In: 23rd AMCIS, pp. 1–10 (2017)Google Scholar
  36. 36.
    Huttner, J.-P., Robra-Bissantz, S.: A design science approach to high immersive mnemonic E-learning. In: MCIS 2016 Proceedings, pp. 1–5 (2016)Google Scholar
  37. 37.
    Huang, B., Tang, H.J.: Study of workshop production system based on Petri Nets and Flexsim. In: Proceedings of the 22nd International Conference on Industrial Engineering and Engineering Management 2015, Atlantis Press, Paris, pp. 833–844 (2016)CrossRefGoogle Scholar
  38. 38.
    Genon, N., Heymans, P., Amyot, D.: Analysing the cognitive effectiveness of the BPMN 2.0 visual notation. In: Malloy, B., Staab, S., van den Brand, M. (eds.) SLE 2010. LNCS, vol. 6563, pp. 377–396. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-19440-5_25CrossRefGoogle Scholar
  39. 39.
    Leyer, M., Strohhecker, J.: Mental models of business processes. Working paper series of the chair of service management, University of Rostock (2017)Google Scholar
  40. 40.
    Sarshar, K., Loos, P.: Comparing the control-flow of EPC and petri net from the end-user perspective. In: van der Aalst, W.M.P., Benatallah, B., Casati, F., Curbera, F. (eds.) BPM 2005. LNCS, vol. 3649, pp. 434–439. Springer, Heidelberg (2005).  https://doi.org/10.1007/11538394_36CrossRefGoogle Scholar
  41. 41.
    Sánchez-González, L., García, F., Ruiz, F., Mendling, J.: Quality indicators for business process models from a gateway complexity perspective. Inf. Softw. Technol. 54, 1159–1174 (2012)CrossRefGoogle Scholar
  42. 42.
    Dikici, A., Turetken, O., Demirors, O.: Factors influencing the understandability of process models: a systematic literature review. Inf. Softw. Technol. 93, 112–129 (2018)CrossRefGoogle Scholar
  43. 43.
    Mendling, J., Verbeek, H.M.W., Dongen, B.F.V., van der Aalst, W.M.P., Neumann, G.: Detection and prediction of errors in EPCs of the SAP reference model. Data Knowl. Eng. 64, 312–329 (2008)CrossRefGoogle Scholar
  44. 44.
    Aysolmaz, B., Schunselaar, D.M.M., Reijers, H.A., Yaldiz, A.: Selecting a process variant modeling approach: guidelines and application. Softw. Syst. Model. 18, 1155–1178 (2017)CrossRefGoogle Scholar
  45. 45.
    Sauzéon, H., Arvind Pala, P., Larrue, F., Wallet, G., Déjos, M., Zheng, X., Guitton, P., N’Kaoua, B.: The use of virtual reality for episodic memory assessment. Exp. Psychol. 59, 99–108 (2012)CrossRefGoogle Scholar
  46. 46.
    Aysolmaz, B., Brown, R., Bruza, P., Reijers, H.A.: A 3D visualization approach for process training in office environments. In: Debruyne, C., et al. (eds.) On the Move to Meaningful Internet Systems: OTM 2016 Conferences: Confederated International Conferences: CoopIS, C&TC, and ODBASE 2016, pp. 418–436. Springer, Heidelberg (2016).  https://doi.org/10.1007/978-3-319-48472-3_24CrossRefGoogle Scholar
  47. 47.
    Kimball, D.R., Holyoak, K.J.: Transfer and expertise. In: The Oxford Handbook of Memory, pp. 109–122. Oxford University Press, New York (2000)Google Scholar
  48. 48.
    Khemlani, S., Johnson-Laird, P.N.: Disjunctive illusory inferences and how to eliminate them. Mem. Cogn. 37, 615–623 (2009)CrossRefGoogle Scholar
  49. 49.
    Frederick, S.: Cognitive reflection and decision making. J. Econ. Perspect. 19, 25–42 (2005)CrossRefGoogle Scholar
  50. 50.
    Kimchi, R., Palmer, S.E.: Form and texture in hierarchically constructed patterns. J. Exp. Psychol. Hum. Percept. Perform. 8, 521–535 (1982)CrossRefGoogle Scholar
  51. 51.
    Förster, J., Dannenberg, L.: GLOMOsys: a systems account of global versus local processing. Psychol. Inq. 21, 175–197 (2010)CrossRefGoogle Scholar
  52. 52.
    Melcher, J., Mendling, J., Reijers, H.A., Seese, D.: On measuring the understandability of process models. In: Rinderle-Ma, S., Sadiq, S., Leymann, F. (eds.) BPM 2009. LNBIP, vol. 43, pp. 465–476. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-12186-9_44CrossRefGoogle Scholar
  53. 53.
    Indulska, M., Recker, J., Rosemann, M., Green, P.: Business process modeling: current issues and future challenges. In: van Eck, P., Gordijn, J., Wieringa, R. (eds.) CAiSE 2009. LNCS, vol. 5565, pp. 501–514. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-02144-2_39CrossRefGoogle Scholar
  54. 54.
    Narayanan, A., Moritz, B.B.: Decision making and cognition in multi-echelon supply chains: an experimental study. Prod. Oper. Manag. 24, 1216–1234 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Michael Leyer
    • 1
    • 2
    Email author
  • Ross Brown
    • 2
  • Banu Aysolmaz
    • 3
  • Irene Vanderfeesten
    • 4
  • Oktay Turetken
    • 4
  1. 1.University of RostockRostockGermany
  2. 2.Queensland University of TechnologyBrisbaneAustralia
  3. 3.Maastricht UniversityMaastrichtThe Netherlands
  4. 4.Eindhoven University of TechnologyEindhovenThe Netherlands

Personalised recommendations