Abstract
Computational models of learners have been recognized to play various roles in training and learning environments. While optimized tutoring strategies should be determined through empirical investigation, the adaptive instructional system design space is too large to fully validate empirically. Synthetic data generated by simulated learners could be one approach to explore the interaction between learner behaviours and adaptive instructional system strategies. The current paper reports on a computer simulation design and results for modelling the effects of learning and training strategies on the learning and performance of simulated learners. The application domain is marine navigation. The computer simulation included a fairly autonomous learning agent with self-assessment capabilities (reinforcement learning), and other means to acquire knowledge and skills including learning from instructions, and declarative memory base-level activation. Three instructional strategies were simulated: 1) a minimalist method leaving the simulated learner to proceed only by trial and error, 2) a discovery method where the simulated learners are left on their own but with an added capability to store a declarative representation of successful rules, and 3) a briefing then practice method, where all the declarative rules to execute tasks are in declarative memory prior to executing navigation tasks.
This project was supported in part by collaborative research funding from the National Research Council of Canada’s Artificial Intelligence for Logistics Program.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
ACT-R Research Group (2002). http://act-r.psy.cmu.edu, http://act-r.psy.cmu.edu
Anderson, J., Betts, S., Bothell, D., Hope, R.M., Lebiere, C.: Three aspects of skill acquisition, June 2018. https://doi.org/10.31234/osf.io/rh6zt, psyarxiv.com/rh6zt
Anderson, J.R., Betts, S., Bothell, D., Lebiere, C.: Discovering skill. Cogn. Psychol. 129, 101410 (2021). https://doi.org/10.1016/j.cogpsych.2021.101410, https://www.sciencedirect.com/science/article/pii/S0010028521000335
Cockburn, A., Gutwin, C., Scarr, J., Malacria, S.: Supporting novice to expert transitions in user interfaces. ACM Comput. Surv. 47(2), 1–36 (2015). https://doi.org/10.1145/2659796, https://dl.acm.org/doi/10.1145/2659796
Domeshek, E., Ramachandran, S., Jensen, R., Ludwig, J., Ong, J., Stottler, D.: Lessons from building diverse adaptive instructional systems (AIS). In: Sottilare, R.A., Schwarz, J. (eds.) Adaptive Instructional Systems. HCII 2019. LNCS, vol. 11597, pp. 62–75. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22341-0, http://link.springer.com/10.1007/978-3-030-22341-0
Emond, B.: WN-LEXICAL: an ACT-R module built from the WordNet lexical database. In: Seventh International Conference on Cognitive Modeling, pp. 359–360. Trieste, Italy (2006)
Emond, B., Comeau, G.: Cognitive modelling of early music reading skill acquisition for piano: a comparison of the Middle-C and Intervallic methods. Cogn. Syst. Res. 24, 26–34 (2013). https://doi.org/10.1016/j.cogsys.2012.12.007, http://dx.doi.org/10.1016/j.cogsys.2012.12.007
Emond, B., et al.: Development of AIS Using simulated learners, Bayesian networks and knowledge elicitation methods. In: Sottilare, R.A., Schwarz, J. (eds.) Adaptive Instructional Systems. HCII 2022. LNCS, vol. 13332, pp. 143–158. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-05887-5_11
Emond, B., Vinson, N.G.: Modelling simple ship conning tasks. In: 15th Meeting of the International Conference on Cognitive Modelling, pp. 42–44. Coventry, UK (2017)
Emond, B., West, R.R.L.: Cyberpsychology: a human-interaction perspective based on cognitive modeling. Cyberpsychol. Behav. 6(5), 527–536 (2003). https://doi.org/10.1089/109493103769710550
Emond, B., West, R.L.: Using cognitive modelling simulations for user interface design decisions. In: Orchard, B., Yang, C., Ali, M. (eds.) IEA/AIE 2004. LNCS (LNAI), vol. 3029, pp. 305–314. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24677-0_32
Essa, A., Mojarad, S.: Does time matter in learning? A computer simulation of Carroll’s model of learning. In: Sottilare, R.A., Schwarz, J. (eds.) HCII 2020. LNCS, vol. 12214, pp. 458–474. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50788-6_34
Harpstead, E., MacLellan, C.J., Weitekamp, D., Koedinger, K.R.: The use simulated learners in adaptive education. In: AIAED-19: AI + Adaptive Education, pp. 1–3. Beijing, China (2019)
Lelei, D.E.K., McCalla, G.: How to use simulation in the design and evaluation of learning environments with self-directed longer-term learners. In: Penstein Rosé, C., et al. (eds.) AIED 2018. LNCS (LNAI), vol. 10947, pp. 253–266. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93843-1_19
MacLellan, C.J., Koedinger, K.R.: Domain-general tutor authoring with apprentice learner models. Int. J. Artif. Intell. Educ. (2020). https://doi.org/10.1007/s40593-020-00214-2, https://link.springer.com/10.1007/s40593-020-00214-2
McCalla, G., Champaign, J.: Simulated learners. IEEE Intell. Syst. 28(4), 67–71 (2013). https://doi.org/10.1109/MIS.2013.116
McEneaney, J.E.: Simulation-based evaluation of learning sequences for instructional technologies. Instruct. Sci. 44(1), 87–106 (2016). https://doi.org/10.1007/s11251-016-9369-x, http://link.springer.com/10.1007/s11251-016-9369-x
Ritter, F.E., Yeh, M.K.C., Yan, Y., Siu, K.C., Oleynikov, D.: Effects of varied surgical simulation training schedules on motor-skill acquisition. Surg. Innov. 27(1), 68–80 (2020). https://doi.org/10.1177/1553350619881591, http://journals.sagepub.com/doi/10.1177/1553350619881591
Smith, J., Yazdanpanah, F., Thistle, R., Musharraf, M., Veitch, B.: Capturing expert knowledge to inform decision support technology for marine operations. J. Marine Sci. Eng. 8(9) (2020). https://doi.org/10.3390/JMSE8090689
Spain, R., Rowe, J., Smith, A., Goldberg, B., Pokorny, R., Mott, B., Lester, J.: A reinforcement learning approach to adaptive remediation in online training. J. Defense Model. Simul. Appl. Methodol. Technol. (2021). https://doi.org/10.1177/15485129211028317, http://journals.sagepub.com/doi/10.1177/15485129211028317
Thistle, R., Veitch, B.: An evidence-based method of training to targeted levels of performance. In: SNAME Maritime Convention 2019, SMC 2019 (2019)
Veitch, E., Molyneux, D., Smith, J., Veitch, B.: Investigating the influence of bridge officer experience on ice management effectiveness using a marine simulator experiment. J. Offshore Mech. Arctic Eng. 141(4) (2019). https://doi.org/10.1115/1.4041761, https://asmedigitalcollection.asme.org/offshoremechanics/article/doi/10.1115/1.4041761/475585/Investigating-the-Influence-of-Bridge-Officer
Walsh, M.M., et al.: Mechanisms underlying the spacing effect in learning: a comparison of three computational models. J. Exp. Psychol. Gen. 147(9), 1325–1348 (2018). https://doi.org/10.1037/xge0000416, http://doi.apa.org/getdoi.cfm?doi=10.1037/xge0000416
Weitekamp, D., Harpstead, E., Koedinger, K.R.: An interaction design for machine teaching to develop AI tutors, pp. 1–11. Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3313831.3376226
Weitekamp, D., Ye, Z., Rachatasumrit, N., Harpstead, E., Koedinger, K.: Investigating differential error types between human and simulated learners. In: Bittencourt, I.I., Cukurova, M., Muldner, K., Luckin, R., Millán, E. (eds.) AIED 2020. LNCS (LNAI), vol. 12163, pp. 586–597. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-52237-7_47
Wray, R., Stowers, K.: Interactions between learner assessment and content requirement: a verification approach. Adv. Intell. Syst. Comput. 596, 36–45 (2018). https://doi.org/10.1007/978-3-319-60018-5_4
Wray, R.E.: Enhancing simulated students with models of self-regulated learning. In: Schmorrow, D.D., Fidopiastis, C.M. (eds.) HCII 2019. LNCS (LNAI), vol. 11580, pp. 644–654. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22419-6_46
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 Crown
About this paper
Cite this paper
Emond, B., Zeinali-Torbati, R., Smith, J., Billard, R., Barnes, J., Veitch, B. (2023). Cognitive Simulations for Adaptive Instructional Systems: Exploring Instruction Strategies with Simulated Tutors and Learners. In: Sottilare, R.A., Schwarz, J. (eds) Adaptive Instructional Systems. HCII 2023. Lecture Notes in Computer Science, vol 14044. Springer, Cham. https://doi.org/10.1007/978-3-031-34735-1_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-34735-1_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-34734-4
Online ISBN: 978-3-031-34735-1
eBook Packages: Computer ScienceComputer Science (R0)