Cognitive Simulations for Adaptive Instructional Systems: Exploring Instruction Strategies with Simulated Tutors and Learners

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.