Evolving Training Scenarios with Measurable Variance in Learning Effects

Autor:	John J. Sanders, Brandt Dargue, Jeremiah T. Folsom-Kovarik
Rok vydání:	2019
Předmět:	Computer science business.industry Event (computing) Variation (game tree) Variance (accounting) Machine learning computer.software_genre Intelligent tutoring system Learning effect Variable (computer science) Artificial intelligence business computer Selection (genetic algorithm)
Zdroj:	Adaptive Instructional Systems ISBN: 9783030223403 HCI (32)
DOI:	10.1007/978-3-030-22341-0_4
Popis:	One major cost driver in simulation-based training (SBT) and Intelligent Tutoring System (ITS) development is authoring scenario content. Effort is compounded when creating a catalog of scenarios to enable optimal scenario selection for the individual learner. Automated Scenario Generation (ASG) methods have been successful at creating variants of scenarios using procedural generation, evolution, or event templates with variable parameters. These methods typically describe boundaries within which content may vary, but do not describe how the variation will affect learning.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::8bf6a1a0e27e8a50299acc7a4ecdb1a9 https://doi.org/10.1007/978-3-030-22341-0_4 Zobrazit plný text záznamu