Intelligent Tutoring Systems, Serious Games, and the Generalized Intelligent Framework for Tutoring (GIFT) (bibtex)
by Graesser, Arthur C, Hu, Xiangen, Nye, Benjamin D. and Sottilare, Robert A.
Abstract:
This chapter explores the prospects of integrating games with intelligent tutoring systems (ITSs). The hope is that there can be learning environments that optimize both motivation through games and deep learning through ITS technologies. Deep learning refers to the acquisition of knowledge, skills, strategies, and reasoning processes at the higher levels of Bloom’s (1956) taxonomy or the Knowledge-Learning-Instruction (KLI) framework (Koedinger, Corbett, & Perfetti, 2012), such as the application of knowledge to new cases, knowledge analysis and synthesis, problem solving, critical thinking, and other difficult cognitive processes. In contrast, shallow learning involves perceptual learning, memorization of explicit material, and mastery of simple rigid procedures. Shallow knowledge may be adequate for near transfer tests of knowledge/skills but not far transfer tests to new situations that have some modicum of complexity.
Reference:
Intelligent Tutoring Systems, Serious Games, and the Generalized Intelligent Framework for Tutoring (GIFT) (Graesser, Arthur C, Hu, Xiangen, Nye, Benjamin D. and Sottilare, Robert A.), Chapter in Using Games and Simulations for Teaching and Assessment, Routledge, 2016.
Bibtex Entry:
@incollection{graesser_intelligent_2016,
	address = {New York, NY},
	title = {Intelligent {Tutoring} {Systems}, {Serious} {Games}, and the {Generalized} {Intelligent} {Framework} for {Tutoring} ({GIFT})},
	isbn = {978-0-415-73787-6},
	url = {https://www.researchgate.net/publication/304013322_Intelligent_Tutoring_Systems_Serious_Games_and_the_Generalized_Intelligent_Framework_for_Tutoring_GIFT},
	abstract = {This chapter explores the prospects of integrating games with intelligent tutoring systems (ITSs). The hope is that there can be learning environments that optimize both motivation through games and deep learning through ITS technologies. Deep learning refers to the acquisition of knowledge, skills, strategies, and reasoning processes at the higher levels of Bloom’s (1956) taxonomy or the Knowledge-Learning-Instruction (KLI) framework (Koedinger, Corbett, \& Perfetti, 2012), such as the application of knowledge to new cases, knowledge analysis and synthesis, problem solving, critical thinking, and other difficult cognitive processes. In contrast, shallow learning involves perceptual learning, memorization of explicit material, and mastery of simple rigid procedures. Shallow knowledge may be adequate for near transfer tests of knowledge/skills but not far transfer tests to new situations that have some modicum of complexity.},
	booktitle = {Using {Games} and {Simulations} for {Teaching} and {Assessment}},
	publisher = {Routledge},
	author = {Graesser, Arthur C and Hu, Xiangen and Nye, Benjamin D. and Sottilare, Robert A.},
	month = jan,
	year = {2016},
	keywords = {Learning Sciences, UARC, ARL, DoD},
	pages = {58--79}
}
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