國立虎尾科技大學 |

Computational Models of Human Learning : = Applications for Tutor Development, Behavior Prediction, and Theory Testing.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Computational Models of Human Learning :/
其他題名:	Applications for Tutor Development, Behavior Prediction, and Theory Testing.
作者:	MacLellan, Christopher J.
面頁冊數:	1 online resource (109 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
Contained By:	Dissertation Abstracts International79-04B(E).
標題:	Artificial intelligence. -
電子資源:	click for full text (PQDT)
ISBN:	9780355423273

Computational Models of Human Learning : = Applications for Tutor Development, Behavior Prediction, and Theory Testing.
MacLellan, Christopher J.

Computational Models of Human Learning :Applications for Tutor Development, Behavior Prediction, and Theory Testing. - 1 online resource (109 pages)

Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.

Thesis (Ph.D.)--Carnegie Mellon University, 2017.

Includes bibliographical references

Intelligent tutoring systems are effective for improving students' learning outcomes (Bowen et al., 2013; Koedinger & Anderson, 1997; Pane et al., 2013). However, constructing tutoring systems that are pedagogically effective has been widely recognized as a challenging problem (Murray, 1999, 2003). In this thesis, I explore the use of computational models of apprentice learning, or computer models that learn interactively from examples and feedback, to support tutor development. In particular, I investigate their use for authoring expert-models via demonstrations and feedback (Matsuda et al., 2014), predicting student behavior within tutors (VanLehn et al., 1994), and for testing alternative learning theories (MacLellan, Harpstead, Patel, & Koedinger, 2016).

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355423273Subjects--Topical Terms:

559380
Artificial intelligence.
Index Terms--Genre/Form:

554714
Electronic books.

Computational Models of Human Learning : = Applications for Tutor Development, Behavior Prediction, and Theory Testing.
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500 $a Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
500 $a Adviser: Kenneth R. Koedinger.
502 $a Thesis (Ph.D.)--Carnegie Mellon University, 2017.
504 $a Includes bibliographical references
520 $a Intelligent tutoring systems are effective for improving students' learning outcomes (Bowen et al., 2013; Koedinger & Anderson, 1997; Pane et al., 2013). However, constructing tutoring systems that are pedagogically effective has been widely recognized as a challenging problem (Murray, 1999, 2003). In this thesis, I explore the use of computational models of apprentice learning, or computer models that learn interactively from examples and feedback, to support tutor development. In particular, I investigate their use for authoring expert-models via demonstrations and feedback (Matsuda et al., 2014), predicting student behavior within tutors (VanLehn et al., 1994), and for testing alternative learning theories (MacLellan, Harpstead, Patel, & Koedinger, 2016).
520 $a To support these investigations, I present the Apprentice Learner Architecture, which posits the types of knowledge, performance, and learning components needed for apprentice learning and enables the generation and testing of alternative models. I use this architecture to create two models: the DECISION TREE model, which non-incrementally learns when to apply its skills, and the TRESTLE model, which instead learns incrementally. Both models both draw on the same small set of prior knowledge for all simulations (six operators and three types of relational knowledge). Despite their limited prior knowledge, I demonstrate their use for efficiently authoring a novel experimental design tutor and show that they are capable of achieving human-level performance in seven additional tutoring systems that teach a wide range of knowledge types (associations, categories, and skills) across multiple domains (language, math, engineering, and science).
520 $a I show that the models are capable of predicting which versions of a fraction arithmetic and box and arrows tutors are more effective for human students' learning. Further, I use a mixedeffects regression analysis to evaluate the fit of the models to the available human data and show that across all seven domains the TRESTLE model better fits the human data than the DECISION TREE model, supporting the theory that humans learn the conditions under which skills apply incrementally, rather than non-incrementally as prior work has suggested (Li, 2013; Matsuda et al., 2009). This work lays the foundation for the development of a Model Human Learner---similar to Card, Moran, and Newell's (1986) Model Human Processor---that encapsulates psychological and learning science findings in a format that researchers and instructional designers can use to create effective tutoring systems.
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538 $a Mode of access: World Wide Web
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650 4 $a Computer science. $3 573171
650 4 $a Cognitive psychology. $3 556029
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710 2 $a Carnegie Mellon University. $b Computer Science. $3 1190708
773 0 $t Dissertation Abstracts International $g 79-04B(E).
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