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Teaching Effectiveness of Intelligen...

Hu, Yang.

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Teaching Effectiveness of Intelligent Tutoring Systems.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Teaching Effectiveness of Intelligent Tutoring Systems./
作者:	Hu, Yang.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
面頁冊數:	156 p.
附註:	Source: Dissertations Abstracts International, Volume: 81-02, Section: A.
Contained By:	Dissertations Abstracts International81-02A.
標題:	Artificial intelligence. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13857527
ISBN:	9781085557986

Teaching Effectiveness of Intelligent Tutoring Systems.
Hu, Yang.

Teaching Effectiveness of Intelligent Tutoring Systems. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 156 p.

Source: Dissertations Abstracts International, Volume: 81-02, Section: A.

Thesis (Ph.D.)--Washington State University, 2019.

This item must not be sold to any third party vendors.

In this dissertation, the author presents two projects regarding teaching strategies that apply to an intelligent tutoring system (ITS). The author applied multiple-solution teaching methods to the ITSs. The first project is an ITS that aims to help college students learn how to use Computer-Aided-Design (CAD) software, FreeCAD ITS. The second project is named SHiB ITS, and it seeks to help seniors learn the principles of finding the right spots to install a smart-home kit in their own house. In the two projects, the author experimented three teaching strategies, trial-and-error, practicing worked examples, and a combination of the two. The results of the post-test survey of the FreeCAD ITS show that college student participants like to learn by practicing multiple-solutions. Also, students in the combination teaching strategy group performed better in post-tests than the ones in the other two groups. The results of the post-test survey of the SHiB ITS shows that both older and younger adults like to learn in having exercises of multiple solutions. The older adults benefit more from the trial-and-error teaching strategy, while the younger adults benefit more from the combination teaching strategy. The experimental results agree to each other. The author then explored machine learning algorithms that uses an AI agent to teach another agent to perform a complicated task. The author proposed two algorithms that require a state importance input, a reinforcement learning (RL) algorithm that trains an RL teacher to teach, and an artificial neural network (ANN) that simulates behaviors of a teacher. Then the author applied the explored RL algorithm to train an RL teacher that knows how to perform tasks of SHiB ITS. The author added the trained teacher to the SHiB ITS and experimented the system with recruited participants. The results suggest that participants taught by the RL teacher performed better in the task that requires alternative solutions. In the post-survey, they significantly indicate that it is quick for them to learn to use the SHiB ITS with the RL teacher.

ISBN: 9781085557986Subjects--Topical Terms:

516317
Artificial intelligence.

Teaching Effectiveness of Intelligent Tutoring Systems.
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