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Capturing Solution Tactics for Effec...

Wang, Ke.

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Capturing Solution Tactics for Effective, Scalable Personalized Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Capturing Solution Tactics for Effective, Scalable Personalized Learning./
作者:	Wang, Ke.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	136 p.
附註:	Source: Dissertation Abstracts International, Volume: 80-07(E), Section: B.
Contained By:	Dissertation Abstracts International80-07B(E).
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10936979
ISBN:	9780438929654

Capturing Solution Tactics for Effective, Scalable Personalized Learning.
Wang, Ke.

Capturing Solution Tactics for Effective, Scalable Personalized Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 136 p.

Source: Dissertation Abstracts International, Volume: 80-07(E), Section: B.

Thesis (Ph.D.)--University of California, Davis, 2018.

Recently the world has seen groundbreaking, revolutionary advancement in Artificial Intelligence (AI). As a result, machines now demonstrate comparable or in some cases superior intelligence to humans. Despite the new generation technology powered by AI and deep learning, education --- an area of fundamental importance --- remains largely unchanged. The traditional one-classroom-for-all approach still dominates schools around the world. Several years ago a new paradigm emerges aiming to digitize and broadcast actual classes through internet. This novel practice made many believe to be the future of education due to a major benefit: higher accessibility and lower cost compared to traditional education. On the other hand, online learning in fact does not provide a principle solution to problems that have hindered traditional education: lack of support that can lead learners feel frustrated and therefore become disengaged. Lately, online course vendors have seen a sharply increasing drop out rate which further validates the unsatisfactory students' learning experience.

ISBN: 9780438929654Subjects--Topical Terms:

523869
Computer science.

Capturing Solution Tactics for Effective, Scalable Personalized Learning.
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520 $a Recently the world has seen groundbreaking, revolutionary advancement in Artificial Intelligence (AI). As a result, machines now demonstrate comparable or in some cases superior intelligence to humans. Despite the new generation technology powered by AI and deep learning, education --- an area of fundamental importance --- remains largely unchanged. The traditional one-classroom-for-all approach still dominates schools around the world. Several years ago a new paradigm emerges aiming to digitize and broadcast actual classes through internet. This novel practice made many believe to be the future of education due to a major benefit: higher accessibility and lower cost compared to traditional education. On the other hand, online learning in fact does not provide a principle solution to problems that have hindered traditional education: lack of support that can lead learners feel frustrated and therefore become disengaged. Lately, online course vendors have seen a sharply increasing drop out rate which further validates the unsatisfactory students' learning experience.
520 $a To address the aforementioned deficiencies of traditional education practice, this dissertation proposes to leverage AI techniques for creating a dynamic and supportive learning workflow. In particular, it presents novel framework and algorithms to facilitate learners from four dimensions: solution generation, interactive tutoring, problem generation, and feedback generation. In each dimension, this dissertation demonstrates the efficiency and effectiveness of the proposed approach via a specific problem instance. At a high level, the key contribution is the idea of solution tactics (i.e. domain-specific problem-solving strategies) based learning. Specifically, teaching students problem-solving strategies and how to apply them in the problem-solving process, generating problems for students to practice the concept and finally providing feedback to mistakes students made when applying problem-solving strategies. This dissertation also listed several realizations of the new learning methodologies which improve students' learning quality and therefore should help to address the long-standing issues in the traditional education paradigm.
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