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Adaptive Guidance for Online Learnin...

Bassen, Jonathan.

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Adaptive Guidance for Online Learning Environments.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Adaptive Guidance for Online Learning Environments./
作者:	Bassen, Jonathan.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:	70 p.
附註:	Source: Dissertations Abstracts International, Volume: 82-02, Section: B.
Contained By:	Dissertations Abstracts International82-02B.
標題:	Educational technology. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28103879
ISBN:	9798662510340

Adaptive Guidance for Online Learning Environments.
Bassen, Jonathan.

Adaptive Guidance for Online Learning Environments. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 70 p.

Source: Dissertations Abstracts International, Volume: 82-02, Section: B.

Thesis (Ph.D.)--Stanford University, 2020.

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

In the last decade, online learning platforms have dramatically increased access to educational materials, and replaced and supplemented traditional co-located instruction. Throughout this shift, instructors have struggled with how best to create and deploy materials to online platforms and understand their effectiveness when scaled across hundreds or thousands of learners. To address these complementary problems, this thesis introduces new systems and methods for both learning analytics and adaptive instruction. In OARS, we demonstrate a real-time learning analytics system deployed across more than ten online courses with tens of thousands of learners. We then report on the value of learning analytics from the perspective of course instructors. Learning from these perspectives, we next introduce a system for adaptively scheduling educational activities through reinforcement learning (RL). Without any skill labels, this model learns how to assign educational activities in a way that maximizes learning gains while minimizing redundant work. Finally, in a randomized controlled experiment, we show that our RL scheduling algorithm helped to improve the educational experience for online learners by reducing the number of learning activities required to produce the same learning gains.

ISBN: 9798662510340Subjects--Topical Terms:

517670
Educational technology.
Subjects--Index Terms:

Reinforcement learning

Adaptive Guidance for Online Learning Environments.
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