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Technology and Pedagogy: Using Big D...

Brinton, Christopher Greg.

Technology and Pedagogy: Using Big Data to Enhance Student Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Technology and Pedagogy: Using Big Data to Enhance Student Learning.
作者:	Brinton, Christopher Greg.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, 2016
面頁冊數:	215 p.
附註:	Source: Dissertation Abstracts International, Volume: 77-11(E), Section: B.
附註:	Adviser: Mung Chiang.
Contained By:	Dissertation Abstracts International77-11B(E).
標題:	Electrical engineering.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10120407
ISBN:	9781339815893

Technology and Pedagogy: Using Big Data to Enhance Student Learning.
Brinton, Christopher Greg.

Technology and Pedagogy: Using Big Data to Enhance Student Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 215 p.

Source: Dissertation Abstracts International, Volume: 77-11(E), Section: B.

Thesis (Ph.D.)--Princeton University, 2016.

The "big data revolution" has penetrated many fields, from network monitoring to online retail. Education and learning are quickly becoming part of it, too, because today, course delivery platforms can collect unprecedented amounts of behavioral data about students as they interact with learning content online. This data includes, for example, each click made while watching a lecture video, while submitting an answer to a quiz question, or while posting a question on a discussion forum. The ability to capture this data presents novel opportunities to study the complex process by which learning occurs, and also raises interesting research questions around how behavioral data can be leveraged to improve the quality of each student's learning experience, especially as online learning is scaled in size at the apparent expense of efficacy.

ISBN: 9781339815893Subjects--Topical Terms:

454503
Electrical engineering.

Technology and Pedagogy: Using Big Data to Enhance Student Learning.
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520 $a The "big data revolution" has penetrated many fields, from network monitoring to online retail. Education and learning are quickly becoming part of it, too, because today, course delivery platforms can collect unprecedented amounts of behavioral data about students as they interact with learning content online. This data includes, for example, each click made while watching a lecture video, while submitting an answer to a quiz question, or while posting a question on a discussion forum. The ability to capture this data presents novel opportunities to study the complex process by which learning occurs, and also raises interesting research questions around how behavioral data can be leveraged to improve the quality of each student's learning experience, especially as online learning is scaled in size at the apparent expense of efficacy.
520 $a In this thesis, I detail three research thrusts we have undertaken in using big data to study learning and enhance pedagogy. First is Learning Data Analytics (LDA), in which we have developed new methods for representing student video-watching behaviors as compact sequences, extracted recurring patterns from these sequences and showed how certain ones are significantly correlated with performance, and used the results in the design of behavior-based, early detection algorithms for performance prediction. Second is Social Learning Networks (SLN), in which we have proposed a new model for social learning that combines the topical and structural aspects of discussions, used this model to determine the efficiency of existing discussions, and designed algorithms to encourage SLN formation around a more optimal state. Third is Integrated and Individualized Courses (IIC), in which we have developed two new learning technology systems---a student-facing, course delivery platform and an instructor-facing, analytics dashboard---that build models based on behavior, individualize the content delivered to students based on these models, and visualize certain components of the models to instructors. I will also discuss the extensions we are exploring in terms of additional data capture, data analytics, algorithms, system design, and user trials by deploying IIC in various learning scenarios.
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