語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
FindBook
Google Book
Amazon
博客來
Data Mining Student Activity Patterns in an Interactive Activity-Based STEM Learning Environment.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Data Mining Student Activity Patterns in an Interactive Activity-Based STEM Learning Environment./
作者:
Brownawell, Susan E.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2020,
面頁冊數:
71 p.
附註:
Source: Masters Abstracts International, Volume: 82-08.
Contained By:
Masters Abstracts International82-08.
標題:
Computer science. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27993916
ISBN:
9798569979998
Data Mining Student Activity Patterns in an Interactive Activity-Based STEM Learning Environment.
Brownawell, Susan E.
Data Mining Student Activity Patterns in an Interactive Activity-Based STEM Learning Environment.
- Ann Arbor : ProQuest Dissertations & Theses, 2020 - 71 p.
Source: Masters Abstracts International, Volume: 82-08.
Thesis (M.S.)--University of Missouri - Columbia, 2020.
This item must not be sold to any third party vendors.
Jupyter Notebook is gaining in popularity for STEM instruction and activity-based learning. This platform for sharing interactive documents via a web interface allows instructors to combine a variety of media together with interactive and editable code, providing rich opportunities for an active learning pedagogy. Other online learning environments, such as Canvas and Moodle, provide or integrate learning analytics for the use of administrators, educators, and students to improve learning outcomes; however, these platforms lack the rich learning environment of Jupyter Notebook. Also, with increasing interest in online learning, research communities have arisen for Learning Analytics and Educational Data Mining. Unfortunately, these research communities have not yet begun to address the Jupyter Notebook learning environment. The University of Missouri College of Engineering offers a Program of Study in Data Science (PSDS) under contract with the National Geospatial Intelligence Agency (NGA.) This program is delivered online, making heavy use of Jupyter notebooks served by JupyterHub for active engagement with course content. The PSDS infrastructure uses the Graylog log management program to collect Jupyter logs, which are stored in an integrated Elasticsearch document store for a period of months. The PSDS program provides an excellent case study for a proof-of-concept in applying learning analytics to the Jupyter learning environment. This thesis consists of two major parts. (1) Mining the Graylog system to extract useful log messages, transformation of those messages into student-activities features, and loading the data into a PostgreSQL database for long-term storage. (2) Developing a variety of visualizations of student activity for administrators, instructors and students. The pedological structure of PSDS courses allows unique insights into student engagement with the course material. Finally, recommendations are made for the development of a more comprehensive logging system and additional analyses that could be performed.
ISBN: 9798569979998Subjects--Topical Terms:
523869
Computer science.
Subjects--Index Terms:
Educational data mining
Data Mining Student Activity Patterns in an Interactive Activity-Based STEM Learning Environment.
LDR
:03270nmm a2200409 4500
001
2344338
005
20220523132433.5
008
241004s2020 ||||||||||||||||| ||eng d
020
$a
9798569979998
035
$a
(MiAaPQ)AAI27993916
035
$a
AAI27993916
040
$a
MiAaPQ
$c
MiAaPQ
100
1
$a
Brownawell, Susan E.
$3
3683108
245
1 0
$a
Data Mining Student Activity Patterns in an Interactive Activity-Based STEM Learning Environment.
260
1
$a
Ann Arbor :
$b
ProQuest Dissertations & Theses,
$c
2020
300
$a
71 p.
500
$a
Source: Masters Abstracts International, Volume: 82-08.
500
$a
Advisor: Scott, Grant J.
502
$a
Thesis (M.S.)--University of Missouri - Columbia, 2020.
506
$a
This item must not be sold to any third party vendors.
520
$a
Jupyter Notebook is gaining in popularity for STEM instruction and activity-based learning. This platform for sharing interactive documents via a web interface allows instructors to combine a variety of media together with interactive and editable code, providing rich opportunities for an active learning pedagogy. Other online learning environments, such as Canvas and Moodle, provide or integrate learning analytics for the use of administrators, educators, and students to improve learning outcomes; however, these platforms lack the rich learning environment of Jupyter Notebook. Also, with increasing interest in online learning, research communities have arisen for Learning Analytics and Educational Data Mining. Unfortunately, these research communities have not yet begun to address the Jupyter Notebook learning environment. The University of Missouri College of Engineering offers a Program of Study in Data Science (PSDS) under contract with the National Geospatial Intelligence Agency (NGA.) This program is delivered online, making heavy use of Jupyter notebooks served by JupyterHub for active engagement with course content. The PSDS infrastructure uses the Graylog log management program to collect Jupyter logs, which are stored in an integrated Elasticsearch document store for a period of months. The PSDS program provides an excellent case study for a proof-of-concept in applying learning analytics to the Jupyter learning environment. This thesis consists of two major parts. (1) Mining the Graylog system to extract useful log messages, transformation of those messages into student-activities features, and loading the data into a PostgreSQL database for long-term storage. (2) Developing a variety of visualizations of student activity for administrators, instructors and students. The pedological structure of PSDS courses allows unique insights into student engagement with the course material. Finally, recommendations are made for the development of a more comprehensive logging system and additional analyses that could be performed.
590
$a
School code: 0133.
650
4
$a
Computer science.
$3
523869
650
4
$a
Curriculum development.
$3
684418
650
4
$a
Educational administration.
$3
2122799
650
4
$a
Educational leadership.
$3
529436
650
4
$a
Science education.
$3
521340
650
4
$a
Mathematics education.
$3
641129
650
4
$a
Educational technology.
$3
517670
650
4
$a
Information technology.
$3
532993
653
$a
Educational data mining
653
$a
Jupyter
653
$a
Learning analytics
690
$a
0984
690
$a
0280
690
$a
0489
690
$a
0514
690
$a
0449
690
$a
0710
690
$a
0727
690
$a
0714
710
2
$a
University of Missouri - Columbia.
$b
Computer Science.
$3
3283876
773
0
$t
Masters Abstracts International
$g
82-08.
790
$a
0133
791
$a
M.S.
792
$a
2020
793
$a
English
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27993916
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9466776
電子資源
11.線上閱覽_V
電子書
EB
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入