國立虎尾科技大學 |

Analysing Users' Interactions with Khan Academy Repositories

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Analysing Users' Interactions with Khan Academy Repositories / by Sahar Yassine, Seifedine Kadry, Miguel-Ángel Sicilia.
作者:	Yassine, Sahar.
其他作者:	Kadry, Seifedine.
面頁冊數:	XVI, 88 p. 26 illus., 23 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Education—Data processing. -
電子資源:	https://doi.org/10.1007/978-3-030-89166-4
ISBN:	9783030891664

Analysing Users' Interactions with Khan Academy Repositories
Yassine, Sahar.

Analysing Users' Interactions with Khan Academy Repositories [electronic resource] /by Sahar Yassine, Seifedine Kadry, Miguel-Ángel Sicilia. - 1st ed. 2021. - XVI, 88 p. 26 illus., 23 illus. in color.online resource.

1. Introduction to Online Learning Repositories -- 2. Research Objectives -- 3. Literature Review -- 4. Methodology -- 5. Data acquisition -- 6. Assessing Online Learning Repository with Descriptive Statistical Analysis -- 7. Detecting Communities in Online Learning Repository -- 8. SNA Measures and Users’ Interactions -- 9. Conclusions -- 10. Future work.

This book addresses the need to explore user interaction with online learning repositories and the detection of emergent communities of users. This is done through investigating and mining the Khan Academy repository; a free, open access, popular online learning repository addressing a wide content scope. It includes large numbers of different learning objects such as instructional videos, articles, and exercises. The authors conducted descriptive analysis to investigate the learning repository and its core features such as growth rate, popularity, and geographical distribution. The authors then analyzed this graph and explored the social network structure, studied two different community detection algorithms to identify the learning interactions communities emerged in Khan Academy then compared between their effectiveness. They then applied different SNA measures including modularity, density, clustering coefficients and different centrality measures to assess the users’ behavior patterns and their presence. By applying community detection techniques and social network analysis, the authors managed to identify learning communities in Khan Academy’s network. The size distribution of those communities found to follow the power-law distribution which is the case of many real-world networks. Despite the popularity of online learning repositories and their wide use, the structure of the emerged learning communities and their social networks remain largely unexplored. This book could be considered initial insights that may help researchers and educators in better understanding online learning repositories, the learning process inside those repositories, and learner behavior.

ISBN: 9783030891664

Standard No.: 10.1007/978-3-030-89166-4doiSubjects--Topical Terms:

1253610
Education—Data processing.

LC Class. No.: LB1028.43-1028.75

Dewey Class. No.: 374.26

Analysing Users' Interactions with Khan Academy Repositories
LDR:03459nam a22003975i 4500 001 1057156
003 DE-He213
005 20211115152416.0
007 cr nn 008mamaa
008 220103s2021 sz | s |||| 0|eng d
020 $a 9783030891664 $9 978-3-030-89166-4
024 7 $a 10.1007/978-3-030-89166-4 $2 doi
035 $a 978-3-030-89166-4
050 4 $a LB1028.43-1028.75
072 7 $a JNV $2 bicssc
072 7 $a EDU039000 $2 bisacsh
072 7 $a JNV $2 thema
082 0 4 $a 374.26 $2 23
100 1 $a Yassine, Sahar. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1362571
245 1 0 $a Analysing Users' Interactions with Khan Academy Repositories $h [electronic resource] / $c by Sahar Yassine, Seifedine Kadry, Miguel-Ángel Sicilia.
250 $a 1st ed. 2021.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a XVI, 88 p. 26 illus., 23 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a 1. Introduction to Online Learning Repositories -- 2. Research Objectives -- 3. Literature Review -- 4. Methodology -- 5. Data acquisition -- 6. Assessing Online Learning Repository with Descriptive Statistical Analysis -- 7. Detecting Communities in Online Learning Repository -- 8. SNA Measures and Users’ Interactions -- 9. Conclusions -- 10. Future work.
520 $a This book addresses the need to explore user interaction with online learning repositories and the detection of emergent communities of users. This is done through investigating and mining the Khan Academy repository; a free, open access, popular online learning repository addressing a wide content scope. It includes large numbers of different learning objects such as instructional videos, articles, and exercises. The authors conducted descriptive analysis to investigate the learning repository and its core features such as growth rate, popularity, and geographical distribution. The authors then analyzed this graph and explored the social network structure, studied two different community detection algorithms to identify the learning interactions communities emerged in Khan Academy then compared between their effectiveness. They then applied different SNA measures including modularity, density, clustering coefficients and different centrality measures to assess the users’ behavior patterns and their presence. By applying community detection techniques and social network analysis, the authors managed to identify learning communities in Khan Academy’s network. The size distribution of those communities found to follow the power-law distribution which is the case of many real-world networks. Despite the popularity of online learning repositories and their wide use, the structure of the emerged learning communities and their social networks remain largely unexplored. This book could be considered initial insights that may help researchers and educators in better understanding online learning repositories, the learning process inside those repositories, and learner behavior.
650 0 $a Education—Data processing. $3 1253610
650 0 $a Education. $3 555912
650 0 $a Data structures (Computer science). $3 680370
650 0 $a Artificial intelligence. $3 559380
650 1 4 $a Computers and Education. $3 669806
650 2 4 $a Education, general. $3 1068901
650 2 4 $a Data Structures and Information Theory. $3 1211601
650 2 4 $a Artificial Intelligence. $3 646849
700 1 $a Kadry, Seifedine. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1062738
700 1 $a Sicilia, Miguel-Ángel. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1362572
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030891657
776 0 8 $i Printed edition: $z 9783030891671
776 0 8 $i Printed edition: $z 9783030891688
856 4 0 $u https://doi.org/10.1007/978-3-030-89166-4
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)