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001			978-3-030-89166-4
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020			_a9783030891664 _9978-3-030-89166-4
024	7		_a10.1007/978-3-030-89166-4 _2doi
050		4	_aLB1028.43-1028.75
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082	0	4	_a371,334 _223
100	1		_aYassine, Sahar. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aAnalysing Users' Interactions with Khan Academy Repositories _h[electronic resource] / _cby Sahar Yassine, Seifedine Kadry, Miguel-Ángel Sicilia.
250			_a1st ed. 2021.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2021.
300			_aXVI, 88 p. 26 illus., 23 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_a1. 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			_aThis 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	_aEducation _xData processing.
650		0	_aEducational technology.
650		0	_aArtificial intelligence _xData processing.
650	1	4	_aComputers and Education.
650	2	4	_aDigital Education and Educational Technology.
650	2	4	_aData Science.
700	1		_aKadry, Seifedine. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aSicilia, Miguel-Ángel. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783030891657
776	0	8	_iPrinted edition: _z9783030891671
776	0	8	_iPrinted edition: _z9783030891688
856	4	0	_uhttps://doi.org/10.1007/978-3-030-89166-4
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