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008			211115s2021 sz | s |||| 0|eng d
020			_a9783030891664
024	7		_a10.1007/978-3-030-89166-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aLB1028.73 _b2021 EB
100	1		_aYassine, Sahar _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aAnalysing Users' Interactions with Khan Academy Repositories _cby Sahar Yassine, Seifedine Kadry, Miguel-Ángel Sicilia.
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Publising _c2021
300			_a1 recurso en línea (XVI, 88 páginas) _b26 ilustraciones, 23 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aComputer Science (SpringerNature-11645)
490	0		_aComputer Science (R0) (SpringerNature-43710)
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	3		_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.
988			_aSpringer_Computer_2021
650		7	_2embne _9141131 _aSistemas expertos (Informática)
700	1		_aKadry, Seifedine _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aSicilia, Miguel-Ángel _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iPrinted edition: _z9783030891657
776	0	8	_iPrinted edition: _z9783030891671
776	0	8	_iPrinted edition: _z9783030891688
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-89166-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2022 _dz _eh _zSI