LEADER 04526nam 22006975 450 001 9910510580003321 005 20240708161808.0 010 $a3-030-89166-6 024 7 $a10.1007/978-3-030-89166-4 035 $a(MiAaPQ)EBC6805051 035 $a(Au-PeEL)EBL6805051 035 $a(CKB)19421981800041 035 $a(OCoLC)1285781403 035 $a(DE-He213)978-3-030-89166-4 035 $a(PPN)258845392 035 $a(EXLCZ)9919421981800041 100 $a20211115d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAnalysing Users' Interactions with Khan Academy Repositories /$fby Sahar Yassine, Seifedine Kadry, Miguel-Ángel Sicilia 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (98 pages) 311 08$aPrint version: Yassine, Sahar Analysing Users' Interactions with Khan Academy Repositories Cham : Springer International Publishing AG,c2021 9783030891657 320 $aIncludes bibliographical references and index. 327 $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. 330 $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. 606 $aEducation$xData processing 606 $aEducational technology 606 $aArtificial intelligence$xData processing 606 $aComputers and Education 606 $aDigital Education and Educational Technology 606 $aData Science 606 $aEnsenyament assistit per ordinador$2thub 606 $aInternet en l'ensenyament$2thub 606 $aTecnologia educativa$2thub 608 $aLlibres electrònics$2thub 608 $aLlibres electrònics$2thub 615 0$aEducation$xData processing. 615 0$aEducational technology. 615 0$aArtificial intelligence$xData processing. 615 14$aComputers and Education. 615 24$aDigital Education and Educational Technology. 615 24$aData Science. 615 7$aEnsenyament assistit per ordinador 615 7$aInternet en l'ensenyament 615 7$aTecnologia educativa 676 $a371.3344678 700 $aYassine$b Sahar$01068787 702 $aSicilia$b Miguel-Angel$f1973- 702 $aKadry$b Seifedine$f1977- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910510580003321 996 $aAnalysing users' interactions with Khan Academy repositories$92905722 997 $aUNINA