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.
About the Author: Sahar Yassine has a PhD in Computer Science from Alcalá University, Spain. Her Doctoral research investigates interactions with online learning and focuses on detecting communities in e‐learning environments. She has interests in educational repositories, learning‐technology and social network analysis techniques.Seifedine Kadry has a bachelor's degree in applied mathematics in 1999 from Lebanese University, MS degree in Computation in 2002 from Reims University (France) and EPFL (Lausanne), PhD in 2007 from Blaise Pascal University (France), HDR degree in Engineering Science in 2017 from Rouen University. At present his research focuses on education using technology, smart cities, system prognostics, stochastic systems, and probability and reliability analysis. He is a fellow of IET, fellow of ACSIT and ABET program evaluator. He is a full professor of data science at Noroff University College, Kristiansand, Norway.
Miguel‐Ángel Sicilia is currently full professor at the Computer Science Department of the University of Alcalá (Madrid, Spain). He holds degrees in Computer Science (Pontifical University of Salamanca) and in Information Science (University of Alcalá) and a PhD in Computer Science from Carlos III University. Before joining academia, Miguel Angel was part of the R&D and e‐commerce architecture staff of iSOCO. Miguel Angel has developed his research activity in the fields of Artificial Intelligence, machine learning and analytics applied to different fields, including learning, health, computational science, command and control systems and information security.
