Analysing Users’ Interactions with Khan Academy Repositories

by Miguel-Angel Sicilia, Sahar Yassine, Seifedine Kadry

Length: 104 pages
Edition: 1
Language: English
Publisher: Springer
Publication Date: 2021-11-16
ISBN-10: 3030891658
ISBN-13: 9783030891657
Sales Rank: #0 (See Top 100 Books)

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.

Preface
Acknowledgements
Contents
List of Figures
List of Tables
Chapter 1: Introduction to Online Learning Repositories
	References
Chapter 2: Research Objectives
Chapter 3: Literature Review
	3.1 Historical Background on Online Learning Repositories
	3.2 Online Learning Repositories and Descriptive Analysis
	3.3 Online Learning Repositories and Statistical Inferential Analysis
	3.4 Online Learning Repositories and Social Network Analysis (SNA)
	3.5 Community Detection Algorithms and Techniques
	3.6 Online Learning Repositories and Community Detection Methods
	References
Chapter 4: Methodology
	References
Chapter 5: Data Acquisition
	5.1 Dataset
	5.2 Data Preparation
	References
Chapter 6: Assessing Online Learning Repository with Descriptive Statistical Analysis
	6.1 General Descriptive Analysis
		6.1.1 Growth of KA Repository
		6.1.2 Geographical Distribution of KA Users
		6.1.3 Average Duration of KA Videos
		6.1.4 Number of Videos Completed by Users
	6.2 Interactions-Related Analysis
		6.2.1 Evolution and Distribution of Interactions Around KA Contents
		6.2.2 Number of Users’ Interactions per Video
	6.3 Assessing the Relation Between Learning Objects and Users’ Interactions Using Inferential Analysis
		6.3.1 Identifying the Collected Metrics
		6.3.2 Classifying Learning Material into Interaction Profiles
		6.3.3 Relationship Between Domain Type and Interaction Profiles
		6.3.4 Relationship Between the Publishing Year of Learning Material and the Interaction Profiles
		6.3.5 Relationship Between Interaction Profiles, Video Length and Reuse Rate
	References
Chapter 7: Detecting Communities in Online Learning Repository
	7.1 Applying Parallel Label Propagation Algorithm (PLP)
	7.2 Applying Parallel Louvain Algorithm (PLM)
	7.3 Applying Louvain Method and Label Propagation Algorithm (LPA) Using NetworkX
	References
Chapter 8: SNA Measures and Users’ Interactions
	8.1 Community Size Distribution
	8.2 The Largest Community
	8.3 Modularity
	8.4 Density
	8.5 Clustering Coefficient
	8.6 Centrality Measures
		8.6.1 Degree Centrality
		8.6.2 Eigenvector Centrality (EVC)
		8.6.3 Closeness Centrality
		8.6.4 Betweenness Centrality
	8.7 Centrality Measures within Communities
	References
Chapter 9: Conclusions
	9.1 R1: Assessing Online Learning Repository with Descriptive Statistical Analysis
	9.2 R2: Detecting Communities in Online Learning Repository
	9.3 R3: SNA Measures and Users’ Interactions
	References
Chapter 10: Future Work
	References