A Web Services-Based Application for LMS Data Extraction and Processing for Social Network Analysis

Chaparro-Peláez, Julián; Acquila-Natale, Emiliano; Iglesías-Pradas, Santiago; Suárez-Navas, Ignacio

doi:10.1007/978-3-319-22204-2_11

Cited by 4 publications

References 16 publications

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Community Detection in Learning Networks Using R

Hernández-García,

Cuenca-Enrique,

Traxler

et al. 2024

Learning Analytics Methods and Tutorials

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In the field of social network analysis, understanding interactions and group structures takes a center stage. This chapter focuses on finding such groups, constellations or ensembles of actors who can be grouped together, a process often referred to as community detection, particularly in the context of educational research. Community detection aims to uncover tightly knit subgroups of nodes who share strong connectivity within a network or have connectivity patterns that demarcates them from the others. This chapter explores various algorithms and techniques to detect these groups or cohesive clusters. Using well-known R packages, the chapter presents the core approach of identifying and visualizing densely connected subgroups in learning networks.

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Community Detection in Learning Networks Using R

Hernández-García,

Cuenca-Enrique,

Traxler

et al. 2024

Learning Analytics Methods and Tutorials

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A Broad Collection of Datasets for Educational Research Training and Application

López-Pernas,

Saqr,

Conde

et al. 2024

Learning Analytics Methods and Tutorials

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In this chapter, we present the main types of data that are used in learning analytics research. Learning analytics has grown to encompass the digital trails left by online learning technologies—clicks, events, and interactions—, sensor data and self-reports among others. We present a collection of curated real-life open datasets that represent the most common types of educational data. The datasets have been collected from diverse sources such as learning management systems, online forums, and surveys. These datasets are used throughout the book to illustrate methods of analysis such as sequence analysis, social network analysis, Markov models, predictive analytics and structure equation modeling, to mention a few. Each data set in the chapter is presented with its context, main properties, links to the original source, as well as a brief exploratory data analysis.

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