Welcome to the InfoSci Platform
Reference Hub1
Undirected Bipartite Networks as an Alternative Methodology to Probabilistic Exploration: Online Interaction and Academic Attainment in MOOC

Undirected Bipartite Networks as an Alternative Methodology to Probabilistic Exploration: Online Interaction and Academic Attainment in MOOC

Juan-Francisco Martínez-Cerdá, Joan Torrent-Sellens
Copyright: © 2018 |Pages: 20
ISBN13: 9781522528142|ISBN10: 1522528148|EISBN13: 9781522528159
DOI: 10.4018/978-1-5225-2814-2.ch005
Cite Chapter Cite Chapter

MLA

Martínez-Cerdá, Juan-Francisco, and Joan Torrent-Sellens. "Undirected Bipartite Networks as an Alternative Methodology to Probabilistic Exploration: Online Interaction and Academic Attainment in MOOC." Graph Theoretic Approaches for Analyzing Large-Scale Social Networks, edited by Natarajan Meghanathan, IGI Global, 2018, pp. 75-94. https://doi.org/10.4018/978-1-5225-2814-2.ch005

APA

Martínez-Cerdá, J. & Torrent-Sellens, J. (2018). Undirected Bipartite Networks as an Alternative Methodology to Probabilistic Exploration: Online Interaction and Academic Attainment in MOOC. In N. Meghanathan (Ed.), Graph Theoretic Approaches for Analyzing Large-Scale Social Networks (pp. 75-94). IGI Global. https://doi.org/10.4018/978-1-5225-2814-2.ch005

Chicago

Martínez-Cerdá, Juan-Francisco, and Joan Torrent-Sellens. "Undirected Bipartite Networks as an Alternative Methodology to Probabilistic Exploration: Online Interaction and Academic Attainment in MOOC." In Graph Theoretic Approaches for Analyzing Large-Scale Social Networks, edited by Natarajan Meghanathan, 75-94. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-2814-2.ch005

Export Reference

Mendeley
Favorite

Abstract

This chapter explores how graph analysis techniques are able to complement and speed up the process of learning analytics and probability theory. It uses a sample of 2,353 e-learners from six European countries (France, Germany, Greece, Poland, Portugal, and Spain), who were enrolled in their first year of open online courses offered by HarvardX and MITX. After controlling the variables for socio-demographics and online content interactions, the research reveals two main results relating student-content interactions and online behavior. First, a multiple binary logistic regression model tests that students who explore online chapters are more likely to be certified. Second, the authors propose an algorithm to generate an undirected bipartite network based on tabular data of student-content interactions (2,392 nodes, 25,883 edges, a visual representation based on modularity, degree and ForceAtlas2 layout); the graph shows a clear relationship between interactions with online chapters and chances of getting certified.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.