Exploring study profiles of Computer Science students with Social Network Analysis

Flores, Nidia López; Íslind, Anna Sigríður; Óskarsdóttir, María

doi:10.24251/hicss.2022.214

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…Additionally, Sturludottir et al [24] found strong similarities between the networks created by students with the same course choices and their actual major specialization in their latter studies. The changes that a network of a forum community may undergo during an academic year were studied by Tsoni et al [25] and Lopez-Flores et al [26]. These two types of research showed significant changes in graph density (that measures the number of ties between the nodes) and participation.…”

Section: Related Workmentioning

confidence: 99%

Exploiting Properties of Student Networks to Enhance Learning in Distance Education

Tsoni,

Paxinou,

Gkoulalas-Divanis

et al. 2024

Information

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Distance Learning has become the “new normal”, especially during the pandemic and due to the technological advances that are incorporated into the teaching procedure. At the same time, the augmented use of the internet has blurred the borders between distance and conventional learning. Students interact mainly through LMSs, leaving their digital traces that can be leveraged to improve the educational process. New knowledge derived from the analysis of digital data could assist educational stakeholders in instructional design and decision making regarding the level and type of intervention that would benefit learners. This work aims to propose an analysis model that can capture the students’ behaviors in a distance learning course delivered fully online, based on the clickstream data associated with the discussion forum, and additionally to suggest interpretable patterns that will support education administrators and tutors in the decision-making process. To achieve our goal, we use Social Network Analysis as networks represent complex interactions in a meaningful and easily interpretable way. Moreover, simple or complex network metrics are becoming available to provide valuable insights into the students’ social interaction. This study concludes that by leveraging the imprint of these actions in an LMS and using metrics of Social Network Analysis, differences can be spotted in the communicational patterns that go beyond simple participation recording. Although HITS and PageRank algorithms were created with completely different targeting, it is shown that they can also reveal methodological features in students’ communicational approach.

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Section: Related Workmentioning

confidence: 99%

Exploiting Properties of Student Networks to Enhance Learning in Distance Education

Tsoni,

Paxinou,

Gkoulalas-Divanis

et al. 2024

Information

View full text Add to dashboard Cite

show abstract

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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Exploiting Properties of Student Networks to Enhance Learning in Distance Education

Tsoni,

Paxinou,

Gkoulalas-Divanis

et al. 2024

Preprint

View full text Add to dashboard Cite

Distance Learning has become the new standard, especially after the pandemic and due to the technological advances, that are incorporated into the teaching procedure. At the same time, the augmented use of the internet has blurred the borders between distance and conventional learning. Students interact mainly through LMSs, leaving their digital traces that can be leveraged to improve the educational process. This work aims to propose a model that can capture the students' behaviors based on the clickstream data associated with the discussion forum and additionally to suggest interpretable patterns that will support education administrators and tutors in the decision-making process. To achieve our goal, we use Social Network Analysis (SNA) as networks represent complex interactions in a meaningful and easily interpretable way. Moreover, simple or complex network metrics are becoming available to provide valuable insights into the students’ social interaction. This study concludes that by leveraging the imprint of these actions in an LMS and using metrics of SNA, differences can be spotted in the communicational patterns that go beyond simple participation recording. Although HITS and PageRank algorithms were created with completely different targeting, it is shown that they can also reveal methodological features in students’ communicational approach.

show abstract

Exploring study profiles of Computer Science students with Social Network Analysis

Cited by 3 publications

References 24 publications

Exploiting Properties of Student Networks to Enhance Learning in Distance Education

Exploiting Properties of Student Networks to Enhance Learning in Distance Education

Community Detection in Learning Networks Using R

Exploiting Properties of Student Networks to Enhance Learning in Distance Education

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