Correlations between student connectivity and academic performance: A pandemic follow-up

Crossette, Nathan; Carr, Lincoln D.; Wilcox, Bethany R.

doi:10.1103/physrevphyseducres.19.010106

Cited by 2 publications

(1 citation statement)

References 25 publications

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“…In this study, the focus lies on identifying patterns of student behavior through SNA, rather than directly correlating these behaviors with academic performance, as not all students actively participated in the forum community. Similarly, Crossette et al [54] demonstrated that missing nodes have a tendency to shift correlations toward zero.…”

Section: Discussionmentioning

confidence: 91%

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.

show abstract

Section: Discussionmentioning

confidence: 91%

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

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

Correlations between student connectivity and academic performance: A pandemic follow-up

Cited by 2 publications

References 25 publications

Exploiting Properties of Student Networks to Enhance Learning in Distance Education

Exploiting Properties of Student Networks to Enhance Learning in Distance Education

Exploiting Properties of Student Networks to Enhance Learning in Distance Education

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