Recommendations for network research in learning analytics: To open a conversation

Oleksandra Poquet, Mohammed Saqr, Bodong Chen

Research output: Contribution to journalConference articlepeer-review

6 Scopus citations


Network science methods are widely adopted in learning analytics, an applied research area that focuses on the analysis of learning data to understand and improve learning. The workshop, taking place at the 11th International Learning Analytics and Knowledge conference, focused on the applications of network science in learning analytics. The workshop attracted over twenty researchers and practitioners working with network analysis and educational data. The workshop included work-in-progress and group-wide conversations about enhancing the quality of network research in learning analytics. The conversations were driven by concerns around reproducibility and interpretability currently discussed across research communities. This paper presents a snapshot of the workshop discussions beyond its work-in-progress papers. To this end, we summarize a literature review presented to the workshop participants, with the focus on the elements related to the reproducibility and interpretability of network research in education settings. We also provide a summary of the workshop discussions and conclude with suggested guidelines for the reporting of network methods to improve generalizability and reproducibility.

Original languageEnglish (US)
Pages (from-to)34-41
Number of pages8
JournalCEUR Workshop Proceedings
StatePublished - 2021
Event2021 NetSciLA Workshop "Using Network Science in Learning Analytics: Building Bridges towards a Common Agenda", NetSciLA 2021 - Virtual, Newport Beach, United States
Duration: Apr 12 2021 → …

Bibliographical note

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© 2021 Copyright for this paper by its authors.


  • Education
  • Learning analytics
  • Learning sciences
  • Network science
  • Recommendations


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