Reviewing Theoretical and Generalizable Text Network Analysis: Forma Mentis Networks in Cognitive Science

Oleksandra Poquet, Massimo Stella

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


Recommendations for network studies in learning analytics emphasize that network construction requires careful definitions of nodes, relationships between them, and network boundaries. Thus far, researchers in learning analytics have discussed how to operationalize interpersonal networks in learning settings. Analytical choices used in constructing networks of text have not been examined as much. By reviewing examples of text network analysis in learning analytics we demonstrate that convenience-based decisions for network construction are common, particularly when the ties in the text networks are defined as the co-occurrences of words or ideas. We argue that such an approach is limited in its potential to contribute to theory or generalize across studies. This submission presents an alternative approach to network representations of the text in learning settings, using the concept of Forma Mentis Networks. As reported in previous studies, Forma Mentis Networks are network representations either (1) elicited from individuals through free association tasks that capture valence or (2) constructed by analysts creating shared mental maps derived from text. Forma Mentis Networks is a theory-based and scalable approach complementary to the existing set of tools available for the analysis of teaching and learning.

Original languageEnglish
Pages (from-to)12-22
Number of pages11
JournalCEUR Workshop Proceedings
StatePublished - 2022
Externally publishedYes
EventNetSciLA 2022 Workshop "Networks and Learning Analytics: Addressing Educational Challenges", NetSciLA 2022 - Virtual, Online
Duration: 22 Mar 2022 → …


  • Text analysis
  • cognitive network science
  • learning analytics


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