=Paper=
{{Paper
|id=Vol-3258/xpreface
|storemode=property
|title=Networks and Learning Analytics: Addressing Educational Challenges
|pdfUrl=https://ceur-ws.org/Vol-3258/xpreface.pdf
|volume=Vol-3258
|authors=Mohammed Saqr,Sonsoles López-Pernas,Angel Hernández-García,Miguel Angel Conde,Oleksandra Poquet
|dblpUrl=https://dblp.org/rec/conf/lak/X22
}}
==Networks and Learning Analytics: Addressing Educational Challenges==
https://ceur-ws.org/Vol-3258/xpreface.pdf
Networks and Learning Analytics:
Addressing Educational Challenges
Mohammed Saqr 1, Sonsoles López-Pernas 1, Ángel Hernández García 2, Miguel Ángel Conde
3 and Oleksandra Poquet 4
1 University of Eastern Finland, Yliopistokatu 2, Joensuu, 80100, Finland
2 Univerisidad Politécnica de Madrid, Av. Complutense 30, Madrid, 28040, Spain
3 University of León, Campus de Vegazana s/n, León, 24071, Spain
4 University of South Australia, City West Campus, Adelaide, 5000, Australia
Abstract
Network Analysis is an established method in learning analytics research. Network Analysis
has been used to analyze learners’ interactions, to inform learning design, and to model
students’ performance. The workshop entitled "Using Network Science in Learning
Analytics: Building Bridges towards a Common Agenda", carried out within the LAK2021
conference, resulted in valuable insights and outcomes: guidelines for better reporting,
methodological improvements, and discussions of several novel research threads.
Traditionally, the focus of the conversation has been on methodological issues of network
analysis. This year, we would like to extend the conversation by slightly shifting the focus
to what network analysis can do to improve learning and educational opportunities. As such,
this new edition of the workshop aims to build on the fruitful achievements of the previous
iteration to address new themes, which we refer to as “challenges and opportunities” in
relation to practice. This edition of the workshop sought contributions around examples of
applications and impact, including those that can help address societal challenges embedded
within educational practices and those that foster an open conversation about privacy and
ethical implications of network data.
Keywords 1
Social network analysis, network science, learning analytics, network analysis
Networks are the fundamental building blocks behind several related fields, such as Social Network
Analysis, Network Science and Network Analysis. In education, these terms have been loosely and
interchangeably employed across several domains of applications. Therefore, we use the term
network analysis to refer to any application of network-based techniques in education. Network
analysis predates the birth of learning analytics as a field. However, a significant upsurge of research
at the intersection of networks and education has been kindled by the growing interest in learning
analytics [1]. Such an upsurge has made network analysis methods gain popularity in learning
analytics research. The wealth, ease, and flexibility of network analysis have contributed to a wide
range of applications across several domains (Borgatti et al., 2009). Early applications of network
analysis included tools for the analysis of learners’ interactions and the patterns derived from those
interactions, as well as using SNA to inform learning (Lockyer et al., 2013). Combining network
analysis with other quantitative methods has augmented our understanding of collaborative learning
interactions and discourse [2] and improved the identification of relevant actors and roles in the
learning process [3], among others. Recently, a growing number of research articles have looked at
Proceedings of the NetSciLA22 workshop, March 22, 2022
EMAIL: mohammed.saqr@uef.fi (A. 1); sonsoles.lopez@uef.fi (A. 2); angel.hernandez@upm.es (A. 3); miguel.conde@unileon.es (A. 4);
Sasha.Poquet@unisa.edu.au (A. 5)
ORCID: 0000-0001-5881-3109 (A. 1); 0000-0002-9621-1392 (A. 2); 0000-0002-6549-9549 (A. 3); 0000-0001-5881-7775 (A. 4); 0000-0001-9782-
816X (A. 5)
© 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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the usefulness of network analysis as a method for modelling students’ performance [4] and
modelling novel interaction platforms, such as instant messaging [5], to mention a few.
The rapid development of the field of social network analysis and the closely related fields (e.g.,
network science, complexity science and psychological networks) has progressed our understanding
of the world: from the structure of genes to the spread of diseases and the development of efficient
algorithms [6]. Nonetheless, the pace of research in learning analytics has not harnessed the full
potential of emerging and rapidly-developing network methods [2,7]. Several recent systematic
reviews, discussion papers and scholars have highlighted some of the challenges in this research area:
predominance of descriptive methods, paucity of temporal network analysis, underusage of network
inference methods, lag of adoption of modern analytics techniques (e.g., psychological network
methods) and sub-optimal reporting [1,2,7]. The challenges faced by network researchers and
educationists can be varied, complex and rapidly changing. However, issues that address
methodological rigor and impact are arguably more urgent to address. Rigor, better reporting and
replicability allow researchers to build on research findings and advance our understanding of theory
and practice [2], while impactful research helps researchers, practitioners and society at large better
adopt the methods.
This workshop aims to address new themes, building on the fruitful accomplishments of the
previous iterations. The themes emerge as “challenges and opportunities” in relation to practice. The
workshop was designed to foster the sharing of perspectives, strengthen the community of scholars
working in the network community, and generate new ideas regarding the future of the field. To
address the current challenges of SNA in the field of learning analytics, participants were invited to
contribute their latest research in one of the following themes:
• How can methodological rigor and reporting be balanced with practical relevance?
• How can researchers address the issues of practical applications and impact? In other words,
how can we translate research results from academic and scholarly publications into
actionable tools or methods that teachers can use in their teaching practice.
• How can network approaches inform socio-emotional and communication skills?
• How can network approaches contribute to the development of equitable educational
practices?
• What are the ethical and privacy applications for using network analysis in learning
analytics?
• How to implement successful network analytical applications that analyze systems to help
advance impact in learning analytics?
• What interdisciplinary bridges between the field and other closely related fields can
contribute to addressing educational challenges via network analytics approaches?
• How can educational researchers use modern network methods (e.g., psychological networks,
inference network methods and network science) to advance current theoretical models and
frameworks?
• How can education researchers contribute to the development of novel network methods,
measures or techniques that considers the contextual factors that are specific to learning and
teaching?
• How can network researchers contribute to more reproducible research?
The workshop also welcomed contributions discussing the potentials of novel methods or novel
applications of existing methods and reflections on their relevance to practical applications, including
but not limited to:
• Recent advances in SNA methods and approaches including new tools, measures, and
techniques applied to learning.
• Innovative data collection, analysis, presentation, and visualization methods.
• Temporal networks, and temporal aspects of networks in general.
• Psychological networks.
• Generative network models.
• Software demonstration for analysis of learning networks.
Acknowledgements
The paper is co-funded by the Academy of Finland for the project TOPEILA, Decision Number 350560
which was received by the first author. This study is also partially funded by the Erasmus+ program of
the European Union within the project ENVISION_2027 grant number (2020-1-FI01-KA226-HE-
092653).
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[2] O. Poquet, M. Saqr, B. Chen, Recommendations for Network Research in Learning Analytics: To
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