Special Section on Networks in Learning Analytics- Call for Papers
- Bodong Chen, University of Minnesota-Twin Cities, USA , email@example.com
- Sasha Poquet, University of South Australia, firstname.lastname@example.org
- Tobias Hecking, German Aerospace Center, email@example.com
AIMS & SCOPE
Since the first Learning Analytics and Knowledge (LAK) conference, researchers and practitioners in Learning Analytics (LA) have integrated network analysis into their understanding of learning. Computationally, networks are in line with a socio-technical view on digital learning (Siemens 2004; Haythornthwaite and De Laat 2012). Representationally, networks help communicate patterns such as who talked to whom online (Bakharia and Dawson 2011). Analytically, network analysis presents a suite of methods to generate network metrics useful for assessing learning and learner interactions (Lund and Suthers 2016; Gašević et al. 2013; Göhnert et al. 2013).
Network science is the study of networks models for data characterised by dependence between and within variables (Brandes et al. 2013). From a rich history of social science research, network science has evolved in its use of mathematical, computational, and physical principles for the study of systems, particularly in the domain of complex networks. Network science has benefited from the increased availability of digital data (e.g. from Twitter and other social media tools) and advanced possibilities for computer simulations. Recent developments have led to new domains of research, including networked communication (Welles and González-Bailón 2020), networks in cognitive science (Baronchelli et al. 2013), and network psychometrics (Epskamp et al. 2016).
Network analysis and modelling in LA has been limited. Much of network analysis in LA has relied on the conceptual principles of social network analysis (SNA), developed for self-reported human relationships. For instance, a review of SNA in computer-supported collaborative learning (CSCL) showed that network studies were generally limited to descriptive reporting of SNA results and one-mode networks of learners, despite CSCL’s emphasis on artefacts (Dado & Bodemer, 2017). Yet, learning in socio-technical systems -- at the individual, group and community levels -- is mediated by diverse actors that operate at these different levels as well as at varying time-scales. Further, mechanisms generating socio-technical networks may differ from those driving friendship and trust relationships (Poquet et al., 2020). In addition, networks with one type of node or type of information fail to capture higher order learning constructs well.
For a more holistic understanding of learning, the LA community needs to move towards examining multiple types of nodes and edges (e.g., Contractor, 2009) with semantic, temporal, or epistemic perspectives integrated (Hecking, Chounta, & Hoppe, 2016).
Insightful mechanisms for network generation as well as measures reflecting learning in a socio-technical system require a composite of indices from a multidimensional network. Too often basic centrality measures are derived from an ill-defined network representation to serve as proxies of learning constructs (Bockholt & Zweig, 2018). Network topology and generative processes to explain network formation mechanisms are similarly under-explored. Exciting work can be done to construct models in light of learning theories, map learning constructs onto components of the network, conduct inferential modeling of network factors that play a role in learning, and derive new network metrics. Creating such metrics so that they are theoretically aligned, scientifically rigorous, empirically tested, and actionable, is a scholarly challenge, as well as an exciting opportunity for the field.
To advance network perspectives in LA, this special section will bring together a set of papers that break new ground, bridge research communities, and collectively shape strategies of future work in this important sub-field of LA. This work is timely and much needed.
This special section invites submissions that apply networked perspectives to learning analytics research or applications. To advance scientific evidence using heterogeneous sources of evidence of learning and knowledge generation, we invite conceptual, methodological, and empirical contributions that leverage the networked nature of learning and knowing. Submissions can present examples from the rich landscape of learning, such as learning in classrooms, open collaboration in online communities, professional learning networks, knowledge processes at work, and team analytics.
Submissions may accommodate diverse data sources in learning settings, enable insights into network dynamics and network formation in learning settings, propose network metrics relevant for learning settings that combine heterogeneous information important for network dynamics, and/or suggest meaningful integration of network analytical approaches with other methodologies (e.g. psychometrics, text and content analyses). Empirical contributions that build on established network techniques and analyze more than one case study are also encouraged. Finally, we strongly encourage submissions from authors who have mobilized network-related insights and analytics to bring about changes in educational practices.
TOPICS OF INTEREST
To push the frontier of network analysis in Learning Analytics, we welcome various types of submissions to the special section including (but not limited to):
- Empirical studies applying network analysis to understanding learning and properties of networks in learning
- Conceptual papers bridging learning processes and network representations
- Methodological papers introducing novel approaches that build of off network analysis, or integrate it with other methods
- User experience studies of tools that support sensemaking of network data or network analysis
- Applications of network analysis for reflection and regulation of learning and knowledge processes in education and the workplace
In addition to an open call for submissions, the special section will solicit a small number of invited submissions and brief commentaries from other research communities (complex networks, network science, learning sciences) to bridge insights from different communities and catalyze further dialogues.
Prospective authors may contact the section editors with queries. Final submissions will take place through JLA’s online submission system at http://learning-analytics.info When submitting a paper, select the section “Special Section: Networks in Learning Analytics". All submissions should follow JLA’s standard manuscript guidelines and template available on the journal website and will undergo double-blind peer review.
- Full manuscripts due: Extended deadline: January 3, 2021
- Completion of first review round: March, 2021
- Revised/final manuscripts due: May, 2021
- Completion of second review round (if needed): August, 2021
- Revised/final manuscripts due: September/October, 2021
- Publication of special issue: December 2021, Issue 8(3)
Bakharia, A., & Dawson, S. (2011). SNAPP. Proceedings of the 1st International Conference on Learning Analytics and Knowledge - LAK ’11, 168. https://doi.org/10.1145/2090116.2090144
Baronchelli, A., Ferrer-i-Cancho, R., Pastor-Satorras, R., Chater, N., & Christiansen, M. H. (2013). Networks in Cognitive Science. Trends in Cognitive Sciences, 17(7), 348–360. https://doi.org/10.1016/j.tics.2013.04.010
Bockholt, M., & Zweig, K. A. (2018). Process-Driven Betweenness Centrality Measures. In R. Alhajj, H. U. Hoppe, T. Hecking, P. Bródka, & P. Kazienko (Eds.), Network Intelligence Meets User Centered Social Media Networks (pp. 17–33). Springer International Publishing. https://doi.org/10.1007/978-3-319-90312-5_2
Brandes, U., Robins, G., McCRANIE, A., & Wasserman, S. (2013). What is network science? Network Science, 1(1), 1–15. https://doi.org/10.1017/nws.2013.2
Contractor, N. (2009). The Emergence of Multidimensional Networks. Journal of Computer-Mediated Communication: JCMC, 14(3), 743–747. https://doi.org/10.1111/j.1083-6101.2009.01465.x
Dado, M., & Bodemer, D. (2017). A review of methodological applications of social network analysis in computer-supported collaborative learning. Educational Research Review, 22, 159–180.
Epskamp, S., Maris, G. K. J., Waldorp, L. J., & Borsboom, D. (2016). Network Psychometrics. ArXiv:1609.02818 [Stat]. http://arxiv.org/abs/1609.02818
Gašević, D., Zouaq, A., & Janzen, R. (2013). “Choose your classmates, your GPA is at stake!” The association of cross-class social ties and academic performance. American Behavioral Scientist, 57(10), 1460–1479.
Göhnert, T., Harrer, A., Hecking, T., & Hoppe, H. U. (2013). A workbench to construct and re-use network analysis workflows: Concept, implementation, and example case. Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining - ASONAM ’13, 1464–1466. https://doi.org/10.1145/2492517.2492596
Haythornthwaite, C., & De Laat, M. (2012). Social network informed design for learning with educational technology. In Informed design of educational technologies in higher education: Enhanced learning and teaching (pp. 352–374). IGI Global.
Hecking, T., Chounta, I.-A., & Hoppe, H. U. (2016). Investigating social and semantic user roles in MOOC discussion forums. Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, 198–207. https://doi.org/10.1145/2883851.2883924
Lund, K., & Suthers, D. (2016). Methodological determinism and researcher agency. Education & Didactique, 10(1), 27–37.
Poquet, O., & Jovanovic, J. (2020). Intergroup and interpersonal forum positioning in shared-thread and post-reply networks. Proceedings of the Tenth International Conference on Learning Analytics & Knowledge, 187–196. https://doi.org/10.1145/3375462.3375533
Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 1–8.
Welles, B. F., & González-Bailón, S. (2020). The oxford handbook of networked communication. Oxford University Press.