Understanding the networked student: how personal learning networks are used for learning

Abstract

Networks, both in theory and practice, are increasingly central to learning, work and academic practice, and have become inseparable from most of the activities which constitute modern life. As a result, today’s Higher Education (HE) student can be viewed as having a interdependent, sociotechnical relationship with a range of people, devices, services and information resources, online and off, which they use for a range of purposes. In short, they each have their own personal learning network (PLN).
Despite growing recognition of this networked paradigm, there remains a lack of understanding of what PLNs look like and how they are used, hence they remain under-represented in mainstream HE pedagogy and learning design. This could result in a disconnect between a student’s personal learning behaviours and the formal learning experience they receive from their HE institution – with potentially negative consequences for learning, engagement, student satisfaction and TEF ratings.
Therefore, this research has designed, tested and deployed an innovative framework for the analysis of PLNs which captures individual PLNs, enables them to be aggregated and filtered by a range of external factors, visualised online in real-time as a series of network maps, and statistically analysed to assess the impact of a range of external shaping factors. As interdisciplinary research, concepts from Web Science, Education and the Social Sciences, as well as existing networked learning research, system modelling and social network analysis, underpin the PLN analysis framework and led to the pre-definition and generalisation of the nodes within the network. This approach meant that individual networks could be usefully aggregated, compared and analysed, thereby addressing a gap in existing networked learning research by reconciling micro (individual learning networks) and the macro (community networks) level networks for analytical purposes.
The empirical data was captured from responses to an online quantitative survey which captured the nodes in the network and the type and frequency of interactions across the network. In a novel approach, this survey was embedded within a Futurelearn MOOC, extending MOOC functionality from a learning tool to a research tool. This approach to data collection provided 842 responses over eighteen months, with participants from 92 different countries, 20 different ethnicities, and ranging across the full age and demographic spectrum - making this one of the largest sample sizes for research specifically aimed at mapping learning networks to date.
This research can not provide an answer to why PLNs are used in the way they are, but does provide detailed and nuanced descriptions of PLNs and, through a range of ANOVA statistical significance testing, identifies the significance of the impact of six external shaping factors on the size, usage and interaction preferences of the networks of diverse groups. The findings suggest that there is more in common between the learning networks of apparently diverse groups than the literature might suggest. Network size is significantly affected by three of the six shaping factors; network usage by four of the six (although the effect is limited as it is not universal for all PLN aspects (mode, purpose, endpoint) and subsets); however, interaction preference is much less affected and displays a considerable homogeneity across diverse sample groups and subsets.
In network terms, the findings indicate potentially important changes between childhood and University at which point the network undergoes growth and development with the how, why and with whom/what learners interact all undergoing transitions. This means that HEI’s are critically positioned to guide and facilitate this network growth and development in educationally productive ways.
Taken together, the findings proved informative in further developing a PLN-centred networked learning pedagogy; both confirmed and challenged aspects of the existing literature; contributed an innovative research methodology; and helped to address a gap in the networked learning field by successfully reconciling micro-level networks (egonets) and macro-level networks (wholenets) by enabling meaningful comparisons across and between diverse samples.
In the future, and in the spirit of open, networked education and research, it is hoped that the framework, data collection and mapping approach can be adapted by fellow researchers for use in other contexts and domains where the focus is on understanding networks at both an individual and community level.

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Identifiers

ORCID for Nic Fair: orcid.org/0000-0003-1566-4689

ORCID for Christian Bokhove: orcid.org/0000-0002-4860-8723

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