The Open UniversitySkip to content

Discovering academics' key learning connections: An ego-centric network approach to analysing learning about teaching

Pataraia, Nino; Margaryan, Anoush; Falconer, Isobel; Littlejohn, Allison and Falconer, Jennifer (2014). Discovering academics' key learning connections: An ego-centric network approach to analysing learning about teaching. Journal of Workplace Learning, 26(1) pp. 56–72.

Full text available as:
PDF (Accepted Manuscript) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (1MB) | Preview
DOI (Digital Object Identifier) Link:
Google Scholar: Look up in Google Scholar


The aim of this exploratory study is to investigate the role of personal networks in supporting academics’ professional learning about teaching. As part of a wider project, the paper focuses on the composition of academics’ networks and possible implications of network tendencies for academics’ learning about teaching. The study adopts a mixed-method approach. Firstly, the composition of academics’ networks is examined using Social Network Analysis. Secondly, the role of these networks in academics’ learning about teaching is analysed through semi-structured interviews. Findings reveal the prevalence of localised and strong-tie connections, which could inhibit opportunities for effective learning and spread of innovations in teaching. The study highlights the need to promote connectivity within and across institutions, creating favourable conditions for effective professional development.

Item Type: Journal Item
Copyright Holders: 2014 Emerald Group Publishing Limited
ISSN: 1366-5626
Keywords: personal learning networks; social network analysis; egocentric network analysis; teaching; Higher Education; workplace learning
Academic Unit/School: Institute of Educational Technology (IET)
Item ID: 51292
Depositing User: Allison Littlejohn
Date Deposited: 25 Oct 2017 09:02
Last Modified: 14 Dec 2019 06:55
Share this page:


Altmetrics from Altmetric

Citations from Dimensions

Download history for this item

These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.

Actions (login may be required)

Policies | Disclaimer

© The Open University   contact the OU