Discovering academics' key learning connections

Purpose: – The aim of this exploratory study is to investigate the role of personal networks in supporting academics' professional learning. In particular, the paper examines the composition of academics' networks and the implications of network tendencies for academics' learning about teaching. Design/methodology/approach: – The study adopts a mixed-methods 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: – 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 learning. Research limitations/implications: – While the study makes a valuable contribution to the literature, the generalisability of these findings is limited, because the sample is restricted to 37 academics. Participants' characteristics and networking behaviours may not be fully representative of academics in a wider range of contexts and settings. Another limitation is that the evaluation of people's learning was limited to self-reported measures. Future research should measure a broader range of evidence related to academics' professional networks. Practical implications: – This study extends the discussion of professional learning in academia in a novel way, by taking a social network perspective. The approach employed attempts to enrich the limited understanding of academics' networks, by unpacking the ways in which academics' personal networks support their learning. Originality/value: – The originality of this work lies in its intent to uncover relationships that condition professional learning and enhancement of teaching practice. Reflection on personal networks can potentially enable individuals to determine the effectiveness of their networks and the significance of their network connections