Exploring networked learning in the workplace using social network learning analytics

Purpose Workplace interaction and collaboration can be enhanced by networked learning. The study intends to explore networked learning in the workplace (knowledge sharing and connection buildings) and gain insights into how workers develop connections through learning analytics social network analysis (SNA). Design/methodology/approach SNA was employed to explore how learning connections were established amongst healthcare workers in a large hospital in Singapore. We examined both the total network interactions (density, diameter, average shortest path length) and the levels of interactions between individuals (degree, betweenness, closeness centralities). A total of 99 responses were included in the final data analysis, and Python packages such as NetworkX were used to perform SNA. Findings The network as a whole is sparse, as indicated by the low-density score (0.4%). The findings of the study reveal that the bigger sub-networks had more than one worker who interacted with more than one co-worker and these tend to have more edges in them interlinking workers from different departments. We also found that workers from the departments with the larger populations in the sub-networks were more likely to have the highest degree, betweenness and closeness centrality values. This indicates that the larger sub-networks hold more value in terms of understanding how workers with higher centrality values are nurtured. Originality/value This paper sheds light on the learning process that occurs when workers engage in networked learning and provides empirical findings with Singapore as the context of the study.