@article {5811, title = {The Impact of Hierarchical Positions on the Type of Communication within Online Communities of Learning}, journal = {Computers in Human Behavior}, volume = {58}, year = {2016}, pages = {158{\textendash}170}, abstract = {Communities of Learning (CoL) have been suggested to facilitate the learning process among participants of online trainings. Yet, previous studies often detached participants from the social context in which learning took place. The present study addresses this shortcoming by providing empirical evidence from 25 CoL of a global organization, where 249 staff members from different hierarchical positions engaged into collaborative learning via asynchronous discussion forums. We conduct a longitudinal study on the type of communication within these CoL, as well as participants{\textquoteright} network positions, in order to investigate the research question: What is the impact of individual{\textquoteright}s hierarchical positions on the type of communication within CoL? Our results indicate that the higher participants{\textquoteright} hierarchical position, the higher their amount of social and cognitive communication, which in turn was also positively related to their network position within CoL. We also identified a sub-group of Stars that outperformed their colleagues and who were at the center of CoL, irrespective of their hierarchical positions. Consequently, we propose design and facilitation strategies to practitioners and organizers of future CoL that can foster the learning processes and outcomes of all participants. Additionally, we consider future research avenues that could be explored further.}, keywords = {Community of Learning, Content Analysis, Hierarchical Positions, Social Network Analysis, type of communication}, doi = {10.1016/j.chb.2015.12.065}, author = {Martin Rehm and Regina H. Mulder and Wim Gijselaers and Mien Segers} } @article {4089, title = {The Impact of Hierarchical Positions on Communities of Learning}, journal = {International Journal of Computer-Supported Collaborative Learning}, volume = {10}, year = {2015}, pages = {117-138}, keywords = {Community of Learning, Content Analysis, Hierarchical Positions}, url = {http://link.springer.com/article/10.1007/s11412-014-9205-8}, author = {Martin Rehm and Wim Gijselaers and Mien Segers} } @article {3811, title = {De invloed van Hi{\"e}rarchische Posities op Communities of Learning}, journal = {Tijdschrift voor Hoger Onderwijs}, year = {2014}, abstract = {

Communities of Learning (CoL) can facilitate the learning process among participants of online learning modules. Yet, previous studies often neglected hierarchical positions as a hurdle to collaborative learning processes. The present article addresses this shortcoming by providing empirical evidence from 25 CoL, organized as part of postacademic education, for 249 staff members of a global organization from different hierarchical positions. They engaged into collaborative learning via discussion forums. We provide the results from four different studies, namely (1) a descriptive case study, (2) an exploratory study investigating the overall impact of hierarchical positions on CoL, (3) a social network study on the influence of participants\’ hierarchical positions on their network behaviour, and (4) a content analysis determining the cognitive level of communication. Our results clearly indicate that the higher participants\’ hierarchical position, the higher their amount of social and cognitive communication, which in turn was also positively related to their centrality within CoL. However, we also identified a sub-group of \“Stars\”, who outperformed their colleagues and were at the centre of CoL, irrespective of their hierarchical positions. Based on these findings, we propose a number of strategies that can foster the learning processes and learning outcomes of all participants.

}, keywords = {Collaborative Learning, Community of Learning, Hierarchical Positions}, author = {Martin Rehm and Dani{\"e}lle Verstegen and Simon Beausaert and Wim Gijselaers and Mien Segers} } @article {3723, title = {Effects of Hierarchical Levels on Social Network Structures within Communities of Learning}, journal = {Frontline Learning Research}, volume = {Learning through Networks}, year = {2014}, abstract = {Facilitating an interpersonal knowledge transfer among employees constitutes a key building block in setting up organizational training initiatives. With practitioners and researchers looking for innovative training methods, online Communities of Learning (CoL) have been promoted as a promising methodology to foster this kind of transfer. However, past research has only provided limited data from actual organizations and largely neglected characteristics that constitute a major obstacle to such collaborative processes, namely participants{\textquoteright} hierarchical levels. The current study addresses these shortcomings by providing empirical evidence from 25 CoL of an online training program, provided for 249 staff members of a global organization. Using social network analysis, we are able to show significant differences in participants{\textquoteright} network behaviour and position based on their hierarchical rank. This translates into higher in- and out-degree network ties, as well as centrality scores among participants from higher up the hierarchical ladder. Finally, based on a longitudinal analysis of all indicated network measures, our results indicate that the main trend develops predominately during the first half of the training program. By incorporating these insights into the implementation of future CoL, it is not only possible to anticipate participants{\textquoteright} behaviour. Our findings also allow to draw conclusions about how collaborative activities within CoL should be designed and facilitated, in order to provide employees with a valuable learning experience.}, keywords = {Centrality, Community of Learning, Hierarchical Positions, Longitudinal Analysis, Social Network Analysis}, url = {http://journals.sfu.ca/flr/index.php/journal/article/view/85}, author = {Martin Rehm and Wim Gijselaers and Mien Segers} }