This article discusses the relevance of large-scale mass collaboration for computer-supported collaborative learning (CSCL) research, adhering to a theoretical perspective that views collective knowledge both as substance and as participatory activity. In an empirical study using the German Wikipedia as a data source, we explored collective knowledge as manifested in the structure of artifacts that were created through the collaborative activity of authors with different levels of contribution experience. Wikipedia's interconnected articles were considered at the macro level as a network and analyzed using a network analysis approach. The focus of this investigation was the relation between the authors' experience and their contribution to two types of articles: "central pivotal articles" within the artifact network of a single knowledge domain and "boundary-crossing pivotal articles" within the artifact network of two adjacent knowledge domains. Both types of pivotal articles were identified by measuring the network position of artifacts based on network analysis indices of topological centrality. The results showed that authors with specialized contribution experience in one domain predominantly contributed to central pivotal articles within that domain. Authors with generalized contribution experience in two domains predominantly contributed to boundary-crossing pivotal articles between the knowledge domains. Moreover, article experience (i.e., the number of articles in both domains an author had contributed to) was positively related to the contribution to both types of pivotal articles, regardless of whether an author had specialized or generalized domain experience. We discuss the implications of our findings for future studies in the field of CSCL.