Participation in virtual communities of practice (vCoP) can be influenced at the same time by technology acceptance and by community factors. To overcome methodological issues connected with the analysis of these influences, learning analytics were applied. Based on a recent vCoP model, the collaborative dialogue comprising 4040 interventions in 1981 messages created by a vCoP located at a US American online university was automatically analyzed. The text-based asynchronous online discussions were scored using a cohesion-based participation and collaboration analysis. Additionally, a sample of N = 133 vCoP participants responded a technology acceptance survey. Thus, a combined research model including the vCoP model and an established technology acceptance model was verified. The results confirmed the vCoP model entirely, and the acceptance model only partially. As consequence for educational research, the CoP model was confirmed and extended to vCoP settings, while the acceptance model appears to need reconsideration. For academic practice, the study initiates the development of assessment tools fostering knowledge sharing through dialogue in vCoP. Also, it suggests how virtual classrooms can be extended to open spaces where value creation takes place through social learning. Learning analytics proved thus successful, provides information that impacts both theory and practice of technology-enhanced learning.

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Textual complexity is widely used to assess the difficulty of reading materials and writing quality in student essays. At a lexical level, word complexity can represent a building block for creating a comprehensive model of lexical networks that adequately estimates learners’ understanding. In order to best capture how lexical associations are created between related concepts, we propose automated indices of word complexity based on Age of Exposure (AoE). AOE indices computationally model the lexical learning process as a function of a learner's experience with language. This study describes a proof of concept based on the on a large-scale learning corpus (i.e., TASA). The results indicate that AoE indices yield strong associations with human ratings of age of acquisition, word frequency, entropy, and human lexical response latencies providing evidence of convergent validity.

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