In the wake of the digital transformation of foreign language education, there would be inevitable transformation and reconstruction in educational evaluation modality. While the embedding of educational data mining technologies and Learning Analytics have already become the emblem of digital evaluation, very few relevant studies provide workable data processing and analysis models for higher foreign language education. In this context, this research aims to propose a software-aided data processing, analysis and visualization model for the empirical data sets acquired from actual blended teaching practice. This research was conducted in an application-oriented university with a small-scale sample of 20 English major juniors. Theoretically, the research design is framed by learning analytics; Methodologically, this research is designed as a mixed-method and adopts the social network analysis paradigm in data analysis. The contribution of the research is a practical empirical approach to digital evaluation and the development of a whole-network-based mapping model which produces the cognitive ability and performative map of learners for the evaluation of learner’s language and socio-cognitive development. The research findings suggest a whole network analysis paradigm can be integrated with digital evaluation in areas like multidimensional data synthesis, analysis and visualization, and the software-aided whole network analysis can be a surrogate measure for digital evaluation.