This experimental investigation seeks to corroborate a knowledge structure sorting task approach as a measure to more fully account for prior knowledge when reading. A latent semantic analysis (LSA) network derived from thousands of texts typically read by first year college students was used to create a prototypical referent network model of the global collective knowledge structure of the key terms in the text. Bilingual Chinese-English participants (n = 205) were randomly assigned to four treatments to sort terms in both languages, then to read an English expository text of an unfamiliar topic, then sort in both languages again, and lastly complete a comprehension posttest. All pre- and post- sorting tasks data were converted to Pathfinder networks as measures of knowledge structure. Multiword clusters in the LSA network were present in the initial pre-reading group-average sorting networks of both languages, but especially in Chinese (their L1), and these clusters tended to persist after reading. Reading had only a small influence on the post-reading group-average networks. Sorting in Chinese had a stronger influence downstream than did sorting in English (L1 > L2 influence). For researchers, these innovative approaches to establish local and global collective knowledge networks show promise as complementary measures to explain learning in terms of knowledge structure alignment and transitions, and pragmatically, sorting tasks are relatively easy to implement and interpret in real classrooms as formative diagnostic measures of conceptual understanding.