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When students reflect on their learning from a textbook via think aloud, network representations can be used to capture their concepts and relations. What can we learn from these network representations about students’ learning processes, knowledge acquisition, and learning outcomes? This study brings methods from entity and relation extraction using classic and LLM-based methods to the application domain of educational psychology. We built a ground-truth baseline of relational data that represent relevant (to educational science), textbook-based information as a semantic network. We identified SPN4RE and LUKE as the most accurate method to extracting semantic networks capturing the same types of information from transcriptions of verbal student data. Correlating the students’ semantic networks with learning outcomes showed that students’ verbalizations varied in structure, reflecting different learning processes. Denser and more interconnected semantic networks indicated more elaborated knowledge acquisition. Structural features such as the number of edges and surface overlap with textbook networks significantly correlated with students’ posttest performance.