Semantic Networks Extracted from Students' Think-Aloud Data are Correlated with Students' Learning Performance

When students reflect on their learning from a textbook via think-aloud processes, network representations can be used to capture the concepts and relations from these data. What can we learn from the resulting 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 represents relevant (to educational science), textbook-based information as a semantic network. Among the tested models, SPN4RE and LUKE achieved the best performance in extracting concepts and relations from students' verbal data. Network representations of students' verbalizations varied in structure, reflecting different learning processes. Correlating the students' semantic networks with learning outcomes revealed that denser and more interconnected semantic networks were associated with more elaborated knowledge acquisition. Structural features such as the number of edges and surface overlap with textbook networks significantly correlated with students' posttest performance.