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.

The Ukraine-Russia conflict has brought sizable detrimental impact to the global energy, food, finance, and manufacturing industries, as well to many affected people. In this paper, we use Twitter (now X) to automatically identify who needs what from text data and how the types of needs that we categorized and standardize evolved throughout this conflict. Our findings suggest that the Ukraine expresses a need for weapons, Russia for land, Europe for gas, and America for leadership. The majority of needs expressed on Twitter during this conflict are related to the categories transportation, military, health & medical, financial and money, energy, and essential items (food, water, shelter, non-food items). Stated needs changed as the conflict escalated or fell into stalemate. Needs also varied depending on the tweet's location, with tweets from Ukraine's neighboring countries being related to food and medicine, while tweets from non-neighboring countries stated needs for clothing and tents. Tweets written in Ukrainian and Russian shared similar need terms, such as medicines and kits, compared to English tweets, which expressed needs such as ammunition and humanitarian aid. Our comparison of needs across four different disaster events, namely this conflict, an earthquake, a major hurricane, and the COVID-19 pandemic, showed how needs differ depending on the nature of the crisis and how domain-adjustment of needs categories is necessary. We contribute to the crisis informatics literature by (1) validating a methodology for using tweets to study the demand and supply of things that different stakeholders need during crisis events and (2) testing, comparing, and improving the fit of widely used need classification schemas for studying crisis from different domains.