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Chang Cai; Shengxin Hong; Min Ma; Haiyue Feng; Sixuan Du; Minyang Chow; Winnie Li-Lian Teo; Siyuan Liu; Xiuyi Fan – Education and Information Technologies, 2025
Analyzing the teaching and learning environment (TLE) through student feedback is essential for identifying curricular gaps and improving teaching practices. However, traditional feedback analysis methods, particularly for qualitative data, are often time-consuming and prone to human bias. Large Language Models (LLMs) offer a promising solution by…
Descriptors: Educational Environment, Feedback (Response), Measures (Individuals), Natural Language Processing
Linh Huynh; Danielle S. McNamara – Grantee Submission, 2025
We conducted two experiments to assess the alignment between Generative AI (GenAI) text personalization and hypothetical readers' profiles. In Experiment 1, four LLMs (i.e., Claude 3.5 Sonnet; Llama; Gemini Pro 1.5; ChatGPT 4) were prompted to tailor 10 science texts (i.e., biology, chemistry, physics) to accommodate four different profiles…
Descriptors: Natural Language Processing, Profiles, Individual Differences, Semantics
Li, Haiying; Cai, Zhiqiang; Graesser, Arthur – International Educational Data Mining Society, 2016
In this paper, we applied the crowdsourcing approach to develop an automated popularity summary scoring, called wild summaries. In contrast, the golden standard summaries generated by one or more experts are called expert summaries. The innovation of our study is to compute LSA (Latent Semantic Analysis) similarities between target summary and…
Descriptors: Peer Acceptance, Electronic Publishing, Collaborative Writing, Grading

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