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Bogdan Nicula; Mihai Dascalu; Tracy Arner; Renu Balyan; Danielle S. McNamara – Grantee Submission, 2023
Text comprehension is an essential skill in today's information-rich world, and self-explanation practice helps students improve their understanding of complex texts. This study was centered on leveraging open-source Large Language Models (LLMs), specifically FLAN-T5, to automatically assess the comprehension strategies employed by readers while…
Descriptors: Reading Comprehension, Language Processing, Models, STEM Education
Joseph P. Magliano; Lauren Flynn; Daniel P. Feller; Kathryn S. McCarthy; Danielle S. McNamara; Laura Allen – Grantee Submission, 2022
The goal of this study was to assess the relationships between computational approaches to analyzing constructed responses made during reading and individual differences in the foundational skills of reading in college readers. We also explored if these relationships were consistent across texts and samples collected at different institutions and…
Descriptors: Semantics, Computational Linguistics, Individual Differences, Reading Materials