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Zachary Himmelsbach; Heather C. Hill; Jing Liu; Dorottya Demszky – Annenberg Institute for School Reform at Brown University, 2023
This study provides the first large-scale quantitative exploration of mathematical language use in U.S. classrooms. Our approach employs natural language processing techniques to describe variation in the use of mathematical language in 1,657 fourth and fifth grade lessons by teachers and students in 317 classrooms in four districts over three…
Descriptors: Mathematics Education, Mathematics Instruction, Teaching Methods, Elementary School Mathematics
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Zhang, H.; Magooda, A.; Litman, D.; Correnti, R.; Wang, E.; Matsumura, L. C.; Howe, E.; Quintana, R. – Grantee Submission, 2019
Writing a good essay typically involves students revising an initial paper draft after receiving feedback. We present eRevise, a web-based writing and revising environment that uses natural language processing features generated for rubric-based essay scoring to trigger formative feedback messages regarding students' use of evidence in…
Descriptors: Formative Evaluation, Essays, Writing (Composition), Revision (Written Composition)
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Liu, Ming; Rus, Vasile; Liu, Li – IEEE Transactions on Learning Technologies, 2018
Automatic question generation can help teachers to save the time necessary for constructing examination papers. Several approaches were proposed to automatically generate multiple-choice questions for vocabulary assessment or grammar exercises. However, most of these studies focused on generating questions in English with a certain similarity…
Descriptors: Multiple Choice Tests, Regression (Statistics), Test Items, Natural Language Processing