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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
Chen, Dandan; Hebert, Michael; Wilson, Joshua – American Educational Research Journal, 2022
We used multivariate generalizability theory to examine the reliability of hand-scoring and automated essay scoring (AES) and to identify how these scoring methods could be used in conjunction to optimize writing assessment. Students (n = 113) included subsamples of struggling writers and non-struggling writers in Grades 3-5 drawn from a larger…
Descriptors: Reliability, Scoring, Essays, Automation
L. Hannah; E. E. Jang; M. Shah; V. Gupta – Language Assessment Quarterly, 2023
Machines have a long-demonstrated ability to find statistical relationships between qualities of texts and surface-level linguistic indicators of writing. More recently, unlocked by artificial intelligence, the potential of using machines to identify content-related writing trait criteria has been uncovered. This development is significant,…
Descriptors: Validity, Automation, Scoring, Writing Assignments
Kerr, Deirdre; Mousavi, Hamid; Iseli, Markus R. – National Center for Research on Evaluation, Standards, and Student Testing (CRESST), 2013
The Common Core assessments emphasize short essay constructed response items over multiple choice items because they are more precise measures of understanding. However, such items are too costly and time consuming to be used in national assessments unless a way is found to score them automatically. Current automatic essay scoring techniques are…
Descriptors: Automation, Scoring, Essay Tests, Natural Language Processing
Crossley, Scott; Liu, Ran; McNamara, Danielle – Grantee Submission, 2017
A number of studies have demonstrated links between linguistic knowledge and performance in math. Studies examining these links in first language speakers of English have traditionally relied on correlational analyses between linguistic knowledge tests and standardized math tests. For second language (L2) speakers, the majority of studies have…
Descriptors: Predictor Variables, Mathematics Achievement, English (Second Language), Natural Language Processing
Jacovina, Matthew E.; McNamara, Danielle S. – Grantee Submission, 2017
In this chapter, we describe several intelligent tutoring systems (ITSs) designed to support student literacy through reading comprehension and writing instruction and practice. Although adaptive instruction can be a powerful tool in the literacy domain, developing these technologies poses significant challenges. For example, evaluating the…
Descriptors: Intelligent Tutoring Systems, Literacy Education, Educational Technology, Technology Uses in Education
Deane, Paul; Sheehan, Kathleen M.; Sabatini, John; Futagi, Yoko; Kostin, Irene – Scientific Studies of Reading, 2006
One source of potential difficulty for struggling readers is the variability of texts across grade levels. This article explores the use of automatic natural language processing techniques to identify dimensions of variation within a corpus of school-appropriate texts. Specifically, we asked: Are there identifiable dimensions of lexical and…
Descriptors: Text Structure, Language Processing, Grade 6, Natural Language Processing