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Wan, Qian; Crossley, Scott; Allen, Laura; McNamara, Danielle – Grantee Submission, 2020
In this paper, we extracted content-based and structure-based features of text to predict human annotations for claims and nonclaims in argumentative essays. We compared Logistic Regression, Bernoulli Naive Bayes, Gaussian Naive Bayes, Linear Support Vector Classification, Random Forest, and Neural Networks to train classification models. Random…
Descriptors: Persuasive Discourse, Essays, Writing Evaluation, Natural Language Processing
Mark L. Davison; David J. Weiss; Ozge Ersan; Joseph N. DeWeese; Gina Biancarosa; Patrick C. Kennedy – Grantee Submission, 2021
MOCCA is an online assessment of inferential reading comprehension for students in 3rd through 6th grades. It can be used to identify good readers and, for struggling readers, identify those who overly rely on either a Paraphrasing process or an Elaborating process when their comprehension is incorrect. Here a propensity to over-rely on…
Descriptors: Reading Tests, Computer Assisted Testing, Reading Comprehension, Elementary School Students
Kent, Shawn C.; Wanzek, Jeanne; Yun, Joonmo – Grantee Submission, 2019
This study examined the predictive validity and classification accuracy of individual and group-administered screening measures relative to student performance on a year-end state reading assessment in two states. A sample of 321 students were assessed in the areas of word-level and text fluency, as well as reading comprehension in the fall of…
Descriptors: Screening Tests, Grade 4, Elementary School Students, At Risk Students