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Direct linkYi Gui – ProQuest LLC, 2024
This study explores using transfer learning in machine learning for natural language processing (NLP) to create generic automated essay scoring (AES) models, providing instant online scoring for statewide writing assessments in K-12 education. The goal is to develop an instant online scorer that is generalizable to any prompt, addressing the…
Descriptors: Writing Tests, Natural Language Processing, Writing Evaluation, Scoring
Download full textSteedle, Jeffrey; Quesen, Sarah; Boyd, Aimee – Partnership for Assessment of Readiness for College and Careers, 2017
On the Partnership for Assessment of Readiness for College and Careers (PARCC) assessments, the attainment of performance level 4 is intended to indicate college readiness or being "on track" to college and career readiness. Students who achieve Level 4 should have a 0.75 probability of attaining at least a C in entry-level,…
Descriptors: College Readiness, Career Readiness, Test Validity, Longitudinal Studies
Allen, Jeff – ACT, Inc., 2014
This report focuses on the initial development of the predicted score paths for ACT Aspire reporting. The paths provide predicted score ranges for the next two years--as well as predicted ACT score ranges for tests administered at grades 9 and 10. Longitudinal ACT Aspire data for students tested in spring 2013 and spring 2014, as well as…
Descriptors: College Readiness, Career Readiness, Achievement Tests, Scores
Peer reviewedLeong, Che Kan; Ho, Man Koon; Chang, Jianfang; Hau, Kit Tai – Language Testing, 2013
The present study examined pedagogic components of Chinese reading literacy in a representative sample of 1164 Grades 7, 9 and 11 Chinese students (mean age of 15 years) from 11 secondary schools in Hong Kong with each student tested for about 2.5 hours. Multiple group confirmatory factor analyses showed that across the three grade levels, the…
Descriptors: Foreign Countries, Secondary School Students, Grade 7, Grade 9
Attali, Yigal; Burstein, Jill – ETS Research Report Series, 2005
The e-rater® system has been used by ETS for automated essay scoring since 1999. This paper describes a new version of e-rater (v.2.0) that differs from the previous one (v.1.3) with regard to the feature set and model building approach. The paper describes the new version, compares the new and previous versions in terms of performance, and…
Descriptors: Essay Tests, Automation, Scoring, Comparative Analysis
