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Hunkoog Jho; Minsu Ha – Journal of Baltic Science Education, 2024
This study aimed at examining the performance of generative artificial intelligence to extract argumentation elements from text. Thus, the researchers developed a web-based framework to provide automated assessment and feedback relying on a large language model, ChatGPT. The results produced by ChatGPT were compared to human experts across…
Descriptors: Feedback (Response), Artificial Intelligence, Persuasive Discourse, Models
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Ibrahim Aydin; Vahide Bulut – Journal of Educational Technology and Online Learning, 2024
The purpose of this study is to provide students with a 21st century training that can be easily applied to industrial automation training, both online and in person, and that goes beyond traditional learning methods. It involves creating and programming a training package that is compatible with the Ministry of National Education (MEB)…
Descriptors: Active Learning, Student Projects, Industrial Education, Manufacturing Industry
Danielle S. McNamara; Scott A. Crossley; Rod D. Roscoe; Laura K. Allen; Jianmin Dai – Grantee Submission, 2015
This study evaluates the use of a hierarchical classification approach to automated assessment of essays. Automated essay scoring (AES) generally relies onmachine learning techniques that compute essay scores using a set of text variables. Unlike previous studies that rely on regression models, this study computes essay scores using a hierarchical…
Descriptors: Automation, Scoring, Essays, Persuasive Discourse
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Vitale, Jonathan M.; Lai, Kevin; Linn, Marcia C. – Journal of Research in Science Teaching, 2015
We present a new system for automated scoring of graph construction items that address complex science concepts, feature qualitative prompts, and support a range of possible solutions. This system utilizes analysis of spatial features (e.g., slope of a line) to evaluate potential student ideas represented within graphs. Student ideas are then…
Descriptors: Scoring, Graphs, Scientific Concepts, Prompting
Partnership for Assessment of Readiness for College and Careers, 2018
The purpose of this technical report is to describe the third operational administration of the Partnership for Assessment of Readiness for College and Careers (PARCC) assessments in the 2016-2017 academic year. PARCC is a state-led consortium creating next-generation assessments that, compared to traditional K-12 assessments, more accurately…
Descriptors: College Readiness, Career Readiness, Common Core State Standards, Language Arts
Partnership for Assessment of Readiness for College and Careers, 2017
The Partnership for Assessment of Readiness for College and Careers (PARCC) is a state-led consortium designed to create next-generation assessments that, compared to traditional K-12 assessments, more accurately measure student progress toward college and career readiness. The PARCC assessments are aligned to the Common Core State Standards…
Descriptors: College Readiness, Career Readiness, Common Core State Standards, Language Arts
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Tsai, Min-hsiu – Action in Teacher Education, 2012
This study investigates the consistency between human raters and an automated essay scoring system in grading high school students' English compositions. A total of 923 essays from 23 classes of 12 senior high schools in Taiwan (Republic of China) were obtained and scored manually and electronically. The results show that the consistency between…
Descriptors: Foreign Countries, High School Students, Writing (Composition), Essays
Gollnitz, Deborah-Lee – ProQuest LLC, 2010
Writing skills are considered essential to lifelong success, yet experts cannot agree on one model or set of traits that distinguishes good writing from poor writing. Instructional strategies in developing student writing at the high school level need to include a means by which students receive immediate, specific feedback that acts as a scaffold…
Descriptors: Writing Improvement, Grade 10, Grade 11, Scoring
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Attali, Yigal; Burstein, Jill – ETS Research Report Series, 2005
The e-raterĀ® 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