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Mathias Benedek; Roger E. Beaty – Journal of Creative Behavior, 2025
The PISA assessment 2022 of creative thinking was a moonshot effort that introduced significant advancements over existing creativity tests, including a broad range of domains (written, visual, social, and scientific), implementation in many languages, and sophisticated scoring methods. PISA 2022 demonstrated the general feasibility of assessing…
Descriptors: Creative Thinking, Creativity, Creativity Tests, Scoring
Sami Baral; Eamon Worden; Wen-Chiang Lim; Zhuang Luo; Christopher Santorelli; Ashish Gurung; Neil Heffernan – Grantee Submission, 2024
The effectiveness of feedback in enhancing learning outcomes is well documented within Educational Data Mining (EDM). Various prior research have explored methodologies to enhance the effectiveness of feedback to students in various ways. Recent developments in Large Language Models (LLMs) have extended their utility in enhancing automated…
Descriptors: Automation, Scoring, Computer Assisted Testing, Natural Language Processing
Cathy Cavanaugh; Bryn Humphrey; Paige Pullen – International Journal on E-Learning, 2024
To address needs in one US state to provide a professional development micro-credential for tens of thousands of educators, we automated an assignment scoring workflow in an online course by developing and refining an AI model to scan submitted assignments and score them against a rubric. This article outlines the AI model development process and…
Descriptors: Artificial Intelligence, Automation, Scoring, Microcredentials
Eran Hadas; Arnon Hershkovitz – Journal of Learning Analytics, 2025
Creativity is an imperative skill for today's learners, one that has important contributions to issues of inclusion and equity in education. Therefore, assessing creativity is of major importance in educational contexts. However, scoring creativity based on traditional tools suffers from subjectivity and is heavily time- and labour-consuming. This…
Descriptors: Creativity, Evaluation Methods, Computer Assisted Testing, Artificial Intelligence
Christopher D. Wilson; Kevin C. Haudek; Jonathan F. Osborne; Zoë E. Buck Bracey; Tina Cheuk; Brian M. Donovan; Molly A. M. Stuhlsatz; Marisol M. Santiago; Xiaoming Zhai – Journal of Research in Science Teaching, 2024
Argumentation is fundamental to science education, both as a prominent feature of scientific reasoning and as an effective mode of learning--a perspective reflected in contemporary frameworks and standards. The successful implementation of argumentation in school science, however, requires a paradigm shift in science assessment from the…
Descriptors: Middle School Students, Competence, Science Process Skills, Persuasive Discourse
Parker, Mark A. J.; Hedgeland, Holly; Jordan, Sally E.; Braithwaite, Nicholas St. J. – European Journal of Science and Mathematics Education, 2023
The study covers the development and testing of the alternative mechanics survey (AMS), a modified force concept inventory (FCI), which used automatically marked free-response questions. Data were collected over a period of three academic years from 611 participants who were taking physics classes at high school and university level. A total of…
Descriptors: Test Construction, Scientific Concepts, Physics, Test Reliability
Chan, Kinnie Kin Yee; Bond, Trevor; Yan, Zi – Language Testing, 2023
We investigated the relationship between the scores assigned by an Automated Essay Scoring (AES) system, the Intelligent Essay Assessor (IEA), and grades allocated by trained, professional human raters to English essay writing by instigating two procedures novel to written-language assessment: the logistic transformation of AES raw scores into…
Descriptors: Computer Assisted Testing, Essays, Scoring, Scores
Congning Ni; Bhashithe Abeysinghe; Juanita Hicks – International Electronic Journal of Elementary Education, 2025
The National Assessment of Educational Progress (NAEP), often referred to as The Nation's Report Card, offers a window into the state of U.S. K-12 education system. Since 2017, NAEP has transitioned to digital assessments, opening new research opportunities that were previously impossible. Process data tracks students' interactions with the…
Descriptors: Reaction Time, Multiple Choice Tests, Behavior Change, National Competency Tests
Latifi, Syed; Gierl, Mark – Language Testing, 2021
An automated essay scoring (AES) program is a software system that uses techniques from corpus and computational linguistics and machine learning to grade essays. In this study, we aimed to describe and evaluate particular language features of Coh-Metrix for a novel AES program that would score junior and senior high school students' essays from…
Descriptors: Writing Evaluation, Computer Assisted Testing, Scoring, Essays
Yi 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
Yerushalmy, Michal; Olsher, Shai – ZDM: The International Journal on Mathematics Education, 2020
We argue that examples can do more than serve the purpose of illustrating the truth of an existential statement or disconfirming the truth of a universal statement. Our argument is relevant to the use of technology in classroom assessment. A central challenge of computer-assisted assessment is to develop ways of collecting rich and complex data…
Descriptors: Computer Assisted Testing, Student Evaluation, Problem Solving, Thinking Skills
Myers, Matthew C.; Wilson, Joshua – International Journal of Artificial Intelligence in Education, 2023
This study evaluated the construct validity of six scoring traits of an automated writing evaluation (AWE) system called "MI Write." Persuasive essays (N = 100) written by students in grades 7 and 8 were randomized at the sentence-level using a script written with Python's NLTK module. Each persuasive essay was randomized 30 times (n =…
Descriptors: Construct Validity, Automation, Writing Evaluation, Algorithms
New York State Education Department, 2023
The instructions in this manual explain the responsibilities of school administrators for the New York State Testing Program (NYSTP) Grades 3-8 English Language Arts and Mathematics Tests. School administrators must be thoroughly familiar with the contents of the manual, and the policies and procedures must be followed as written so that testing…
Descriptors: Testing Programs, Mathematics Tests, Test Format, Computer Assisted Testing
Potter, Andrew; Wilson, Joshua – Educational Technology Research and Development, 2021
Automated Writing Evaluation (AWE) provides automatic writing feedback and scoring to support student writing and revising. The purpose of the present study was to analyze a statewide implementation of an AWE software (n = 114,582) in grades 4-11. The goals of the study were to evaluate: (1) to what extent AWE features were used; (2) if equity and…
Descriptors: Computer Assisted Testing, Writing Evaluation, Feedback (Response), Scoring
New York State Education Department, 2024
The instructions in this manual explain the responsibilities of school administrators for the New York State Testing Program (NYSTP) Grades 3-8 English Language Arts, Mathematics, and Grades 5 & 8 Science Tests. School administrators must be thoroughly familiar with the contents of the manual, and the policies and procedures must be followed…
Descriptors: Testing Programs, Language Arts, Mathematics Tests, Science Tests