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Mounia Machkour; Latifa Lamalif; Sophia Faris; Khalifa Mansouri – Educational Process: International Journal, 2025
Background/purpose: This study addresses the problem of demotivation generated by traditional assessment methods, which are often standardized, unengaging, and ill-suited to individual differences. In an increasingly digitized educational context, the primary objective is to assess the ability of an adaptive assessment system, developed on the…
Descriptors: Foreign Countries, High School Seniors, Student Evaluation, Student Motivation
Lae Lae Shwe; Sureena Matayong; Suntorn Witosurapot – Education and Information Technologies, 2024
Multiple Choice Questions (MCQs) are an important evaluation technique for both examinations and learning activities. However, the manual creation of questions is time-consuming and challenging for teachers. Hence, there is a notable demand for an Automatic Question Generation (AQG) system. Several systems have been created for this aim, but the…
Descriptors: Difficulty Level, Computer Assisted Testing, Adaptive Testing, Multiple Choice Tests
Yang Zhen; Xiaoyan Zhu – Educational and Psychological Measurement, 2024
The pervasive issue of cheating in educational tests has emerged as a paramount concern within the realm of education, prompting scholars to explore diverse methodologies for identifying potential transgressors. While machine learning models have been extensively investigated for this purpose, the untapped potential of TabNet, an intricate deep…
Descriptors: Artificial Intelligence, Models, Cheating, Identification
Running out of Time: Leveraging Process Data to Identify Students Who May Benefit from Extended Time
Burhan Ogut; Ruhan Circi; Huade Huo; Juanita Hicks; Michelle Yin – International Electronic Journal of Elementary Education, 2025
This study explored the effectiveness of extended time (ET) accommodations in the 2017 NAEP Grade 8 Mathematics assessment to enhance educational equity. Analyzing NAEP process data through an XGBoost model, we examined if early interactions with assessment items could predict students' likelihood of requiring ET by identifying those who received…
Descriptors: Identification, Testing Accommodations, National Competency Tests, Equal Education
James Pengelley; Peter R. Whipp; Anabela Malpique – Technology, Pedagogy and Education, 2025
The rising use of technology in classrooms has also brought with it a concomitant wave of computer-based assessments. The argument for computer-based testing is often framed in terms of efficiency and data management: computer-based tests facilitate more efficient processing of test data and the rate at which feedback can be leveraged for student…
Descriptors: Cognitive Processes, Paper and Pencil Tests, Computer Assisted Testing, Student Evaluation
Hongwen Guo; Matthew S. Johnson; Luis Saldivia; Michelle Worthington; Kadriye Ercikan – ETS Research Institute, 2025
ETS scientists developed a human-centered AI (HAI) framework that combines data on how students interact with assessments--such as task navigation and time spent--with their performance, providing deeper insights into student performance in large-scale assessments.
Descriptors: Artificial Intelligence, Student Evaluation, Evaluation Methods, Measurement
Ben Erwin; Shayna Levitan – Education Commission of the States, 2024
Summative assessments measure students' mastery of grade-level academic standards and skills in specific content areas after learning. State summative assessment systems provide students and families, school and district leaders, and state policymakers with valuable data to help understand student progress and school quality. This Policy Guide…
Descriptors: Summative Evaluation, Student Evaluation, Federal State Relationship, Public Policy
Baryktabasov, Kasym; Jumabaeva, Chinara; Brimkulov, Ulan – Research in Learning Technology, 2023
Many examinations with thousands of participating students are organized worldwide every year. Usually, this large number of students sit the exams simultaneously and answer almost the same set of questions. This method of learning assessment requires tremendous effort and resources to prepare the venues, print question books and organize the…
Descriptors: Information Technology, Computer Assisted Testing, Test Items, Adaptive Testing
Jiang, Zhuhan; Huang, Jiansheng – IEEE Transactions on Learning Technologies, 2022
Advanced digital technologies and social media have greatly improved both the learning experience and the assessment convenience, while inadvertently facilitated potential plagiarism and collaborative cheating at the same time. In this article, we will focus on the strategies and their technological implementations to run exams, or in-class tests…
Descriptors: Plagiarism, Educational Technology, Computer Assisted Testing, Cheating
Meagan Karvonen; Russell Swinburne Romine; Amy K. Clark – Practical Assessment, Research & Evaluation, 2024
This paper describes methods and findings from student cognitive labs, teacher cognitive labs, and test administration observations as evidence evaluated in a validity argument for a computer-based alternate assessment for students with significant cognitive disabilities. Validity of score interpretations and uses for alternate assessments based…
Descriptors: Students with Disabilities, Intellectual Disability, Severe Disabilities, Student Evaluation
Rizki Zakwandi; Edi Istiyono; Wipsar Sunu Brams Dwandaru – Education and Information Technologies, 2024
Computational Thinking (CT) skill was a part of the global framework of reference on Digital Literacy for Indicator 4.4.2, widely developed in mathematics and science learning. This study aimed to promote an assessment tool using a two-tier Computerized Adaptive Test (CAT). The study used the Design and Development Research (DDR) method with four…
Descriptors: Computer Assisted Testing, Adaptive Testing, Student Evaluation, Computation
Xin Wei – Grantee Submission, 2025
This study investigates the time-use patterns of students with learning disabilities during digital mathematics assessments and explores the role of extended time accommodations (ETA) in shaping these patterns. Using latent profile analysis, four distinct time-use profiles were identified separately for students with and without ETA. "Initial…
Descriptors: Computer Assisted Testing, Mathematics Tests, Students with Disabilities, Testing Accommodations
Ashish Gurung; Kirk Vanacore; Andrew A. McReynolds; Korinn S. Ostrow; Eamon S. Worden; Adam C. Sales; Neil T. Heffernan – Grantee Submission, 2024
Learning experience designers consistently balance the trade-off between open and close-ended activities. The growth and scalability of Computer Based Learning Platforms (CBLPs) have only magnified the importance of these design trade-offs. CBLPs often utilize close-ended activities (i.e. Multiple-Choice Questions [MCQs]) due to feasibility…
Descriptors: Multiple Choice Tests, Testing, Test Format, Computer Assisted Testing
Hon Keung Yau; Choi Ho Man – Turkish Online Journal of Educational Technology - TOJET, 2025
This study explores Hong Kong higher education students' perceptions of E-assessment systems, focusing on factors shaping acceptance of E-examinations over traditional formats. Quantitative analysis of 107 respondents reveals significant positive correlations between diverse pre-exam guidance (e.g., tutorials) and key system features (e.g.,…
Descriptors: Foreign Countries, College Students, Student Attitudes, Computer Assisted Testing
Kárász, Judit T.; Széll, Krisztián; Takács, Szabolcs – Quality Assurance in Education: An International Perspective, 2023
Purpose: Based on the general formula, which depends on the length and difficulty of the test, the number of respondents and the number of ability levels, this study aims to provide a closed formula for the adaptive tests with medium difficulty (probability of solution is p = 1/2) to determine the accuracy of the parameters for each item and in…
Descriptors: Test Length, Probability, Comparative Analysis, Difficulty Level

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