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Yu Liu; Jing Zhang; Miranda May McIntyre; Gölge Seferoglu; Montgomery Van Wart – International Journal of Adult Education and Technology, 2025
This study investigates students' perceptions of rehearsal (test preparation) and testing after the pandemic forced increased online teaching use and experimentation. Data was gathered from information and decision sciences (IDS) students in an underrepresented minority (URM) serving university. Responses from 136 participants were analyzed and…
Descriptors: Test Preparation, Electronic Learning, Testing, Student Attitudes
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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
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Debarati Mukherjee; Supriya Bhavnani; Georgia Lockwood Estrin; Vaisnavi Rao; Jayashree Dasgupta; Hiba Irfan; Bhismadev Chakrabarti; Vikram Patel; Matthew K. Belmonte – Autism: The International Journal of Research and Practice, 2024
Current challenges in early identification of autism spectrum disorder lead to significant delays in starting interventions, thereby compromising outcomes. Digital tools can potentially address this barrier as they are accessible, can measure autism-relevant phenotypes and can be administered in children's natural environments by non-specialists.…
Descriptors: Autism Spectrum Disorders, Young Children, Evaluation, Technology
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Fu Chen; Chang Lu; Ying Cui – Education and Information Technologies, 2024
Successful computer-based assessments for learning greatly rely on an effective learner modeling approach to analyze learner data and evaluate learner behaviors. In addition to explicit learning performance (i.e., product data), the process data logged by computer-based assessments provide a treasure trove of information about how learners solve…
Descriptors: Computer Assisted Testing, Problem Solving, Learning Analytics, Learning Processes
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Priti Oli; Rabin Banjade; Jeevan Chapagain; Vasile Rus – Grantee Submission, 2024
Assessing students' answers and in particular natural language answers is a crucial challenge in the field of education. Advances in transformer-based models such as Large Language Models (LLMs), have led to significant progress in various natural language tasks. Nevertheless, amidst the growing trend of evaluating LLMs across diverse tasks,…
Descriptors: Student Evaluation, Computer Assisted Testing, Artificial Intelligence, Comprehension
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Ata Jahangir Moshayedi; Atanu Shuvam Roy; Zeashan Hameed Khan; Hong Lan; Habibollah Lotfi; Xiaohong Zhang – Education and Information Technologies, 2025
In this paper, a secure exam proctoring assistant 'EMTIHAN' (which means exam in Arabic/Persian/Urdu/Turkish languages) is developed to address concerns related to online exams for handwritten topics by allowing students to submit their answers online securely via their mobile devices. This system is designed with an aim to lessen the student's…
Descriptors: Computer Assisted Testing, Distance Education, MOOCs, Virtual Classrooms
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Ishaya Gambo; Faith-Jane Abegunde; Omobola Gambo; Roseline Oluwaseun Ogundokun; Akinbowale Natheniel Babatunde; Cheng-Chi Lee – Education and Information Technologies, 2025
The current educational system relies heavily on manual grading, posing challenges such as delayed feedback and grading inaccuracies. Automated grading tools (AGTs) offer solutions but come with limitations. To address this, "GRAD-AI" is introduced, an advanced AGT that combines automation with teacher involvement for precise grading,…
Descriptors: Automation, Grading, Artificial Intelligence, Computer Assisted Testing
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Simon Ntumi – Discover Education, 2025
This study investigated the impact of AI-powered adaptive testing on student academic performance and test anxiety, comparing its effectiveness to traditional testing methods. Using a quantitative research approach, hierarchical regression analysis was employed to examine the influence of adaptive testing on student outcomes, controlling for…
Descriptors: Adaptive Testing, Computer Assisted Testing, Artificial Intelligence, Test Anxiety
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Robert L. Moore; Sophia Soomin Lee; Amanda Taylor Pate; Amanda J. Wilson – Distance Education, 2025
This systematic review synthesizes 14 peer-reviewed studies from 2015 to 2023, focusing on the assessment methods and delivery of digital microcredentials. Microcredentials provide specialized, focused content and recognize professional learning or competency in specific skills. This paper defines digital microcredentials as those offered in an…
Descriptors: Literature Reviews, Microcredentials, Evaluation Methods, Program Evaluation
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K. Talman; J. Vierula; T. Karihtala; E. Laakkonen; J. Engblom; E. Haavisto – Higher Education Quarterly, 2025
Higher education institutions need to develop valid, fair, and objective selection methods. Current literature reporting the development and validation of new national large-scale selection tests is scarce. This two-phased study aimed to (1) develop and (2) evaluate the validity of the Finnish digital Universities of Applied Sciences Entrance…
Descriptors: Admission Criteria, Test Construction, Test Validity, Computer Assisted Testing
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Rajagopal Sankaranarayanan; Mohan Yang; Kyungbin Kwon – Journal of Computing in Higher Education, 2025
The purpose of this study is to explore the influence of the microlearning instructional approach in an online introductory database programming classroom. The ultimate goal of this study is to inform educators and instructional designers on the design and development of microlearning content that maximizes student learning. Grounded within the…
Descriptors: Teaching Methods, Introductory Courses, Databases, Programming
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Danielle Lottridge; Davis Dimalen; Gerald Weber – ACM Transactions on Computing Education, 2025
Automated assessment is well-established within computer science courses but largely absent from human--computer interaction courses. Automating the assessment of human--computer interaction (HCI) is challenging because the coursework tends not to be computational but rather highly creative, such as designing and implementing interactive…
Descriptors: Computer Science Education, Computer Assisted Testing, Automation, Man Machine Systems
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Sayed Mohammad Mahdi Vaghei; Mozhgan Asadi; Banafshe Mansuri – International Journal of Language & Communication Disorders, 2025
Background: Remote assessment through telehealth offers potential benefits for people with aphasia (PWA), particularly where in-person services are limited. However, its diagnostic validity in developing countries remains underexplored due to differences in infrastructure and digital literacy. Aims: To compare in-person and remote administration…
Descriptors: Computer Assisted Testing, Aphasia, Indo European Languages, Adults
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Nicholas Hobbs; Janet Steele – HAPS Educator, 2025
Formative assessment is a type of low-stakes assessment that can help reduce student anxiety and encourage students to engage in the course regularly. Formative assessment can also help instructors identify knowledge gaps. Students who had completed the first semester of a two-semester sophomore-level anatomy and physiology sequence were surveyed…
Descriptors: Anatomy, Physiology, Tests, Learner Engagement
Ildiko Porter-Szucs; Cynthia J. Macknish; Suzanne Toohey – John Wiley & Sons, Inc, 2025
"A Practical Guide to Language Assessment" helps educators at every level redefine their approach to language assessment. Grounded in extensive research and aligned with the latest advances in language education, this comprehensive guide introduces foundational concepts and explores key principles in test development and item writing.…
Descriptors: Student Evaluation, Language Tests, Test Construction, Test Items
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