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Bin Tan; Nour Armoush; Elisabetta Mazzullo; Okan Bulut; Mark J. Gierl – International Journal of Assessment Tools in Education, 2025
This study reviews existing research on the use of large language models (LLMs) for automatic item generation (AIG). We performed a comprehensive literature search across seven research databases, selected studies based on predefined criteria, and summarized 60 relevant studies that employed LLMs in the AIG process. We identified the most commonly…
Descriptors: Artificial Intelligence, Test Items, Automation, Test Format
Sandra Nilsson; Elisabet Östlund; Yvonne Thalén; Ulrika Löfkvist – Journal of Speech, Language, and Hearing Research, 2025
Purpose: The Language ENvironment Analysis (LENA) is a technological tool designed for comprehensive recordings and automated analysis of young children's daily language and auditory environments. LENA recordings play a crucial role in both clinical interventions and research, offering insights into the amount of spoken language children are…
Descriptors: Foreign Countries, Family Environment, Toddlers, Oral Language
Blaženka Divjak; Barbi Svetec; Damir Horvat – Journal of Computer Assisted Learning, 2024
Background: Sound learning design should be based on the constructive alignment of intended learning outcomes (LOs), teaching and learning activities and formative and summative assessment. Assessment validity strongly relies on its alignment with LOs. Valid and reliable formative assessment can be analysed as a predictor of students' academic…
Descriptors: Automation, Formative Evaluation, Test Validity, Test Reliability
Huawei, Shi; Aryadoust, Vahid – Education and Information Technologies, 2023
Automated writing evaluation (AWE) systems are developed based on interdisciplinary research and technological advances such as natural language processing, computer sciences, and latent semantic analysis. Despite a steady increase in research publications in this area, the results of AWE investigations are often mixed, and their validity may be…
Descriptors: Writing Evaluation, Writing Tests, Computer Assisted Testing, Automation
Falcão, Filipe; Pereira, Daniela Marques; Gonçalves, Nuno; De Champlain, Andre; Costa, Patrício; Pêgo, José Miguel – Advances in Health Sciences Education, 2023
Automatic Item Generation (AIG) refers to the process of using cognitive models to generate test items using computer modules. It is a new but rapidly evolving research area where cognitive and psychometric theory are combined into digital framework. However, assessment of the item quality, usability and validity of AIG relative to traditional…
Descriptors: Computer Assisted Testing, Test Construction, Test Items, Automation
Han, Chao – Language Testing, 2022
Over the past decade, testing and assessing spoken-language interpreting has garnered an increasing amount of attention from stakeholders in interpreter education, professional certification, and interpreting research. This is because in these fields assessment results provide a critical evidential basis for high-stakes decisions, such as the…
Descriptors: Translation, Language Tests, Testing, Evaluation Methods
Ryoo, Ji Hoon; Park, Sunhee; Suh, Hongwook; Choi, Jaehwa; Kwon, Jongkyum – SAGE Open, 2022
In the development of cognitive science understanding human intelligence and mind, measurement of cognitive ability has played a key role. To address the development in data scientific point of views related to cognitive neuroscience, there has been a demand of creating a measurement to capture cognition in short and repeated time periods. This…
Descriptors: Cognitive Ability, Psychometrics, Test Validity, Test Construction
Guher Gorgun; Okan Bulut – Educational Measurement: Issues and Practice, 2025
Automatic item generation may supply many items instantly and efficiently to assessment and learning environments. Yet, the evaluation of item quality persists to be a bottleneck for deploying generated items in learning and assessment settings. In this study, we investigated the utility of using large-language models, specifically Llama 3-8B, for…
Descriptors: Artificial Intelligence, Quality Control, Technology Uses in Education, Automation
Charles Hulme; Joshua McGrane; Mihaela Duta; Gillian West; Denise Cripps; Abhishek Dasgupta; Sarah Hearne; Rachel Gardner; Margaret Snowling – Language, Speech, and Hearing Services in Schools, 2024
Purpose: Oral language skills provide a critical foundation for formal education and especially for the development of children's literacy (reading and spelling) skills. It is therefore important for teachers to be able to assess children's language skills, especially if they are concerned about their learning. We report the development and…
Descriptors: Automation, Language Tests, Standardized Tests, Test Construction
Lottridge, Sue; Burkhardt, Amy; Boyer, Michelle – Educational Measurement: Issues and Practice, 2020
In this digital ITEMS module, Dr. Sue Lottridge, Amy Burkhardt, and Dr. Michelle Boyer provide an overview of automated scoring. Automated scoring is the use of computer algorithms to score unconstrained open-ended test items by mimicking human scoring. The use of automated scoring is increasing in educational assessment programs because it allows…
Descriptors: Computer Assisted Testing, Scoring, Automation, Educational Assessment
Rao, Dhawaleswar; Saha, Sujan Kumar – IEEE Transactions on Learning Technologies, 2020
Automatic multiple choice question (MCQ) generation from a text is a popular research area. MCQs are widely accepted for large-scale assessment in various domains and applications. However, manual generation of MCQs is expensive and time-consuming. Therefore, researchers have been attracted toward automatic MCQ generation since the late 90's.…
Descriptors: Multiple Choice Tests, Test Construction, Automation, Computer Software
Ziwei Zhou – ProQuest LLC, 2020
In light of the ever-increasing capability of computer technology and advancement in speech and natural language processing techniques, automated speech scoring of constructed responses is gaining popularity in many high-stakes assessment and low-stakes educational settings. Automated scoring is a highly interdisciplinary and complex subject, and…
Descriptors: Certification, Speech Skills, Automation, Scoring
Doris Zahner; Jeffrey T. Steedle; James Soland; Catherine Welch; Qi Qin; Kathryn Thompson; Richard Phelps – Online Submission, 2023
The "Standards for Educational and Psychological Testing" have served as a cornerstone for best practices in assessment. As the field evolves, so must these standards, with regular revisions ensuring they reflect current knowledge and practice. The National Council on Measurement in Education (NCME) conducted a survey to gather feedback…
Descriptors: Standards, Educational Assessment, Psychological Testing, Best Practices
Martínez-Huertas, José Á.; Jastrzebska, Olga; Olmos, Ricardo; León, José A. – Assessment & Evaluation in Higher Education, 2019
Automated summary evaluation is proposed as an alternative to rubrics and multiple-choice tests in knowledge assessment. Inbuilt rubric is a recent Latent Semantic Analysis (LSA) method that implements rubrics in an artificially-generated semantic space. It was compared with classical LSA's cosine-based methods assessing knowledge in a…
Descriptors: Automation, Scoring Rubrics, Alternative Assessment, Test Reliability
Cohen, Yoav; Levi, Effi; Ben-Simon, Anat – Applied Measurement in Education, 2018
In the current study, two pools of 250 essays, all written as a response to the same prompt, were rated by two groups of raters (14 or 15 raters per group), thereby providing an approximation to the essay's true score. An automated essay scoring (AES) system was trained on the datasets and then scored the essays using a cross-validation scheme. By…
Descriptors: Test Validity, Automation, Scoring, Computer Assisted Testing