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Po-Chun Huang; Ying-Hong Chan; Ching-Yu Yang; Hung-Yuan Chen; Yao-Chung Fan – IEEE Transactions on Learning Technologies, 2024
Question generation (QG) task plays a crucial role in adaptive learning. While significant QG performance advancements are reported, the existing QG studies are still far from practical usage. One point that needs strengthening is to consider the generation of question group, which remains untouched. For forming a question group, intrafactors…
Descriptors: Automation, Test Items, Computer Assisted Testing, Test Construction
Pauline Frizelle; Ana Buckley; Tricia Biancone; Anna Ceroni; Darren Dahly; Paul Fletcher; Dorothy V. M. Bishop; Cristina McKean – Journal of Child Language, 2024
This study reports on the feasibility of using the Test of Complex Syntax- Electronic (TECS-E), as a self-directed app, to measure sentence comprehension in children aged 4 to 5 ½ years old; how testing apps might be adapted for effective independent use; and agreement levels between face-to-face supported computerized and independent computerized…
Descriptors: Language Processing, Computer Software, Language Tests, Syntax
Putnikovic, Marko; Jovanovic, Jelena – IEEE Transactions on Learning Technologies, 2023
Automatic grading of short answers is an important task in computer-assisted assessment (CAA). Recently, embeddings, as semantic-rich textual representations, have been increasingly used to represent short answers and predict the grade. Despite the recent trend of applying embeddings in automatic short answer grading (ASAG), there are no…
Descriptors: Automation, Computer Assisted Testing, Grading, Natural Language Processing
Semere Kiros Bitew; Amir Hadifar; Lucas Sterckx; Johannes Deleu; Chris Develder; Thomas Demeester – IEEE Transactions on Learning Technologies, 2024
Multiple-choice questions (MCQs) are widely used in digital learning systems, as they allow for automating the assessment process. However, owing to the increased digital literacy of students and the advent of social media platforms, MCQ tests are widely shared online, and teachers are continuously challenged to create new questions, which is an…
Descriptors: Multiple Choice Tests, Computer Assisted Testing, Test Construction, Test Items
Stefan Ruseti; Ionut Paraschiv; Mihai Dascalu; Danielle S. McNamara – Grantee Submission, 2024
Automated Essay Scoring (AES) is a well-studied problem in Natural Language Processing applied in education. Solutions vary from handcrafted linguistic features to large Transformer-based models, implying a significant effort in feature extraction and model implementation. We introduce a novel Automated Machine Learning (AutoML) pipeline…
Descriptors: Computer Assisted Testing, Scoring, Automation, Essays
Stefan Ruseti; Ionut Paraschiv; Mihai Dascalu; Danielle S. McNamara – International Journal of Artificial Intelligence in Education, 2024
Automated Essay Scoring (AES) is a well-studied problem in Natural Language Processing applied in education. Solutions vary from handcrafted linguistic features to large Transformer-based models, implying a significant effort in feature extraction and model implementation. We introduce a novel Automated Machine Learning (AutoML) pipeline…
Descriptors: Computer Assisted Testing, Scoring, Automation, Essays
Yi-Jui I. Chen; Yi-Jhen Wu; Yi-Hsin Chen; Robin Irey – Journal of Psychoeducational Assessment, 2025
A short form of the 60-item computer-based orthographic processing assessment (long-form COPA or COPA-LF) was developed. The COPA-LF consists of five skills, including rapid perception, access, differentiation, correction, and arrangement. Thirty items from the COPA-LF were selected for the short-form COPA (COPA-SF) based on cognitive diagnostic…
Descriptors: Computer Assisted Testing, Test Length, Test Validity, Orthographic Symbols
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
Qiao, Chen; Hu, Xiao – IEEE Transactions on Learning Technologies, 2023
Free text answers to short questions can reflect students' mastery of concepts and their relationships relevant to learning objectives. However, automating the assessment of free text answers has been challenging due to the complexity of natural language. Existing studies often predict the scores of free text answers in a "black box"…
Descriptors: Computer Assisted Testing, Automation, Test Items, Semantics
Ying Fang; Rod D. Roscoe; Danielle S. McNamara – Grantee Submission, 2023
Artificial Intelligence (AI) based assessments are commonly used in a variety of settings including business, healthcare, policing, manufacturing, and education. In education, AI-based assessments undergird intelligent tutoring systems as well as many tools used to evaluate students and, in turn, guide learning and instruction. This chapter…
Descriptors: Artificial Intelligence, Computer Assisted Testing, Student Evaluation, Evaluation Methods
Firoozi, Tahereh; Bulut, Okan; Epp, Carrie Demmans; Naeimabadi, Ali; Barbosa, Denilson – Journal of Applied Testing Technology, 2022
Automated Essay Scoring (AES) using neural networks has helped increase the accuracy and efficiency of scoring students' written tasks. Generally, the improved accuracy of neural network approaches has been attributed to the use of modern word embedding techniques. However, which word embedding techniques produce higher accuracy in AES systems…
Descriptors: Computer Assisted Testing, Scoring, Essays, Artificial Intelligence
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
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
Holly Robson; Harriet Thomasson; Matthew H. Davis – International Journal of Language & Communication Disorders, 2024
Background: The use of telepractice in aphasia research and therapy is increasing in frequency. Teleassessment in aphasia has been demonstrated to be reliable. However, neuropsychological and clinical language comprehension assessments are not always readily translatable to an online environment and people with severe language comprehension or…
Descriptors: Aphasia, Severity (of Disability), Videoconferencing, Comparative Analysis
Schneider, Johannes; Richner, Robin; Riser, Micha – International Journal of Artificial Intelligence in Education, 2023
Autograding short textual answers has become much more feasible due to the rise of NLP and the increased availability of question-answer pairs brought about by a shift to online education. Autograding performance is still inferior to human grading. The statistical and black-box nature of state-of-the-art machine learning models makes them…
Descriptors: Grading, Natural Language Processing, Computer Assisted Testing, Ethics