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Henderson, Michael; Chung, Jennifer; Awdry, Rebecca; Ashford, Cliff; Bryant, Mike; Mundy, Matthew; Ryan, Kris – International Journal for Educational Integrity, 2023
Discussions around assessment integrity often focus on the exam conditions and the motivations and values of those who cheated in comparison with those who did not. We argue that discourse needs to move away from a binary representation of cheating. Instead, we propose that the conversation may be more productive and more impactful by focusing on…
Descriptors: College Students, Computer Assisted Testing, Cheating, Ambiguity (Semantics)
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Archana Praveen Kumar; Ashalatha Nayak; Manjula Shenoy K.; Chaitanya; Kaustav Ghosh – International Journal of Artificial Intelligence in Education, 2024
Multiple Choice Questions (MCQs) are a popular assessment method because they enable automated evaluation, flexible administration and use with huge groups. Despite these benefits, the manual construction of MCQs is challenging, time-consuming and error-prone. This is because each MCQ is comprised of a question called the "stem", a…
Descriptors: Multiple Choice Tests, Test Construction, Test Items, Semantics
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William Orwig; Emma R. Edenbaum; Joshua D. Greene; Daniel L. Schacter – Journal of Creative Behavior, 2024
Recent developments in computerized scoring via semantic distance have provided automated assessments of verbal creativity. Here, we extend past work, applying computational linguistic approaches to characterize salient features of creative text. We hypothesize that, in addition to semantic diversity, the degree to which a story includes…
Descriptors: Computer Assisted Testing, Scoring, Creativity, Computational Linguistics
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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
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Ormerod, Christopher; Lottridge, Susan; Harris, Amy E.; Patel, Milan; van Wamelen, Paul; Kodeswaran, Balaji; Woolf, Sharon; Young, Mackenzie – International Journal of Artificial Intelligence in Education, 2023
We introduce a short answer scoring engine made up of an ensemble of deep neural networks and a Latent Semantic Analysis-based model to score short constructed responses for a large suite of questions from a national assessment program. We evaluate the performance of the engine and show that the engine achieves above-human-level performance on a…
Descriptors: Computer Assisted Testing, Scoring, Artificial Intelligence, Semantics
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Dhini, Bachriah Fatwa; Girsang, Abba Suganda; Sufandi, Unggul Utan; Kurniawati, Heny – Asian Association of Open Universities Journal, 2023
Purpose: The authors constructed an automatic essay scoring (AES) model in a discussion forum where the result was compared with scores given by human evaluators. This research proposes essay scoring, which is conducted through two parameters, semantic and keyword similarities, using a SentenceTransformers pre-trained model that can construct the…
Descriptors: Computer Assisted Testing, Scoring, Writing Evaluation, Essays
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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
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Shuo Feng; Kailun Zhang – Second Language Research, 2025
The present study aims to explore how second language (L2) speakers process four types of presupposition triggers in an online self-paced reading task and an offline acceptability judgment task. The four types of triggers are definite expressions with "the," the factive verb "know," the change-of-state verb "stop" and…
Descriptors: Second Language Learning, Bilingualism, Computer Assisted Testing, Paper and Pencil Tests
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Becker, Kirk A.; Kao, Shu-chuan – Journal of Applied Testing Technology, 2022
Natural Language Processing (NLP) offers methods for understanding and quantifying the similarity between written documents. Within the testing industry these methods have been used for automatic item generation, automated scoring of text and speech, modeling item characteristics, automatic question answering, machine translation, and automated…
Descriptors: Item Banks, Natural Language Processing, Computer Assisted Testing, Scoring
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C. H., Dhawaleswar Rao; Saha, Sujan Kumar – IEEE Transactions on Learning Technologies, 2023
Multiple-choice question (MCQ) plays a significant role in educational assessment. Automatic MCQ generation has been an active research area for years, and many systems have been developed for MCQ generation. Still, we could not find any system that generates accurate MCQs from school-level textbook contents that are useful in real examinations.…
Descriptors: Multiple Choice Tests, Computer Assisted Testing, Automation, Test Items
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Goodwin, Amanda P.; Petscher, Yaacov; Reynolds, Dan – Scientific Studies of Reading, 2022
Purpose: This study explores the roles of morphological skills (Morphological Awareness, Morphological-Syntactic-Knowledge,Morphological-Semantic-Knowledge, and Morphological-Orthographic/Phonological-Knowledge), vocabulary (knowledge of definitions, relationships between words, and polysemous meanings), and syntax in contributing to adolescent…
Descriptors: Reading Comprehension, Morphology (Languages), Metalinguistics, Syntax
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Natalie Bleijlevens; Tanya Behne – Developmental Psychology, 2024
Upon hearing a novel label, listeners tend to assume that it refers to a novel, rather than a familiar object. While this disambiguation or mutual exclusivity (ME) effect has been robustly shown across development, it is unclear what it involves. Do listeners use their pragmatic and lexical knowledge to exclude the familiar object and thus select…
Descriptors: Ambiguity (Semantics), Toddlers, Adults, Cognitive Mapping
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Kurdi, Ghader; Leo, Jared; Parsia, Bijan; Sattler, Uli; Al-Emari, Salam – International Journal of Artificial Intelligence in Education, 2020
While exam-style questions are a fundamental educational tool serving a variety of purposes, manual construction of questions is a complex process that requires training, experience, and resources. This, in turn, hinders and slows down the use of educational activities (e.g. providing practice questions) and new advances (e.g. adaptive testing)…
Descriptors: Computer Assisted Testing, Adaptive Testing, Natural Language Processing, Questioning Techniques
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Yang, Yu'an; Goodhue, Daniel; Hacquard, Valentine; Lidz, Jeffrey – Language Acquisition: A Journal of Developmental Linguistics, 2022
"Wh"-phrases in Mandarin have an interrogative (like English "what") and an indefinite (like English "a/some") interpretation. Previous comprehension studies find that children can access both interpretations around 4.5 years old; studies with younger children focus on production and find that children between 2 and…
Descriptors: Phrase Structure, Mandarin Chinese, Morphemes, Language Processing
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Taikh, Alexander; Lupker, Stephen J. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2020
Considerable research effort has been devoted to investigating semantic priming effects, particularly, the locus of those effects. Semantically related primes might activate their target's lexical representation (through automatic spreading activation at short stimulus onset asynchronies (SOAs), or through generation of words expected to follow…
Descriptors: Semantics, Cues, Priming, Language Processing
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