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Swapna Haresh Teckwani; Amanda Huee-Ping Wong; Nathasha Vihangi Luke; Ivan Cherh Chiet Low – Advances in Physiology Education, 2024
The advent of artificial intelligence (AI), particularly large language models (LLMs) like ChatGPT and Gemini, has significantly impacted the educational landscape, offering unique opportunities for learning and assessment. In the realm of written assessment grading, traditionally viewed as a laborious and subjective process, this study sought to…
Descriptors: Accuracy, Reliability, Computational Linguistics, Standards
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Yishen Song; Qianta Zhu; Huaibo Wang; Qinhua Zheng – IEEE Transactions on Learning Technologies, 2024
Manually scoring and revising student essays has long been a time-consuming task for educators. With the rise of natural language processing techniques, automated essay scoring (AES) and automated essay revising (AER) have emerged to alleviate this burden. However, current AES and AER models require large amounts of training data and lack…
Descriptors: Scoring, Essays, Writing Evaluation, Computer Software
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Zhang, Mengxue; Heffernan, Neil; Lan, Andrew – International Educational Data Mining Society, 2023
Automated scoring of student responses to open-ended questions, including short-answer questions, has great potential to scale to a large number of responses. Recent approaches for automated scoring rely on supervised learning, i.e., training classifiers or fine-tuning language models on a small number of responses with human-provided score…
Descriptors: Scoring, Computer Assisted Testing, Mathematics Instruction, Mathematics Tests
Jiyeo Yun – English Teaching, 2023
Studies on automatic scoring systems in writing assessments have also evaluated the relationship between human and machine scores for the reliability of automated essay scoring systems. This study investigated the magnitudes of indices for inter-rater agreement and discrepancy, especially regarding human and machine scoring, in writing assessment.…
Descriptors: Meta Analysis, Interrater Reliability, Essays, Scoring
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Doewes, Afrizal; Saxena, Akrati; Pei, Yulong; Pechenizkiy, Mykola – International Educational Data Mining Society, 2022
In Automated Essay Scoring (AES) systems, many previous works have studied group fairness using the demographic features of essay writers. However, individual fairness also plays an important role in fair evaluation and has not been yet explored. Initialized by Dwork et al., the fundamental concept of individual fairness is "similar people…
Descriptors: Scoring, Essays, Writing Evaluation, Comparative Analysis
Yi Gui – ProQuest LLC, 2024
This study explores using transfer learning in machine learning for natural language processing (NLP) to create generic automated essay scoring (AES) models, providing instant online scoring for statewide writing assessments in K-12 education. The goal is to develop an instant online scorer that is generalizable to any prompt, addressing the…
Descriptors: Writing Tests, Natural Language Processing, Writing Evaluation, Scoring
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Yuko Hayashi; Yusuke Kondo; Yutaka Ishii – Innovation in Language Learning and Teaching, 2024
Purpose: This study builds a new system for automatically assessing learners' speech elicited from an oral discourse completion task (DCT), and evaluates the prediction capability of the system with a view to better understanding factors deemed influential in predicting speaking proficiency scores and the pedagogical implications of the system.…
Descriptors: English (Second Language), Second Language Learning, Second Language Instruction, Japanese
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Goecke, Benjamin; Schmitz, Florian; Wilhelm, Oliver – Journal of Intelligence, 2021
Performance in elementary cognitive tasks is moderately correlated with fluid intelligence and working memory capacity. These correlations are higher for more complex tasks, presumably due to increased demands on working memory capacity. In accordance with the binding hypothesis, which states that working memory capacity reflects the limit of a…
Descriptors: Intelligence, Cognitive Processes, Short Term Memory, Reaction Time
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Shermis, Mark D.; Lottridge, Sue; Mayfield, Elijah – Journal of Educational Measurement, 2015
This study investigated the impact of anonymizing text on predicted scores made by two kinds of automated scoring engines: one that incorporates elements of natural language processing (NLP) and one that does not. Eight data sets (N = 22,029) were used to form both training and test sets in which the scoring engines had access to both text and…
Descriptors: Scoring, Essays, Computer Assisted Testing, Natural Language Processing
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Dalton, Sarah Grace; Stark, Brielle C.; Fromm, Davida; Apple, Kristen; MacWhinney, Brian; Rensch, Amanda; Rowedder, Madyson – Journal of Speech, Language, and Hearing Research, 2022
Purpose: The aim of this study was to advance the use of structured, monologic discourse analysis by validating an automated scoring procedure for core lexicon (CoreLex) using transcripts. Method: Forty-nine transcripts from persons with aphasia and 48 transcripts from persons with no brain injury were retrieved from the AphasiaBank database. Five…
Descriptors: Validity, Discourse Analysis, Databases, Scoring
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Nadolski, Rob J.; Hummel, Hans G. K.; Rusman, Ellen; Ackermans, Kevin – Educational Technology Research and Development, 2021
Acquiring complex oral presentation skills is cognitively demanding for students and demands intensive teacher guidance. The aim of this study was twofold: (a) to identify and apply design guidelines in developing an effective formative assessment method for oral presentation skills during classroom practice, and (b) to develop and compare two…
Descriptors: Formative Evaluation, Oral Language, Scoring Rubrics, Design
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Uzun, Kutay – Contemporary Educational Technology, 2018
Managing crowded classes in terms of classroom assessment is a difficult task due to the amount of time which needs to be devoted to providing feedback to student products. In this respect, the present study aimed to develop an automated essay scoring environment as a potential means to overcome this problem. Secondarily, the study aimed to test…
Descriptors: Computer Assisted Testing, Essays, Scoring, English Literature
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Passonneau, Rebecca J.; Poddar, Ananya; Gite, Gaurav; Krivokapic, Alisa; Yang, Qian; Perin, Dolores – International Journal of Artificial Intelligence in Education, 2018
Development of reliable rubrics for educational intervention studies that address reading and writing skills is labor-intensive, and could benefit from an automated approach. We compare a main ideas rubric used in a successful writing intervention study to a highly reliable wise-crowd content assessment method developed to evaluate…
Descriptors: Computer Assisted Testing, Writing Evaluation, Content Analysis, Scoring Rubrics
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Al Habbash, Maha; Alsheikh, Negmeldin; Liu, Xu; Al Mohammedi, Najah; Al Othali, Safa; Ismail, Sadiq Abdulwahed – International Journal of Instruction, 2021
This convergent mixed method study aimed at exploring the English context of the widely used Emirates Standardized Test (EmSAT) by juxtaposing it to its sequel, the International English Language Testing System (IELTS). For this purpose, the study used the Common European Framework of Reference (CEFR) international standards which is used as a…
Descriptors: Language Tests, English (Second Language), Second Language Learning, Guidelines
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Vandeweerd, Nathan; Housen, Alex; Paquot, Magali – Language Testing, 2023
This study investigates whether re-thinking the separation of lexis and grammar in language testing could lead to more valid inferences about proficiency across modes. As argued by Römer, typical scoring rubrics ignore important information about proficiency encoded at the lexis-grammar interface, in particular how the co-selection of lexical and…
Descriptors: French, Language Tests, Grammar, Second Language Learning
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