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Showing 1 to 15 of 21 results Save | Export
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Peter Baldwin; Victoria Yaneva; Kai North; Le An Ha; Yiyun Zhou; Alex J. Mechaber; Brian E. Clauser – Journal of Educational Measurement, 2025
Recent developments in the use of large-language models have led to substantial improvements in the accuracy of content-based automated scoring of free-text responses. The reported accuracy levels suggest that automated systems could have widespread applicability in assessment. However, before they are used in operational testing, other aspects of…
Descriptors: Artificial Intelligence, Scoring, Computational Linguistics, Accuracy
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Chi Hong Leung; Winslet Ting Yan Chan – Asian Journal of Contemporary Education, 2025
This paper explores the efficacy of ChatGPT, a generative artificial intelligence in educational contexts, particularly concerning its potential to assist students in overcoming academic challenges while highlighting its limitations. ChatGPT is suitable for solving general problems. When a student comes across academic challenges, ChatGPT may…
Descriptors: Artificial Intelligence, Computer Software, Technology Uses in Education, Error Patterns
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Turner, Simon Lee; Korevaar, Elizabeth; Cumpston, Miranda S.; Kanukula, Raju; Forbes, Andrew B.; McKenzie, Joanne E. – Research Synthesis Methods, 2023
Interrupted time series (ITS) studies are frequently used to examine the impact of population-level interventions or exposures. Systematic reviews with meta-analyses including ITS designs may inform public health and policy decision-making. Re-analysis of ITS may be required for inclusion in meta-analysis. While publications of ITS rarely provide…
Descriptors: Quasiexperimental Design, Graphs, Accuracy, Computation
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Steven J. Pentland; Christie M. Fuller; Lee A. Spitzley; Douglas P. Twitchell – International Journal of Social Research Methodology, 2023
The analysis of spoken language has been integral to a breadth of research in social science and beyond. However, for analyses to occur with efficiency, language must be in the form of computer-readable text. Historically, the speech-to-text process has occurred manually using human transcriptionists. Automated speech recognition (ASR) is…
Descriptors: Accuracy, Social Science Research, Classification, Reading Processes
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Salem, Alexandra C.; Gale, Robert; Casilio, Marianne; Fleegle, Mikala; Fergadiotis, Gerasimos; Bedrick, Steven – Journal of Speech, Language, and Hearing Research, 2023
Purpose: ParAlg (Paraphasia Algorithms) is a software that automatically categorizes a person with aphasia's naming error (paraphasia) in relation to its intended target on a picture-naming test. These classifications (based on lexicality as well as semantic, phonological, and morphological similarity to the target) are important for…
Descriptors: Semantics, Computer Software, Aphasia, Classification
Liceralde, Van Rynald T. – ProQuest LLC, 2021
When we read, errors in oculomotor programming can cause the eyes to land and fixate on different words from what the mind intended. Previous work suggests that these "mislocated fixations" form 10-30% of first-pass fixations in reading eye movement data, which presents theoretical and analytic issues for eyetracking-while-reading…
Descriptors: Eye Movements, Reading Processes, Error Patterns, Psychomotor Skills
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Dillon, Thomas; Wells, Donald – English Teaching, 2023
This study examined effects of pronunciation training using automatic speech recognition technology on common pronunciation errors of Korean English learners. Participants were divided into two groups. One group was given instruction and training about the use of automatic speech recognition for pronunciation practice. The other group was not…
Descriptors: Pronunciation, English (Second Language), Second Language Instruction, English Language Learners
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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Southwell, Rosy; Pugh, Samuel; Perkoff, E. Margaret; Clevenger, Charis; Bush, Jeffrey B.; Lieber, Rachel; Ward, Wayne; Foltz, Peter; D'Mello, Sidney – International Educational Data Mining Society, 2022
Automatic speech recognition (ASR) has considerable potential to model aspects of classroom discourse with the goals of automated assessment, feedback, and instructional support. However, modeling student talk is besieged by numerous challenges including a lack of data for child speech, low signal to noise ratio, speech disfluencies, and…
Descriptors: Audio Equipment, Error Analysis (Language), Classroom Communication, Feedback (Response)
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Christopher Saarna – International Journal of Technology in Education, 2024
This study seeks to clarify whether teachers are able to distinguish between essays written by English L2 students or generated by ChatGPT. 47 instructors who hold experience teaching English to native speakers of Japanese in universities or other higher education institutions were tested on whether they could identify between human written essays…
Descriptors: Identification, Artificial Intelligence, Computer Software, Grammar
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Loboda, Krzysztof; Mastela, Olga – Interpreter and Translator Trainer, 2023
Mass adoption of neural machine translation (NMT) tools in the translation workflow has exerted a significant impact on the language services industry over the last decade. There are claims that with the advent of NMT, automated translation has reached human parity for translating news (see, e.g. Popel et al. 2020). Moreover, some machine…
Descriptors: Computer Software, Computational Linguistics, Polish, Folk Culture
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Liu, Chengyuan; Cui, Jialin; Shang, Ruixuan; Xiao, Yunkai; Jia, Qinjin; Gehringer, Edward – International Educational Data Mining Society, 2022
An online peer-assessment system typically allows students to give textual feedback to their peers, with the goal of helping the peers improve their work. The amount of help that students receive is highly dependent on the quality of the reviews. Previous studies have investigated using machine learning to detect characteristics of reviews (e.g.,…
Descriptors: Peer Evaluation, Feedback (Response), Computer Mediated Communication, Teaching Methods
Botarleanu, Robert-Mihai; Dascalu, Mihai; Watanabe, Micah; Crossley, Scott Andrew; McNamara, Danielle S. – Grantee Submission, 2022
Age of acquisition (AoA) is a measure of word complexity which refers to the age at which a word is typically learned. AoA measures have shown strong correlations with reading comprehension, lexical decision times, and writing quality. AoA scores based on both adult and child data have limitations that allow for error in measurement, and increase…
Descriptors: Age Differences, Vocabulary Development, Correlation, Reading Comprehension
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Song, Qiuyuan – English Language Teaching, 2021
This study aims to explore how corpus-based approaches can be used to address the distinctions of English near-synonyms effectively. Especially, it collected source data from the British National Corpus (BNC) and adopted Sketch Engine (SkE) as an analyzing tool to compare the near synonymous pair "damage" and "destroy" commonly…
Descriptors: Computational Linguistics, Phrase Structure, English, Language Usage
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Woodworth, Johanathan; Barkaoui, Khaled – TESL Canada Journal, 2020
While feedback is widely considered essential for second language (L2) writing development (Bitchener & Ferris, 2012), teachers may not always be able to provide their learners with immediate and frequent corrective feedback. Automated writing evaluation (AWE) systems can help respond to this challenge by providing L2 learners with written…
Descriptors: Writing Evaluation, Feedback (Response), Error Correction, Second Language Instruction
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