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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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Andrea Horbach; Joey Pehlke; Ronja Laarmann-Quante; Yuning Ding – International Journal of Artificial Intelligence in Education, 2024
This paper investigates crosslingual content scoring, a scenario where scoring models trained on learner data in one language are applied to data in a different language. We analyze data in five different languages (Chinese, English, French, German and Spanish) collected for three prompts of the established English ASAP content scoring dataset. We…
Descriptors: Contrastive Linguistics, Scoring, Learning Analytics, Chinese
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Rebeckah K. Fussell; Megan Flynn; Anil Damle; Michael F. J. Fox; N. G. Holmes – Physical Review Physics Education Research, 2025
Recent advancements in large language models (LLMs) hold significant promise for improving physics education research that uses machine learning. In this study, we compare the application of various models for conducting a large-scale analysis of written text grounded in a physics education research classification problem: identifying skills in…
Descriptors: Physics, Computational Linguistics, Classification, Laboratory Experiments
Xuandong Zhao – ProQuest LLC, 2024
The rapid advancement of powerful Large Language Models (LLMs), such as ChatGPT and Llama, has revolutionized the world by bringing new creative possibilities and enhancing productivity. However, these advancements also pose significant challenges and risks, including the potential for misuse in the form of fake news, academic dishonesty,…
Descriptors: Computational Linguistics, Intellectual Property, Artificial Intelligence, Productivity
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Sharaff, Aakanksha; Nagwani, Naresh Kumar – International Journal of Web-Based Learning and Teaching Technologies, 2020
A multi-label variant of email classification named ML-EC[superscript 2] (multi-label email classification using clustering) has been proposed in this work. ML-EC[superscript 2] is a hybrid algorithm based on text clustering, text classification, frequent-term calculation (based on latent dirichlet allocation), and taxonomic term-mapping…
Descriptors: Electronic Mail, Classification, Taxonomy, Indexes
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Owen Henkel; Hannah Horne-Robinson; Maria Dyshel; Greg Thompson; Ralph Abboud; Nabil Al Nahin Ch; Baptiste Moreau-Pernet; Kirk Vanacore – Journal of Learning Analytics, 2025
This paper introduces AMMORE, a new dataset of 53,000 math open-response question-answer pairs from Rori, a mathematics learning platform used by middle and high school students in several African countries. Using this dataset, we conducted two experiments to evaluate the use of large language models (LLM) for grading particularly challenging…
Descriptors: Learning Analytics, Learning Management Systems, Mathematics Instruction, Middle School Students
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Harun, Harryizman; Othman, Azliza; Annamalai, Subashini – Journal of Language and Linguistic Studies, 2021
The motif of the folktale is essentially the smallest and striking elements of narrative content with the ability to endure in tradition. It is divided into three categories: actor, item and single incident. These categories become the foundation that supports the built of the original motif-index of folk literature by Stith Thompson.…
Descriptors: Folk Culture, Literature, Text Structure, Computational Linguistics
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Lijin Zhang; Xueyang Li; Zhiyong Zhang – Grantee Submission, 2023
The thriving developer community has a significant impact on the widespread use of R software. To better understand this community, we conducted a study analyzing all R packages available on CRAN. We identified the most popular topics of R packages by text mining the package descriptions. Additionally, using network centrality measures, we…
Descriptors: Computer Software, Programming Languages, Data Analysis, Visual Aids
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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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Büsra Aras; Sultan Bozkurt; Serap Önen – Novitas-ROYAL (Research on Youth and Language), 2024
This research aimed to scrutinize and delineate the figures of speech manifesting in English hit songs. A selection of songs from Spotify's Top 30 Hit Songs List was chosen as the study material. The study employed a qualitative content analysis approach to classify the type and calculate the frequency of the figures of speech within the corpus of…
Descriptors: Figurative Language, English (Second Language), Second Language Learning, Second Language Instruction
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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
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Zakaria, Azrifah; Renandya, Willy A.; Aryadoustc, Vahid – LEARN Journal: Language Education and Acquisition Research Network, 2023
Studies on graded readers used in extensive reading have tended to focus on vocabulary. This study set out to investigate the linguistic profile of graded readers, taking into account both grammar and lexis. A corpus of 90 readers were tagged according to the variables in Biber's Multidimensional (MD) analysis, using the Multidimensional Analysis…
Descriptors: Computational Linguistics, Grammar, Reading Materials, Classification
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Yoo, Jiseung; Kim, Min Kyeong – Contemporary Educational Technology, 2023
This study focuses on how teachers' pedagogical content knowledge (PCK) of mathematics may differ depending on teacher interactions in an online teacher community of practice (CoP). The study utilizes data from 26,857 posts collected from the South Korean self-generated online teacher CoP, 'Indischool'. This data was then analyzed using natural…
Descriptors: Natural Language Processing, Elementary School Teachers, Pedagogical Content Knowledge, Mathematics Instruction
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Thaweesak Chanpradit; Phakkaramai Samran; Siriprapa Saengpinit; Pailin Subkasin – Journal of English Teaching, 2024
AI-generated paraphrasing tools, especially QuillBot and Paraphrasing Tool, play a crucial role in preventing plagiarism in academic writing. However, their effectiveness and proficiency have been questioned, particularly regarding the adequacy of their strategies. This qualitative study analyzed the paraphrasing strategies and proficiency levels…
Descriptors: English (Second Language), Second Language Learning, Second Language Instruction, Phrase Structure
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Christopher E. Gomez; Marcelo O. Sztainberg; Rachel E. Trana – International Journal of Bullying Prevention, 2022
Cyberbullying is the use of digital communication tools and spaces to inflict physical, mental, or emotional distress. This serious form of aggression is frequently targeted at, but not limited to, vulnerable populations. A common problem when creating machine learning models to identify cyberbullying is the availability of accurately annotated,…
Descriptors: Video Technology, Computer Software, Computer Mediated Communication, Bullying
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