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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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Balqis Albreiki; Tetiana Habuza; Nishi Palakkal; Nazar Zaki – Education and Information Technologies, 2024
The nature of education has been transformed by technological advances and online learning platforms, providing educational institutions with more options than ever to thrive in a complex and competitive environment. However, they still face challenges such as academic underachievement, graduation delays, and student dropouts. Fortunately, by…
Descriptors: Multivariate Analysis, Graphs, Identification, At Risk Students
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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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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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Wells, Craig S.; Sireci, Stephen G. – Applied Measurement in Education, 2020
Student growth percentiles (SGPs) are currently used by several states and school districts to provide information about individual students as well as to evaluate teachers, schools, and school districts. For SGPs to be defensible for these purposes, they should be reliable. In this study, we examine the amount of systematic and random error in…
Descriptors: Growth Models, Reliability, Scores, Error Patterns
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McDonald, Margarethe; Kwon, Taeahn; Kim, Hyunji; Lee, Youngki; Ko, Eon-Suk – Journal of Speech, Language, and Hearing Research, 2021
Purpose: The algorithm of the Language ENvironment Analysis (LENA) system for calculating language environment measures was trained on American English; thus, its validity with other languages cannot be assumed. This article evaluates the accuracy of the LENA system applied to Korean. Method: We sampled sixty 5-min recording clips involving 38 key…
Descriptors: Computational Linguistics, Korean, Audio Equipment, Accuracy
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Christensen, Rikke Vang – Journal of Speech, Language, and Hearing Research, 2019
Purpose: The aim of the study was to explore the potential of performance on a Danish sentence repetition (SR) task--including specific morphological and syntactic properties--to identify difficulties in children with developmental language disorder (DLD) relative to typically developing (TD) children. Furthermore, the potential of the task as a…
Descriptors: Indo European Languages, Morphology (Languages), Verbs, Grammar
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Fuentes-Luque, Adrián – Journal of Multilingual and Multicultural Development, 2017
Cuisine and restaurants are powerful tools for cultural, social and tourist image-building, and projection for tourist promotion, particularly in the case of major tourist places and destinations which boast a well-deserved, long-standing history and reputation for gastronomical beacons. When menus are not properly translated (or transcreated)…
Descriptors: Foreign Countries, Dining Facilities, Translation, Tourism
Liu, Ran; Koedinger, Kenneth R. – International Educational Data Mining Society, 2015
A growing body of research suggests that accounting for student specific variability in educational data can improve modeling accuracy and may have implications for individualizing instruction. The Additive Factors Model (AFM), a logistic regression model used to fit educational data and discover/refine skill models of learning, contains a…
Descriptors: Models, Regression (Statistics), Learning, Classification
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Groulx, Timothy J. – Journal of Research in Music Education, 2013
Music education students ("N" = 21) at a university in the southeastern United States took an error detection test that had been designed for this study to determine the effects of tonal contexts versus atonal contexts on the ability to detect performance errors. The investigator composed 16 melodies, 8 of which were tonal and 8 of which…
Descriptors: Error Patterns, Accuracy, Music, Music Education
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Hauser, Carl; Thum, Yeow Meng; He, Wei; Ma, Lingling – Educational and Psychological Measurement, 2015
When conducting item reviews, analysts evaluate an array of statistical and graphical information to assess the fit of a field test (FT) item to an item response theory model. The process can be tedious, particularly when the number of human reviews (HR) to be completed is large. Furthermore, such a process leads to decisions that are susceptible…
Descriptors: Test Items, Item Response Theory, Research Methodology, Decision Making
McClarty, Katie Larsen; Murphy, Daniel; Keng, Leslie; Turhan, Ahmet; Tong, Ye – Pearson, 2012
There is much focus at the state and national levels in graduating students that are prepared for college and careers. In order for students to be prepared at the end of their K-12 education, indicators are also needed along the way about whether students are on track. Using historical state data, nine different methods were used to align…
Descriptors: Elementary School Mathematics, Secondary School Mathematics, Alignment (Education), Academic Standards
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