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Helen N. Levenson; Sara Amato; Ian Bogus; Fern E. Brody; Mary Miller; Jacob Nadal – College & Research Libraries, 2024
Shared print programs are helping their member libraries right-size their collections. As they do, there are concerns about the adverse impact of bibliographic inaccuracies. This paper studies bibliographic record inaccuracies and the resulting frequency of mismatches between an item owned and the record representing ownership. Through analysis of…
Descriptors: Libraries, Library Automation, Library Materials, Library Services
Barrot, Jessie S. – Computer Assisted Language Learning, 2023
Despite the building up of research on the adoption of automated writing evaluation (AWE) systems, the differential effects of automated written corrective feedback (AWCF) on errors with different severity levels and gains across writing tasks remain unclear. Thus, this study fills in the vacuum by examining how AWCF through Grammarly affects…
Descriptors: Automation, Written Language, Error Correction, Feedback (Response)
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
Liyanagunawardena, Tharindu R. – European Journal of Open, Distance and E-Learning, 2020
Transcripts and captions make videos more accessible to everyone. However, the time and resources required for manual transcription are a known barrier in creating accessible videos. This paper presents a small study where students (283) and tutors (27) reported their views on automatic transcriptions for recorded webinar videos. Despite not…
Descriptors: Transcripts (Written Records), Video Technology, Assistive Technology, Students with Disabilities
Danielle S. McNamara; Scott A. Crossley; Rod D. Roscoe; Laura K. Allen; Jianmin Dai – Grantee Submission, 2015
This study evaluates the use of a hierarchical classification approach to automated assessment of essays. Automated essay scoring (AES) generally relies onmachine learning techniques that compute essay scores using a set of text variables. Unlike previous studies that rely on regression models, this study computes essay scores using a hierarchical…
Descriptors: Automation, Scoring, Essays, Persuasive Discourse