A Framework for Large-Scale Automatic Fluency Assessment

A Framework for Large-Scale Automatic Fluency Assessment

Warley Almeida Silva, Luiz Carlos Carchedi, Jorão Gomes Junior, João Victor de Souza, Eduardo Barrere, Jairo Francisco de Souza
Copyright: © 2021 |Volume: 19 |Issue: 3 |Pages: 19
ISSN: 1539-3100|EISSN: 1539-3119|EISBN13: 9781799859260|DOI: 10.4018/IJDET.2021070105
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MLA

Silva, Warley Almeida, et al. "A Framework for Large-Scale Automatic Fluency Assessment." IJDET vol.19, no.3 2021: pp.70-88. http://doi.org/10.4018/IJDET.2021070105

APA

Silva, W. A., Carchedi, L. C., Junior, J. G., Victor de Souza, J., Barrere, E., & Francisco de Souza, J. (2021). A Framework for Large-Scale Automatic Fluency Assessment. International Journal of Distance Education Technologies (IJDET), 19(3), 70-88. http://doi.org/10.4018/IJDET.2021070105

Chicago

Silva, Warley Almeida, et al. "A Framework for Large-Scale Automatic Fluency Assessment," International Journal of Distance Education Technologies (IJDET) 19, no.3: 70-88. http://doi.org/10.4018/IJDET.2021070105

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Abstract

Learning assessments are important to monitor the progress of students throughout the teaching process. In the digital era, many local and large-scale learning assessments are conducted through technological tools. In this view, a large-scale learning assessment can be designed to tackle one or multiple parts of the teaching process. Oral reading fluency assessments evaluate the ability to read reference texts. However, even though the use of applications to collect the reading of the students avoids logistics costs and speeds up the process, the evaluation of recordings has become a challenging task. Therefore, this work presents a computational solution for large-scale precision-critical fluency assessment. The goal is to build an approach based on automatic speech recognition (ASR) for the automatic evaluation of the oral reading fluency of children and reduce hiring costs as much as possible.

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