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Supporting Literacy Assessment in West Africa: Using State-of-the-Art Speech Models to Assess Oral Reading Fluency

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Title: Supporting Literacy Assessment in West Africa: Using State-of-the-Art Speech Models to Assess Oral Reading Fluency
Language: English
Authors: Owen Henkel (ORCID 0009-0001-8850-067X), Hannah Horne-Robinson, Libby Hills, Bill Roberts, Josh McGrane
Source: International Journal of Artificial Intelligence in Education. 2025 35(1):282-303.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 22
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: Foreign Countries, Oral Reading, Reading Fluency, Literacy, Error Patterns, Scores, Automation, Reading Tests, Educational Assessment, Auditory Perception
Geographic Terms: Ghana
DOI: 10.1007/s40593-024-00435-9
ISSN: 1560-4292
1560-4306
Abstract: This paper reports on a set of three recent experiments utilizing large-scale speech models to assess the oral reading fluency (ORF) of students in Ghana. While ORF is a well-established measure of foundational literacy, assessing it typically requires one-on-one sessions between a student and a trained rater, a process that is time-consuming and costly. Automating the assessment of ORF could support better literacy instruction, particularly in education contexts where formative assessment is uncommon due to large class sizes and limited resources. This research is among the first to examine the use of the most recent versions of large-scale speech models for ORF assessment in the Global South. We find that the best performing model, Whisper V2, with no additional fine-tuning, produces transcriptions of Ghanaian students reading aloud with a Word Error Rate of 10.3. When these transcriptions are used to produce fully automated ORF scores, they closely align with scores generated by expert human raters, with a correlation coefficient of 0.98. These results were achieved on a representative dataset (i.e., students with regional accents, recordings taken in actual classrooms), using a free and publicly available speech with no additional fine-tuning. This model's strong performance on real-world classroom data, combined with its accessibility and simplified implementation, suggests potential for scaling ORF assessment in lower-resource, linguistically diverse educational contexts.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1461166
Database: ERIC
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  Data: <searchLink fieldCode="AR" term="%22Owen+Henkel%22">Owen Henkel</searchLink> (ORCID <externalLink term="http://orcid.org/0009-0001-8850-067X">0009-0001-8850-067X</externalLink>)<br /><searchLink fieldCode="AR" term="%22Hannah+Horne-Robinson%22">Hannah Horne-Robinson</searchLink><br /><searchLink fieldCode="AR" term="%22Libby+Hills%22">Libby Hills</searchLink><br /><searchLink fieldCode="AR" term="%22Bill+Roberts%22">Bill Roberts</searchLink><br /><searchLink fieldCode="AR" term="%22Josh+McGrane%22">Josh McGrane</searchLink>
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  Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
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  Data: This paper reports on a set of three recent experiments utilizing large-scale speech models to assess the oral reading fluency (ORF) of students in Ghana. While ORF is a well-established measure of foundational literacy, assessing it typically requires one-on-one sessions between a student and a trained rater, a process that is time-consuming and costly. Automating the assessment of ORF could support better literacy instruction, particularly in education contexts where formative assessment is uncommon due to large class sizes and limited resources. This research is among the first to examine the use of the most recent versions of large-scale speech models for ORF assessment in the Global South. We find that the best performing model, Whisper V2, with no additional fine-tuning, produces transcriptions of Ghanaian students reading aloud with a Word Error Rate of 10.3. When these transcriptions are used to produce fully automated ORF scores, they closely align with scores generated by expert human raters, with a correlation coefficient of 0.98. These results were achieved on a representative dataset (i.e., students with regional accents, recordings taken in actual classrooms), using a free and publicly available speech with no additional fine-tuning. This model's strong performance on real-world classroom data, combined with its accessibility and simplified implementation, suggests potential for scaling ORF assessment in lower-resource, linguistically diverse educational contexts.
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