Melissa R. Hunte, Samantha McCormick, Maitree Shah, Clarissa Lau, E. Jang
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引用次数: 1
Abstract
ABSTRACT Children’s oral language proficiency (OLP) is integral for developing literacy skills. Storytelling or retelling is often used by parents and educators to elicit children’s OLP, yet it is less commonly used for assessment purposes. Leveraged by natural language processing and machine learning, this study examined the extent to which computational linguistic and acoustic indices predict human ratings of children’s (n=184 aged 9 to 11) OLP using two story retell stimuli presented in written and aural forms. Human raters scored children’s OLP on five oral proficiency criteria: vocabulary, grammar, idea development, task-fulfilment, and speech delivery, using a 4-point scale, and linguistic and acoustic features were used to predict each criterion. Results showed the efficacy of automated indices to predict human scores of children’s OLP. This study calls for attention to discrepancies in human and machine speech delivery scores and stimulus effects on story retelling performance among children of different language backgrounds.
期刊介绍:
Recent decades have witnessed significant developments in the field of educational assessment. New approaches to the assessment of student achievement have been complemented by the increasing prominence of educational assessment as a policy issue. In particular, there has been a growth of interest in modes of assessment that promote, as well as measure, standards and quality. These have profound implications for individual learners, institutions and the educational system itself. Assessment in Education provides a focus for scholarly output in the field of assessment. The journal is explicitly international in focus and encourages contributions from a wide range of assessment systems and cultures. The journal''s intention is to explore both commonalities and differences in policy and practice.