Answer:
This study explores the potential for automated indices related to speech delivery,
language use, and topic development to model human judgments of TOEFL speaking
proficiency in second language (L2) speech samples. For this study, 244 transcribed
TOEFL speech samples taken from 244 L2 learners were analyzed using automated
indices taken from Coh-Metrix, CPIDR, and LIWC. A stepwise linear regression was used
to explain the variance in human judgments of independent speaking ability and overall
speaking proficiency. Automated indices related to word type counts, causal cohesion, and
lexical diversity predicted 52% of the variance in human ratings for the independent
speech samples. Automated indices related to word type counts and word frequency
predicted 61% of the variance of the human scores of overall speaking proficiency. These
analyses demonstrate that, even in the absence of indices related to pronunciation and
prosody (e.g., phonological accuracy, intonation, and stress), automated indices related to
vocabulary size, causality, and word frequency can predict a significant amount of the
variance in human ratings of speaking proficiency. These findings have important
implications for understanding the construct of speaking proficiency and for the
development of automatic scoring techniques.
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Answers & Comments
Answer:
This study explores the potential for automated indices related to speech delivery,
language use, and topic development to model human judgments of TOEFL speaking
proficiency in second language (L2) speech samples. For this study, 244 transcribed
TOEFL speech samples taken from 244 L2 learners were analyzed using automated
indices taken from Coh-Metrix, CPIDR, and LIWC. A stepwise linear regression was used
to explain the variance in human judgments of independent speaking ability and overall
speaking proficiency. Automated indices related to word type counts, causal cohesion, and
lexical diversity predicted 52% of the variance in human ratings for the independent
speech samples. Automated indices related to word type counts and word frequency
predicted 61% of the variance of the human scores of overall speaking proficiency. These
analyses demonstrate that, even in the absence of indices related to pronunciation and
prosody (e.g., phonological accuracy, intonation, and stress), automated indices related to
vocabulary size, causality, and word frequency can predict a significant amount of the
variance in human ratings of speaking proficiency. These findings have important
implications for understanding the construct of speaking proficiency and for the
development of automatic scoring techniques.