Complementary strengths? Evaluation of a hybrid human-machine scoring approach for a test of oral academic English

ABSTRACT Human raters and machine scoring systems potentially have complementary strengths in evaluating language ability; specifically, it has been suggested that automated systems might be used to make consistent measurements of specific linguistic phenomena, whilst humans evaluate more global aspects of performance. We report on an empirical study that explored the possibility of combining human and machine scores using responses from the speaking section of the TOEFL iBT® test. Human raters awarded scores for three sub-constructs: delivery, language use and topic development. The SpeechRaterSM automated scoring system produced scores for delivery and language use. Composite scores computed from three different combinations of human and automated analytic scores were equally or more reliable than human holistic scores, probably due to the inclusion of multiple observations in composite scores. However, composite scores calculated solely from human analytic scores showed the highest reliability and reliability steadily decreased as more machine scores replaced human scores.