Based on the interactionalist speaking construct,this study validates the human-machine collaboration in rating open-ended speaking tasks with an automated scoring system for the College English Test-Spoken English Test,in which test takers'performances are scored both analytically and holistically on rating scales at the task level.The findings show that the automated scoring system gives scores in relation to task features and test purposes,demonstrating a high level of rating accuracy.In the mode of human-machine collaboration,quite a large portion of the score variance could be attributed to the speaking abilities related factors deemed essential for task completion.When applying automated scoring to the large-scale rating of speaking tests,it is suggested that the task-based analytic scoring be used in setting gold standards for machine learning,and the task-based holistic scoring be adopted for human-machine collaboration in large-scale rating sessions,in order to facilitate score interpretation and ensure rating efficiency.
关键词
大学英语四、六级考试/口语考试/自动评分/交互构念理论
Key words
College English Test/speaking test/automated scoring/interactionalist construct theory
维普
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