论文信息 - Using Speech Recognition to Evaluate Two Student Models for a Reading Tutor

Using Speech Recognition to Evaluate Two Student Models for a Reading Tutor

Intelligent Tutoring Systems derive much of their power from having a student model that describes the learner’s competencies. However, constructing a student model is challenging for computer tutors that use automated speech recognition (ASR) as input, due to inherent inaccuracies in ASR. We describe two extremely simplified models of developing word decoding skills and explore whether there is sufficient information in ASR output to determine which model fits student performance better, and under what circumstances one model is preferable

Jack Mostow | Kai-min Chang | Joseph Beck | Albert Corbett

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