In language (and content) instruction, free-text questions are important instruments for gauging student ability. Grading is often done manually, so that frequent testing means high teacher workloads. We propose a new strategy for supporting manual graders: We carefully analyse the performance of automated graders individually and as a grader ensemble and present a procedure to guide manual effort and to estimate the size of the remaining grading error. We evaluate our approach on a range of data sets to demonstrate its robustness.
Keywords: short-answer grading, machine grading, manual grading support
Proceedings of the 7th Workshop on NLP for Computer Assisted Language Learning (NLP4CALL 2018) at SLTC, Stockholm, 7th November 2018
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