Short answer grading: When sorting helps and when it doesn’t

Ulrike Pado
HFT Stuttgart, Stuttgart, Germany

Cornelia Kiefer
HFT Stuttgart, Stuttgart, Germany

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Ingår i: Proceedings of the 4th workshop on NLP for Computer Assisted Language Learning at NODALIDA 2015, Vilnius, 11th May, 2015

Linköping Electronic Conference Proceedings 114:6, s. 42-50

NEALT Proceedings Series 26:6, s. 42-50

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Publicerad: 2015-05-06

ISBN: 978-91-7519-036-5

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


Automatic short-answer grading promises improved student feedback at reduced teacher effort both during and after instruction. Automated grading is, however, controversial in high-stakes testing and complex systems can be difficult to set up by non-experts, especially for frequently changing questions. We propose a versatile, domain-independent system that assists manual grading by pre-sorting answers according to their similarity to a reference answer. We show near state-of-the-art performance on the task of automatically grading the answers from CREG (Meurers et al., 2011). To evaluate the grader assistance task, we present CSSAG (Computer Science Short Answers in German), a new corpus of German computer science questions answered by natives and highly-proficient non-natives. On this corpus, we demonstrate the positive influence of answer sorting on the slowest-graded, most complex-to-assess questions.


short-answer grading; assisted grading; short-answer corpora


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