Powergrading: a Clustering Approach to Amplify Human Effort for Short Answer Grading
Sumit Basu,Charles E. Jacobs,Lucy Vanderwende +2 more
- Vol. 1, pp 391-402
TLDR
This paper used a similarity metric between student responses, and then used this metric to group responses into clusters and subclusters, which allowed teachers to grade multiple responses with a single action, provide rich feedback to groups of similar answers, and discover modalities of misunderstanding among students.Abstract:
We introduce a new approach to the machine-assisted grading of short answer questions. We follow past work in automated grading by first training a similarity metric between student responses, but then go on to use this metric to group responses into clusters and subclusters. The resulting groupings allow teachers to grade multiple responses with a single action, provide rich feedback to groups of similar answers, and discover modalities of misunderstanding among students; we refer to this amplification of grader effort as “powergrading.” We develop the means to further reduce teacher effort by automatically performing actions when an answer key is available. We show results in terms of grading progress with a small “budget” of human actions, both from our method and an LDA-based approach, on a test corpus of 10 questions answered by 698 respondents.read more
Citations
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Automatic Chinese Reading Comprehension Grading by LSTM with Knowledge Adaptation
TL;DR: An algorithm for automatic grading of open-ended Chinese reading comprehension questions is presented, utilizing long-short term memory recurrent neural network to extract semantic feature in student answers automatically and imposing the knowledge adaptation from web corpus to student answers.
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Spoken English Grading: Machine Learning with Crowd Intelligence
TL;DR: This paper addresses the problem of grading spontaneous speech using a combination of machine learning and crowdsourcing and proposes a framework that combines machine learning with crowdsourcing that rivals that of expert agreement.
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Learning to Grade Short Answers using Machine Learning Techniques
R. Krithika,Jayasree Narayanan +1 more
TL;DR: This work has devised novel techniques to apply the concept of Random Projection for grading 150 algorithmic answers on a coding question using the authors' own domain specific corpus which gives precise classification of right and wrong answers.
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