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Identifying Patterns For Short Answer Scoring
Using Graph-based Lexico…
Identifying Patterns For Short Answer Scoring
Using Graph-based Lexico-Semantic Text Matching
Ramachandran et al 2015
Features
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top-scoring student responses, prompt and stimulus text
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Model (supervised)
Tandalla’s Approach: two Random Forests and two Gradient Boosting Machines; regression problem on Kaggle Short Answer Dataset
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We use a Random Forest regressor as the learner
to build models. The learner is trained on the average of the human grades. We stack results from models created with each type of pattern to compute final results. (Mohler 2011 CS dataset)
Results
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Mohler et al. (2011)’s Short Answer Dataset (Pearson = 0.52, RMSE = 0.98, Md(RMSE) = 0.86)
On questions
Pearson = 0.61, RMSE = 0.88, Md(RMSE) = 0.02
On assigments
Pearson = 0.61, RMSE = 0.86, Md(RMSE) = 0.77
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