Year: 2015
Type: conference-paper
Abstract: Automatic evaluation of written responses to content-focused assessment items (automated short answer scoring) is a challenging educational application of natural language processing.It is often addressed using supervised machine learning by estimating models to predict human scores from detailed linguistic features such as word n-grams.However, training data (i.e., human-scored responses) can be difficult to acquire.In this paper, we conduct experiments using scored responses to 44 prompts from 5 diverse datasets in order to better understand how training set size and other factors relate to system performance.We believe this will help future researchers and practitioners working on short answer scoring to answer practically important questions such as, "How much training data do I need?"
DOI: 10.3115/v1/w15-0610
Authors: Michael Heilman, Nitin Madnani
Institution: Educational Testing Service
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
OpenAlex ID: W2250817048