Work

Towards Evaluating Narrative Quality In Student Writing

Year: 2018

Type: article

Abstract: This work lays the foundation for automated assessments of narrative quality in student writing. We first manually score essays for narrative-relevant traits and sub-traits, and measure inter-annotator agreement. We then explore linguistic features that are indicative of good narrative writing and use them to build an automated scoring system. Experiments show that our features are more effective in scoring specific aspects of narrative quality than a state-of-the-art feature set.

Source: Transactions of the Association for Computational Linguistics

DOI: 10.1162/tacl_a_00007

Authors: Swapna Somasundaran, Michael Flor, Martin Chodorow, Hillary Molloy, Binod Gyawali, Laura McCulla

Institutions: Educational Testing Service, The Graduate Center, CUNY, Hunter College

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Quality education

Open Access status: diamond

OpenAlex ID: W2787991549

FWCI: 1.331

Cites: 81

Cited by: 28

Related to: 10