Pssst⋯ Textual features⋯ There is more to automatic essay scoring than just you!

Scott Crossley; Laura K. Allen; Danielle McNamara; Erica L. Snow

doi:10.1145/2723576.2723595

Pssst⋯ Textual features⋯ There is more to automatic essay scoring than just you!

Scott Crossley, Laura K. Allen, Danielle McNamara, Erica L. Snow

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

15 Scopus citations

Abstract

This study investigates a new approach to automatically assessing essay quality that combines traditional approaches based on assessing textual features with new approaches that measure student attributes such as demographic information, standardized test scores, and survey results. The results demonstrate that combining both text features and student attributes leads to essay scoring models that are on par with state-of-the-art scoring models. Such findings expand our knowledge of textual and nontextual features that are predictive of writing success.

Original language	English (US)
Title of host publication	ACM International Conference Proceeding Series
Publisher	Association for Computing Machinery
Pages	203-207
Number of pages	5
Volume	16-20-March-2015
ISBN (Print)	9781450334174
DOIs	https://doi.org/10.1145/2723576.2723595
State	Published - Mar 16 2015
Event	5th International Conference on Learning Analytics and Knowledge, LAK 2015 - Poughkeepsie, United States Duration: Mar 16 2015 → Mar 20 2015

Other

Other	5th International Conference on Learning Analytics and Knowledge, LAK 2015
Country/Territory	United States
City	Poughkeepsie
Period	3/16/15 → 3/20/15

Keywords

Automatic essay scoring
Corpus linguistics
Data mining
Individual differences
Intelligent tutoring systems
Natural language processing

ASJC Scopus subject areas

Human-Computer Interaction
Computer Networks and Communications
Computer Vision and Pattern Recognition
Software

Access to Document

10.1145/2723576.2723595

Cite this

Crossley, S, Allen, LK, McNamara, D & Snow, EL 2015, Pssst⋯ Textual features⋯ There is more to automatic essay scoring than just you! in ACM International Conference Proceeding Series. vol. 16-20-March-2015, Association for Computing Machinery, pp. 203-207, 5th International Conference on Learning Analytics and Knowledge, LAK 2015, Poughkeepsie, United States, 3/16/15. https://doi.org/10.1145/2723576.2723595

@inproceedings{dba5073935c24659b44a9e98e9da6b0d,

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abstract = "This study investigates a new approach to automatically assessing essay quality that combines traditional approaches based on assessing textual features with new approaches that measure student attributes such as demographic information, standardized test scores, and survey results. The results demonstrate that combining both text features and student attributes leads to essay scoring models that are on par with state-of-the-art scoring models. Such findings expand our knowledge of textual and nontextual features that are predictive of writing success.",

keywords = "Automatic essay scoring, Corpus linguistics, Data mining, Individual differences, Intelligent tutoring systems, Natural language processing",

author = "Scott Crossley and Allen, {Laura K.} and Danielle McNamara and Snow, {Erica L.}",

year = "2015",

month = mar,

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doi = "10.1145/2723576.2723595",

language = "English (US)",

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note = "5th International Conference on Learning Analytics and Knowledge, LAK 2015 ; Conference date: 16-03-2015 Through 20-03-2015",

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AU - Snow, Erica L.

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Y1 - 2015/3/16

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AB - This study investigates a new approach to automatically assessing essay quality that combines traditional approaches based on assessing textual features with new approaches that measure student attributes such as demographic information, standardized test scores, and survey results. The results demonstrate that combining both text features and student attributes leads to essay scoring models that are on par with state-of-the-art scoring models. Such findings expand our knowledge of textual and nontextual features that are predictive of writing success.

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KW - Corpus linguistics

KW - Data mining

KW - Individual differences

KW - Intelligent tutoring systems

KW - Natural language processing

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DO - 10.1145/2723576.2723595

M3 - Conference contribution

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SN - 9781450334174

VL - 16-20-March-2015

SP - 203

EP - 207

BT - ACM International Conference Proceeding Series

PB - Association for Computing Machinery

T2 - 5th International Conference on Learning Analytics and Knowledge, LAK 2015

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ER -

Pssst⋯ Textual features⋯ There is more to automatic essay scoring than just you!

Abstract

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Keywords

ASJC Scopus subject areas

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