To Aggregate or Not? : Linguistic Features in Automatic Essay Scoring and Feedback Systems / Scott A. Crossley, Kristopher Kyle and Danielle S. McNamara.
This study investigates the relative efficacy of using linguistic micro-features, the aggregation of such features, and a combination of micro-features and aggregated features in developing automatic essay scoring (AES) models. Although the use of aggregated features is widespread in AES systems (e....
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| Main Authors: | , , |
| Format: | eBook |
| Language: | English |
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2015.
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| Summary: | This study investigates the relative efficacy of using linguistic micro-features, the aggregation of such features, and a combination of micro-features and aggregated features in developing automatic essay scoring (AES) models. Although the use of aggregated features is widespread in AES systems (e.g., e-rater; Intellimetric), very little published data exists that demonstrates the superiority of using such a method over the use of linguistic micro-features or combination of both micro-features and aggregated features. The results of this study indicate that AES models comprised of micro-features and a combination of micro-features and aggregated features outperform AES models comprised of aggregated features alone. The results also indicate that that AES models based on micro-features and a combination of micro-features and aggregated features provide a greater variety of features with which to provide formative feedback to writers. These results have implications for the development of AES systems and for providing automatic feedback to writers within these systems. |
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| Item Description: | Sponsoring Agency: Institute of Education Sciences (ED). Contract Number: R305A080589. Contract Number: R305G020018. Abstractor: As Provided. |
| Physical Description: | 1 online resource (19 pages) |
| Type of Computer File or Data Note: | Text (Journal Articles) Text (Reports, Research) |
| Preferred Citation of Described Materials Note: | Grantee Submission, Journal of Writing Assessment v8 n1 2015. |