Zhang, Haoran
(2021)
Exploring Automated Essay Scoring Models for Multiple Corpora and Topical Component Extraction from Student Essays.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
Since it is a widely accepted notion that human essay grading is labor-intensive, automatic scoring method has drawn more attention. It reduces reliance on human effort and subjectivity over time and has commercial benefits for standardized aptitude tests. Automated essay scoring could be defined as a method for grading student essays, which is based on high inter-agreement with human grader, if they exist, and requires no human effort during the process. This research mainly focuses on improving existing Automated Essay Scoring (AES) models with different technologies. We present three different scoring models for grading two corpora: the Response to Text Assessment (RTA) and the Automated Student Assessment Prize (ASAP). First of all, a traditional machine learning model that extracts features based on semantic similarity measurement is employed for grading the RTA task. Secondly, a neural network model with the co-attention mechanism is used for grading sourced-based writing tasks. Thirdly, we propose a hybrid model integrating the neural network model with hand-crafted features. Experiments show that the feature-based model outperforms its baseline, but a stand-alone neural network model significantly outperforms the feature-based model. Additionally, a hybrid model integrating the neural network model and hand-crafted features outperforms its baselines, especially in a cross-prompt experimental setting. Besides, we present two investigations of using the intermediate output of the neural network model for keywords and key phrases extraction from student essays and the source article. Experiments show that keywords and key phrases extracted by our models support the feature-based AES model, and human effort can be relieved by using automated essay quality signals during the training process.
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Details
| Item Type: |
University of Pittsburgh ETD
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| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
3 May 2021 |
| Date Type: |
Publication |
| Defense Date: |
2 December 2020 |
| Approval Date: |
3 May 2021 |
| Submission Date: |
1 March 2021 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
175 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
Dietrich School of Arts and Sciences > Computer Science |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Automated Essay Scoring, Automated Writing Evaluation, Information Extraction, Natural Language Processing, Topical Components |
| Date Deposited: |
03 May 2021 14:54 |
| Last Modified: |
03 May 2021 14:54 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/40299 |
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