COMPUTERIZED WRITING ASSESSMENT:

2017 ◽  
pp. 138-146 ◽  
Author(s):  
William W. Ziegler
Keyword(s):  
Writing Assessment ◽  
Computerized Writing

Developing and Scoring an Innovative Computerized Writing Assessment

1997 ◽  
Vol 34 (1) ◽  
pp. 21-41 ◽  
Author(s):  
Tim Davey ◽  
Janet Godwin ◽  
David Mittelholtz
Keyword(s):  
Writing Assessment ◽  
Computerized Writing

scholarly journals The Validity of Human and Computerized Writing Assessment

2005 ◽  
Vol 49 (7) ◽  
pp. 759-763 ◽  
Author(s):  
Ronald Laurids Boring
Keyword(s):  
Latent Semantic Analysis ◽  
Writing Assessment ◽  
Semantic Analysis ◽  
Expert Knowledge ◽  
Pathfinder Networks ◽  
Essay Grading ◽  
Definition Of ◽  
Computerized Writing

This paper summarizes an experiment designed to assess the validity of essay grading between holistic and analytic human graders and a computerized grader based on latent semantic analysis. The validity of the grade was gauged by the extent to which the student's knowledge of the topic correlated with the grader's expert knowledge. To assess knowledge, Pathfinder networks were generated by the student essay writers, the holistic and analytic graders, and the computerized grader. It was found that the computer generated grades more closely matched the definition of valid grading than did human generated grades.

Development a Python-based Writing Assessment Practice Program

2019 ◽  
Vol 19 (12) ◽  
pp. 769-787
Author(s):  
Yeongmi Choi ◽  
Sumi Kim ◽  
So hyeon Lee ◽  
Youngmin Park
Keyword(s):  
Writing Assessment ◽  
Assessment Practice

General Models for Automated Essay Scoring: Exploring an Alternative to the Status Quo

2005 ◽  
Vol 33 (1) ◽  
pp. 101-113 ◽  
Author(s):  
P. Adam Kelly
Keyword(s):  
Empirical Evidence ◽  
Writing Assessment ◽  
Model Performance ◽  
Status Quo ◽  
Automated Essay Scoring ◽  
The Status ◽  
Essay Scoring ◽  
Better Than

Powers, Burstein, Chodorow, Fowles, and Kukich (2002) suggested that automated essay scoring (AES) may benefit from the use of “general” scoring models designed to score essays irrespective of the prompt for which an essay was written. They reasoned that such models may enhance score credibility by signifying that an AES system measures the same writing characteristics across all essays. They reported empirical evidence that general scoring models performed nearly as well in agreeing with human readers as did prompt-specific models, the “status quo” for most AES systems. In this study, general and prompt-specific models were again compared, but this time, general models performed as well as or better than prompt-specific models. Moreover, general models measured the same writing characteristics across all essays, while prompt-specific models measured writing characteristics idiosyncratic to the prompt. Further comparison of model performance across two different writing tasks and writing assessment programs bolstered the case for general models.

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