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Related Experiment Videos

Natural language processing in an intelligent writing strategy tutoring system.

Danielle S McNamara¹, Scott A Crossley, Rod Roscoe

¹Learning Sciences Institute, Arizona State University, Phoenix, AZ, USA. dsmcnamara1@gmail.com

Behavior Research Methods

|October 12, 2012

Summary

This study shows that computational indices can predict essay quality. New indices for rhetoric and cohesion significantly improved prediction accuracy, enabling more meaningful feedback for writing tutoring systems.

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Area of Science:

Natural Language Processing
Artificial Intelligence
Educational Technology

Background:

Intelligent tutoring systems like The Writing Pal use AI and NLP to assess essay quality.
Developing accurate AI for essay assessment requires considering linguistic, rhetorical, and contextual features due to writing's subjective nature.

Purpose of the Study:

To assess the potential of computational indices to predict human ratings of essay quality.
To expand upon prior research by including a larger dataset and a wider range of indices.

Main Methods:

Evaluated three regression models to predict human essay quality scores.
Assessed indices of lexical diversity, word frequency, syntactic complexity, cohesion, rhetoric, and reading ease.
Compared model performance using variance explained and adjacent accuracy.

Main Results:

A model with lexical, word frequency, and syntactic indices explained only 6% of the variance.
A model incorporating prior indices explained 38% of variance with 91% adjacent accuracy.
A model including new rhetorical and cohesion indices explained 44% of variance with 94% adjacent accuracy.

Conclusions:

Expanded computational indices, particularly those related to rhetoric and cohesion, significantly enhance the accuracy of predicting human essay quality ratings.
These improved indices offer a pathway to providing more nuanced and effective feedback within intelligent writing tutoring systems.