- Enhanced usage of grammar features in our predictive models
- Ensemble methods for increased accuracy and reduced variability
- Elimination of length bias
- Additional predictors
- More uniform distribution of scores
- Incorporation of other Machine Learning and NLP techniques...
Although these changes represent an improvement in our AES technology, we recognize that classrooms as well as individuals may track changes in a score on a thesis or other written work over time, and that these changes could disrupt that process. To mitigate this issue and ease the transition, we are blending the scores from our previous AES model with scores generated using our new scoring models. As always, we welcome any feedback on the new scoring system.
We hope to continue with another round of major enhancements to the automated grader in the summer of 2016 when it will likely be less disruptive to most users of our service.