F. Jay Breyer

Comparison of e-rater® Automated Essay Scoring Model Calibration Methods Based on Distributional Targets

This article describes two separate, related studies that provide insight into the effectiveness of e-rater score calibration methods based on different distributional targets. In the first study, we developed and evaluated a new type of e-rater scoring model that was cost-effective and applicable under conditions of absent human rating and small candidate volume. This new model type, called the Scale Midpoint Model, outperformed an existing e-rater scoring model that is often adopted by certain e-rater system users without modification. In the second study, we examined the impact of three distributional score calibration approaches on existing models’ performance. These approaches included percentile calibrations on e-rater scores in accordance with a human rating distribution, normal distribution, and uniform distribution. Results indicated that these score calibration approaches did not have overall positive effects on the performance of existing e-rater scoring models.