Reliability-Based Feature Weighting for Automated Essay Scoring

Abstract

From their earliest origins, automated essay scoring systems strived to emulate human essay scores and viewed them as their ultimate validity criterion. Consequently, the importance (or weight) and even identity of computed essay features in the composite machine score were determined by statistical techniques that sought to optimally predict human scores from essay features. However, it is evident that machine evaluation of essays is fundamentally different from human evaluation and therefore is not likely to measure the same set of writing skills. As a consequence, feature weights of human-prediction machine scores (reflecting their importance in the composite scores) are bound to reflect statistical artifacts. This article suggests alternative feature weighting schemes based on the premise of maximizing reliability and internal consistency of the composite score. The article shows, in the context of a large-scale writing assessment, that these alternative weighting schemes are significantly different from human-prediction weights and give rise to comparable or even superior reliability and validity coefficients.