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Direct linkShermis, Mark D.; Lottridge, Sue; Mayfield, Elijah – Journal of Educational Measurement, 2015
This study investigated the impact of anonymizing text on predicted scores made by two kinds of automated scoring engines: one that incorporates elements of natural language processing (NLP) and one that does not. Eight data sets (N = 22,029) were used to form both training and test sets in which the scoring engines had access to both text and…
Descriptors: Scoring, Essays, Computer Assisted Testing, Natural Language Processing
