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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
Shermis, Mark D.; Mao, Liyang; Mulholland, Matthew; Kieftenbeld, Vincent – International Journal of Testing, 2017
This study uses the feature sets employed by two automated scoring engines to determine if a "linguistic profile" could be formulated that would help identify items that are likely to exhibit differential item functioning (DIF) based on linguistic features. Sixteen items were administered to 1200 students where demographic information…
Descriptors: Computer Assisted Testing, Scoring, Hypothesis Testing, Essays
