Developed through decades of research by leading language and cognitive scientists, Pearson’s automated scoring engines provide consistent, accurate, and timely feedback on automated tests. These technologies can be easily integrated into a variety of systems for scoring any amount of spoken or written language.
Knowledge Analysis Technologies™ (KAT)
The technology underlying Summary Street, Intelligent Essay Assessor (IEA), and WriteToLearn is based on the KAT engine, including Pearson’s unique implementation of Latent Semantic Analysis (LSA), an approach that is trained to measure the semantic similarity of words and passages by analyzing large bodies of relevant text. LSA can then closely approximate the degree of similarity of meaning between two texts as judged by human readers.
Intelligent Essay Assessor (IEA)
An Internet-based tool for automatically scoring the quality of electronically submitted essays, IEA uses Pearson’s state-of-the-art Knowledge Analysis Technologies™ (KAT) engine, which automatically evaluates the meaning of text, as well as grammar, style, and mechanics. What’s more, IEA can also evaluate short constructed responses. In tests with thousands of constructed responses, the Intelligent Essay Assessor has proven as reliable as professional human scorers.
Reading Maturity Metric (RMM)
In the past, the only way to automatically evaluate the reading level of a text was to measure the length of the words and sentences and the difficulty of the words used. These methods rely too much on correlative data rather than causative, and ignore the divergence in vocabulary and syntax that occurs as readers advance from elementary school through university level readings. Pearson’s RMM is able to model more accurately the way words are learned through reading. It does a better job of identifying the relative meaning between words in the text, providing a much more accurate measure of complexity for texts of all sizes and types. All of these calculations are done within seconds, allowing you to quickly choose the most appropriate level of text for your middle school classroom, your university level seminar, or your children at home.
The Versant testing system, based on the patented Versant technology, uses a speech processing system specifically designed to analyze speech in a manner that distinguishes between native and non-native speakers. In addition to recognizing words, the system also locates and evaluates relevant segments, syllables, and phrases in speech. Statistical modeling techniques are applied to assess the spoken performance. With an average of 0.97 correlation across Versant tests, the computer-generated Versant test scores are virtually indistinguishable from human scoring.