Pearson Education, Inc.

Mark E Liedtke

Victoria Kortan

Daniel L Ensign

Srirama Kolla

Sumona J Routh

Clayton Tong

Systems and methods are provided by which a machine learning model may be executed to determine the probability that a given user will respond correctly to a given assessment item of a digital assessment on their first attempt. The machine learning model may process feature data corresponding to the user and the assessment item in order to determine the probability. The feature data may be calculated periodically and/or in real time or near-real time according to a machine learning model definition based on assessment data corresponding to the user's activity and/or based on responses submitted by all users to the assessment item and/or to content related to the assessment item.

Systems and methods for predictive modelling of digital assessment performance

Systems and methods are provided by which an adaptive learning engine may be executed to determine the probability that a given user will respond correctly to a given assessment item of a digital assessment on their first attempt. The adaptive learning engine may apply one or more machine learning models to feature data corresponding to the user and the assessment item in order to determine the probability. The feature data may be calculated periodically and/or in real time or near-real time according to a machine learning model definition based on assessment data corresponding to the user's activity and/or based on responses submitted globally by users to the assessment item and/or to content related to the assessment item. Based on the correct first attempt probability, the adaptive learning engine may identify and recommend assessment items for which a user should be preemptively assigned credit.

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Castle Rock, CO, United States of America

Mark E Liedtke