Systems and methods for assessment item credit assignment based on predictive modelling
US11443647B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 10, 2020 |
| Grant date | Sep 13, 2022 |
| Priority date | — |
| Expiry date | Apr 14, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.