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US Patent 10354544 Predicting student proficiencies in knowledge components

Patent 10354544 was granted and assigned to Snapwiz on July, 2019 by the United States Patent and Trademark Office.

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Is a
Patent
Patent

Patent attributes

Patent Applicant
Snapwiz
Snapwiz
Current Assignee
Snapwiz
Snapwiz
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10354544
Date of Patent
July 16, 2019
Patent Application Number
15048640
Date Filed
February 19, 2016
Patent Citations Received
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US Patent 12112653 Systems and methods for providing tailored educational materials
0
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US Patent 11030919 Measuring language learning using standardized score scales and adaptive assessment engines
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US Patent 11056015 Systems and methods for providing tailored educational materials
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US Patent 11475329 Systems and methods for adaptive training of a machine learning system processing textual data
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US Patent 10885024 Mapping data resources to requested objectives
Patent Primary Examiner
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Reginald A Renwick
Patent abstract

Embodiments of the present invention allow an online course to predict proficiency of knowledge components using student responses to assessment items. In some embodiments, assessment items are tagged with metadata, such as associated knowledge component, difficulty, and depth of knowledge. Initially the author or subject matter expert supplies these values. Students then answer the assessment items. During an assessment, the student responses are automatically evaluated for correctness. Each student response receives a score between 0 and 1; where 1 corresponds to a completely correct response and 0 an incorrect response. Student proficiency can be predicted at any time using the student's up-to-date assessment item response history. Some embodiments use Samejima's Continuous Response Model (CRM), which is a special case of an Item Response Theory (IRT). Question metadata such as question difficulty, difficulty of knowledge component, impact of learning gain and retention is used to predict the most recent proficiency.

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