Electronic handwriting analysis through adaptive machine-learning
AssigneePearson Education, Inc.
InventorZhaodong Wang
Life of this patent
Estimating the term…
Abstract
An improved machine learning system is provided. For example, a content management server may provide a digital assessment of a user's handwriting to assess the user's knowledge of a language. The assessment may comprise adaptive technology to help determine initial questions to provide to the user as well as follow-up questions to clarify appropriate remediation content in a particular context. The content management server may also provide real-time analysis, including assessing multiple users at the same time in adjusting the assessment based on the digital input from each of these users. In some examples, the content management server may incorporate handwriting analysis methods to perform object detection and score handwriting input.
Claims20
A content management system for improving distribution and conversion of academic data, the content management system comprising:
- one or more processors;
- and
- one or memories coupled with the one or more processors, wherein the one or more processors and the one or more memories are configured to:
- receive, by the one or more processors, a training data set comprising at least a model test item and a model test response, wherein:
- the model test item being associated with a plurality of letters to spell a word associated with the model test item, and
- the model test response comprising a first digital representation of handwritten user input associated with the model test item;
- determine a model response score based on an analysis of the model test item;
- generate a model ground truth scoring method that correlates the model response score with the training data set;
- receive a second response, the second response comprising a second digital representation of handwritten user input associated with a second test item;
- analyze the second response using an automated handwriting assessment method and the model ground truth scoring method;
- and
- determine a second response score based on the analysis of the second response.
A computer-implemented method for rendering a markup language document, the method comprising:
- receiving a training data set comprising at least a model test item and a model test response, wherein:
- the model test item being associated with a plurality of letters to spell a word associated with the model test item, and
- the model test response comprising a first digital representation of handwritten user input associated with the model test item;
- determining a model response score based on an analysis of the model test item;
- generating a model ground truth scoring method that correlates the model response score with the training data set;
- receiving a second response, the second response comprising a second digital representation of handwritten user input associated with a second test item;
- analyzing the second response using an automated handwriting assessment method and the model ground truth scoring method;
- and
- determining a second response score based on the analysis of the second response.
One or more computer-readable storage media collectively storing computer-executable instructions that, when executed by one or more computer systems, configure the one or more computer systems to collectively perform operations comprising:
- receiving a training data set comprising at least a model test item and a model test response, wherein:
- the model test item being associated with a plurality of letters to spell a word associated with the model test item, and
- the model test response comprising a first digital representation of handwritten user input associated with the model test item;
- determining a model response score based on an analysis of the model test item;
- generating a model ground truth scoring method that correlates the model response score with the training data set;
- receiving a second response, the second response comprising a second digital representation of handwritten user input associated with a second test item;
- analyzing the second response using an automated handwriting assessment method and the model ground truth scoring method;
- and
- determining a second response score based on the analysis of the second response.
Dependencies are inferred from the claim text.