Ck12 Foundation

Neeru Khosla

Vinod Khosla

Reza Shahbazi

Miral Shah

Nimish Pachapurkar

Adhitya Venkatesh

A method includes receiving interaction data including more than one interaction type and providing the interaction data to a first machine learning model to generate a vector representation defining similarity measures between subsets of the interaction data and update a knowledge graph. The method includes retrieving data associated with a user based on a target associated with a prediction associated with a capability level the user and providing inputs to multiple machine learning models to define relative complexity scores, pseudoguessing weights, and an item discrimination index, which are provided to a transformer trained on the knowledge graph to generate at least one prediction. The method includes providing the at least one prediction to a machine learning model to generate a plurality of simulations. The method includes identifying, based on the plurality of simulations, a prediction associated with a capability level of the user.

Systems and methods for training machine learning models with user-specific knowledge graphs to predict outcomes

A method including receiving at least one input, detecting at least one parameter associated with a context, generating, by a machine learning model, a first set of data classes enriched with the context, determining if each data class is associated with a data class repository to define a subset of data classes not associated with a data class repository, and generating, by a machine learning model, at least one data class repository for each data class. The method includes generating a display signal to display information associated with at least one data class repository, the display signal associated with a graphical user interface, altering, using a machine learning model, the display signal by altering at least one portion of the graphical user interface associated with the context, and sending the display signal to display the at least one portion of the graphical user interface on the user device.

Systems and methods for machine learning to assess and increase user engagement from user inputs

In some embodiments a processor can receive inputs associated with a user and classify, based on a first machine learning model using at least one input rubric, each input from the inputs into an input type. The processor can define, based on the input type of each input, a first set of inputs associated with a first evaluation type and a second set of inputs associated with a second evaluation type. The processor can select a second machine learning model based on the first evaluation type and can extract, using the second machine learning model, from the first set of inputs a pattern associated with the user, evaluate a first state of the user based on the pattern and a second state of the user based on the second set of inputs, and generate an assessment of the user based on the first state and the second state.

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Portola Valley, CA, United States of America

Neeru Khosla