International Business Machines Corporation

Sesame Workshop

Emily Reardon

Tamer E Abuelsaad

Satyanarayana Venkata Nitta

Prasenjit Dey

James P Gray

Miles Ludwig

Ravindranath Kokku

Aditya Sinha

Jodi M Cutler

Michelle Newman-Kaplan

Allison C Allain

Nick Bartzokas

Ravi Kokku

A combination of knowledge representation and retrieval, assessments and personalization for a tutoring platform that provides an automated phased word exposition method for vocabulary learning and enable learners to learn words (or any learning objective) with sufficient proficiency and without excessive repetition. The phased learning objective method implements a moving average model that models the learner across multiple dimensions (e.g. reading, listening, writing, speaking) such that the platform is confident of the learner's understanding, while using minimal assessment doses, and such that the understanding transfers across experiences. The method implemented provides for multiple learning and assessment activities for a learning objective and leverages the same learner model for each learning dimension. Each word of a word set passes through learning and assessment-only phases for statistically significant evidence of learning. The model derived from the phased word expansion can be shared across applications, toys and tangibles that learners interact with.

Phased word expansion for vocabulary learning

Personalized video interjections based on a learner model and a learning objective. A method for adding interjections to a video includes analyzing the content of a plurality of videos based on a set of learning objectives, selecting a video based on a learning objective, determining types of video interjections using an analytics engine that compares a learner model and the learning objective, determining a location for the video interjections using the analytics engine, generating a video interjection for each video interjection type and inserting the video interjections into the video at the determined locations.

Personalized video interjections based on learner model and learning objective

Personalized video interjections based on a learner model and a learning objective. A method for adding interjections to a video includes analyzing the content of a plurality of videos based on a set of learning objectives, generating and storing educational content metadata on a timeline for the videos, selecting a video based on a user specific learning objective, determining types of video interjections using an analytics engine that compares a user learner model, the user learning objective and the metadata, determining a location for the video interjections using the analytics engine that compares the user learner model, the learning objective, the metadata and timeline, generating a video interjection for each video interjection type and inserting the video interjections into the video at the determined locations.

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Brooklyn, NY, United States of America

Emily Reardon