Abstract: In some embodiments, a method receives data for a user from an application that is used for an intervention. The data is analyzed to determine unstructured data and structured data. The method determines a context from the structured data to transform text of the unstructured data to transformed unstructured data. The transformed unstructured data is analyzed via a first model to generate a first prediction fit score for an intervention modality. The first model is configured to analyze text of the unstructured data to determine a fit with the intervention modality. The structured data is analyzed via a second model to generate a second prediction fit score for the intervention modality. The second model is configured to analyze numerical values for features of the structured data to determine the fit with the intervention modality. The method combines the first prediction fit score and the second prediction fit score.

Abstract: A multi-stage refinement process that utilizes a filtering engine, various subsystems, and artificial intelligence/machine learning (AI/ML) processes is deployed to identify a unique set of candidate-coaches for presentation to a user-member. A coach application may be instantiated on a user computing device that interoperates with a remote intelligent determination system. The determination system receives user data from queries presented to the user and/or remote third-party systems during a retrieval process. Such received data is encoded and ultimately utilized to generate a vector index that is then fed into a filtering engine with a number of subsystems. The subsystems include any one or more of user-defined policies and criteria, NLP (natural language processing), and ML/AI subsystems, among other subsystems, during a candidate generation process. A ranking and ordering process is then utilized to further refine the results before presentation to the user.

Abstract: On-line course offerings can be made available to users using computational techniques that reliably authenticate the identity of individual student users during the course of the very submissions and/or participation that will establish student user proficiency with course content. Authentication methods and systems include applications of behavioral biometrics.

Abstract: Conventional techniques for automatically evaluating and grading assignments are generally ill-suited to evaluation of coursework submitted in media-rich form. For courses whose subject includes programming, signal processing or other functionally expressed designs that operate on, or are used to produce media content, conventional techniques are also ill-suited. It has been discovered that media-rich, indeed even expressive, content can be accommodated as, or as derivatives of, coursework submissions using feature extraction and machine learning techniques. Accordingly, in on-line course offerings, even large numbers of students and student submissions may be accommodated in a scalable and uniform grading or scoring scheme. Instructors or curriculum designers may adaptively refine assignments or testing based on classifier feedback.

Abstract: Conventional techniques for automatically evaluating and grading assignments are generally ill-suited to evaluation of coursework submitted in media-rich form. For courses whose subject includes programming, signal processing or other functionally expressed designs that operate on, or are used to produce media content, conventional techniques are also ill-suited. It has been discovered that media-rich, indeed even expressive, content can be accommodated as, or as derivatives of, coursework submissions using feature extraction and machine learning techniques. Accordingly, in on-line course offerings, even large numbers of students and student submissions may be accommodated in a scalable and uniform grading or scoring scheme. Instructors or curriculum designers may adaptively refine assignments or testing based on classifier feedback.

Abstract: On-line course offerings can be made available to users using computational techniques that reliably authenticate the identity of individual student users during the course of the very submissions and/or participation that will establish student user proficiency with course content. Authentication methods and systems include applications of behavioral biometrics.

Abstract: Conventional techniques for automatically evaluating and grading assignments are generally ill-suited to evaluation of coursework submitted in media-rich form. For courses whose subject includes programming, signal processing or other functionally expressed designs that operate on, or are used to produce media content, conventional techniques are also ill-suited. It has been discovered that media-rich, indeed even expressive, content can be accommodated as, or as derivatives of, coursework submissions using feature extraction and machine learning techniques. Accordingly, in on-line course offerings, even large numbers of students and student submissions may be accommodated in a scalable and uniform grading or scoring scheme. Instructors or curriculum designers may adaptively refine assignments or testing based on classifier feedback.

Abstract: Conventional techniques for automatically evaluating and grading assignments are generally ill-suited to evaluation of coursework submitted in media-rich form. For courses whose subject includes programming, signal processing or other functionally expressed designs that operate on, or are used to produce media content, conventional techniques are also ill-suited. It has been discovered that media-rich, indeed even expressive, content can be accommodated as, or as derivatives of, coursework submissions using feature extraction and machine learning techniques. Accordingly, in on-line course offerings, even large numbers of students and student submissions may be accommodated in a scalable and uniform grading or scoring scheme. Instructors or curriculum designers may adaptively refine assignments or testing based on classifier feedback.