Massachusetts Institute of Technology

Microsoft Technology Licensing, LLC

The General Hospital Corporation

Demba Ba

Emery Neal Brown

Cha Zhang

Dinei A Florencio

Seong-Eun Kim

Michael Behr

Anne Caroline Smith

The present disclosure features systems and methods for analyzing student learning and calibrating the difficulty of questions on a test or examination. In one embodiment, a method for analyzing the learning of a student includes administering, by an assessment agent, a task to a student, the task comprising a question having an associated difficulty. The assessment agent receives a response to the question from the student and evaluates the response to generate an observable, the observable comprising information related to the response. A posterior determination of the student's ability is then calculated by incorporating the observable into an ability model associated with the student, and the posterior determination of ability may be compared with the difficulty of the question, a skill acquisition probability, or other measure. The student's response, or a plurality of responses from students within a cohort, may be used to determine the difficulty of each question.

System and method for real-time analysis and guidance of learning

Systems and methods for analyzing electrophysiological signals acquired from a subject are provided. In some aspects, a method includes receiving electrophysiological signals acquired from a subject using one or more sensors, and assembling a set of time-series data using the acquired electrophysiological signals. The method also includes analyzing the set of time-series data using a state-space multi-taper framework to generate spectral information describing the electrophysiological signals, and determining a brain state of the subject using the spectral information. The method further includes generating a report indicative of the determined brain state.

Systems and methods for analyzing electrophysiological data from patients undergoing medical treatments

A novel enhanced beamforming technique that improves beamforming operations by incorporating a model for the directional gains of the sensors, such as microphones, and provides means of estimating these gains. The technique forms estimates of the relative magnitude responses of the sensors (e.g., microphones) based on the data received at the array and includes those in the beamforming computations.

Enhanced beamforming for arrays of directional microphones

Growing community of inventors

Cambridge, MA, United States of America

Demba Ba