Pearson Education, Inc.

Jay K Lynch

Reuben N Kelly

Systems and methods of the present invention provide for: generating a GUI comprising survey questions associated with degree factors and associated rating GUI components indicating application of the factor to a user; receiving the factor rating for each survey question; identifying a high factor rating exceeding a threshold; selecting a degree identifier sharing a common high factor rating between the first response and a response stored in the database; generating a candidate degree list including the degree identifier; generating a second GUI including the candidate degree list; and transmitting the second GUI to a client device.

User degree matching algorithm

A website may track online activities, such as assignments and/or assessment, of students taking online digital courses (courses). Courseware-level data and student-level data may be extracted from the tracked online activities and as well as student registration data. Institutional-level data may be generated from data regarding the institutions that teach the courses. Teacher-level data may be generated for the teachers teaching the courses. A teacher or student may request on a website an analysis of a course. Data for the course may be weighted in the courseware-level data. Data for the student(s), institution and/or teacher may also be weighted, depending on the desired analysis. A Bayesian multi-level model may generate a plurality of posterior distributions using the collected data. A prediction of a difficult subject matter may be determined from the plurality of posterior distributions and used to select a targeted remediation that may be performed on a website.

Performing a remediation based on a Bayesian multilevel model prediction

Systems and method for remote intervention are disclosed herein. The system can include a memory including: a user profile database; and a model database. The system can include a user device and a supervisor device, each of which including: a network interface; and an I/O subsystem. The system can include a content management server that can: receive data identifying a user of the user device; retrieve user data for the user from the user profile database; retrieve a risk model from the model database; generate a risk value based on the risk model; generate an action recommendation identifying an action for completion; and generate and send an alert to the supervisor device, which alert includes the action recommendation and includes computer code to trigger activation of the I/O subsystem of the supervisor device to provide the action recommendation.

Data-enabled success and progression system

Systems and methods can dynamically set user-specific schedules for prompting users to access educational content objects. An educational performance metric is identified for a user. A learning schedule is accessed that includes an identification of a plurality of learning episodes. Each of the plurality of learning episodes is associated with a time-related variable indicative of when the learning episode is to occur and a substance-related variable indicative of a learning substance for the learning episode. The learning schedule is adjusted based on the identified educational performance metric and associated with the user. A presentation time is identified based on a time-related variable in the learning schedule. An electronic content object associated with the presentation time is identified based on a substance-related variable the learning schedule. A notification with an offer to access the identified electronic content object is presented at the presentation time via a device of the user.

Dynamic and individualized scheduling engine for app-based learning

Systems and methods can track, at a group level, times for responding to educational questions. A set of users is identified as users enrolled in an academic course. A set of responses is accessed (e.g., after being received). Each response in the set of responses is one submitted via an electronic user device associated with a user in the set of users and is one submitted in response to one or more educational questions. For each response in the set of responses, an accuracy of the response is identified and a response time for the response is identified. The response time is indicative of a time between presentation of the one or more questions and submission the response. The identified response times for at least two responses in the set of responses are aggregated. A representation of the aggregation is caused to be presented.

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Arvada, CO, United States of America

Jay K Lynch