Systems and methods for automated aggregated content commenting are disclosed herein. Such a system can include a memory including a content database; an evaluation criteria database; and a comment database. The system can include at least one server. The at least one server can: receive a content identifier; receive a user identifier; provide a representation of the piece of content to the user; receive a comment from the user identifying a portion of the piece of content; generate a portion of the comment database linked to the user and the content; generate an entry in the portion of the comment database linked to the user and the content; and store the comment in the entry in the portion of the comment database.

A network learning system includes a host being coupled to a first user end and a second user end. A processor of the host end is configured to perform the following steps of: storing original lesson information being sent from the first user end in which the original lesson information has a plurality of content sections; selecting the content sections to form adaptive lesson information based on a learning condition of the second user end; sending the adaptive lesson information to the second user end so that an output device of the second user end outputs the adaptive lesson information based on a view of a simulated space; when the output device of the second user end outputs the adaptive lesson information, recording a value from an input device of the second user end.

Producing media content such as films, television shows, commercials, and other formats requires aggregating a large number of resources before, during, and after filming. A method of production scheduling that directly connects to real-time accessible marketplace resources (labor, equipment, services) to help producers find and hire the equipment, creative services, and human capital needed to produce a film or other audiovisual media is disclosed. This method helps producers schedule and book the resources they need to produce content. The resources are tied to a smart timeline that synchronizes production schedule changes against available resources. The method also alerts suppliers and producers of any changes to the production timeline.

Systems and methods for feature-based alert triggering are disclosed herein. The system can include memory including a model database containing a machine-learning algorithm. The system can include a user device that can receive inputs from a user; and at least one server. The at least one server can: receive electrical signals from the user device, the electrical signals corresponding to a plurality of user inputs provided to the user device; automatically generate input-based features from the received electrical signals; input the input-based features into the machine-learning algorithm; automatically and directly generate a risk prediction with the machine-learning algorithm from the input-based features; and generate and display an alert when the risk prediction exceeds a threshold value.

A method includes a computing device of a computing infrastructure interpreting a request from a user computing device of the computing infrastructure to cause a license of a set of learning objects pertaining to a common topic for use by the user computing device to produce licensee information for the set of learning objects. The method further includes identifying a non-fungible token (NFT) associated with the set of learning objects and establishing, with the user computing device, agreed licensing terms utilizing the licensee information and based on available licensing terms of a smart contract for the set of learning objects. The method further includes generating a license smart contract for the set of learning objects to include the licensee information and the agreed licensing terms and causing generation of a license block affiliated with the NFT via a blockchain of the object distributed ledger.

Method, systems, and computer-readable media for receiving, from an application instance operating on a client device, education information that indicates a client device identifier, a wireless proximity beacon identifier, and a proximity of the identified client device to the identified wireless proximity beacon. From the received education information, a determination is made whether the proximity of the identified client device to the identified wireless proximity beacon satisfies a threshold proximity. Based at least on the determination, an action is determined that the application instance operating on the identified client device is permitted to perform while the proximity of the identified client device to the identified wireless proximity beacon satisfies the threshold proximity. Education information is transmitted to the identified client device that enables the application instance operating on the identified client device to perform the action.

An automated marketing and training regimen for certifying instructors and allowing same to create a microbusiness via a microsite and a technology software platform for implementing and supporting same.

Embodiments are described of systems and methods for the creation, transmittal, review of, and related operations on, as well as the prevention, detection, and such, of unauthorized manipulation (e.g., substitution) of, high-value data files, including electronic documents.

This invention concerns methods and systems for monitoring one or more students' use of computers, measuring, analyzing, and summarizing in statistical and pedagogical ways learning quality, on-task versus off-taskness, making diagnostic and prescriptive recommendations, and displaying relevant and configurable results in real time and historically in meaningful ways to students, teachers, and various educationalists and managers.

Systems and methods are described which relate to machine learning model validation. A first machine learning model may be trained to dependent variable data for a first population. A second machine learning model may be trained to simulate dependent variable data for the first population. The second machine learning model may then be applied to student activity data of a second population having different characteristics from the first population to produce simulated dependent variable data. The first machine learning model may then generate predictions for the second population, which may be validated via comparison to the simulated dependent variable data. A given simulated dependent variable value may be generated by the second machine learning model at a specific time T.sub.X, where some features input to the machine learning model may be derived from datapoints occurring before T.sub.X and others being derived from datapoints occurring after T.sub.X.