Abstract: Systems and methods for rendering spatial audio in accordance with embodiments of the invention are illustrated. One embodiment includes a spatial audio system, including a primary network connected speaker, including a plurality of sets of drivers, where each set of drivers is oriented in a different direction, a processor system, memory containing an audio player application, wherein the audio player application configures the processor system to obtain an audio source stream from an audio source via the network interface, spatially encode the audio source, decode the spatially encoded audio source to obtain driver inputs for the individual drivers in the plurality of sets of drivers, where the driver inputs cause the drivers to generate directional audio.

Abstract: A system generates a training dataset based on historical consumption information and historical comprehension information of historical users, and uses the training dataset to train a machine-learned model to predict a measure of comprehension for a user consuming educational content. The system applies the machine-learned model to behaviors of a target user to determine a target measure of comprehension for target educational content, identifies one or more characteristics of the target educational content, applies a content identification model to identify supplemental educational content, and generates an educational content interface to present the supplemental educational content to the target user. In some examples, the system trains the machine-learned model to predict a collective measure of comprehension for a set of users consuming educational content, identifies a set of supplemental educational content, and generates a teacher interface to present the set of supplemental educational content.

Abstract: A system generates a training dataset based on historical consumption information and historical comprehension information of historical users, and uses the training dataset to train a machine-learned model to predict a measure of comprehension for a user consuming educational content. The system applies the machine-learned model to behaviors of a target user to determine a target measure of comprehension for target educational content, identifies one or more characteristics of the target educational content, applies a content identification model to identify supplemental educational content, and generates an educational content interface to present the supplemental educational content to the target user. In some examples, the system trains the machine-learned model to predict a collective measure of comprehension for a set of users consuming educational content, identifies a set of supplemental educational content, and generates a teacher interface to present the set of supplemental educational content.