A group study system is described. The group study system allows for students to form study groups with other students in the same class or related classes by utilizing various technologies that make such a system possible. The system provides for an anonymous creation of the study sessions and maintains the anonymity until all students joining the study session participate in the study session. These sessions can be in person and virtual. Hybrid and virtual sessions are becoming more and more important with the increasing presence of online education. This system harnesses technology in an innovative way to do something that was not possible years ago by enabling students to gather in a learning environment with other students who previously did not know each other. Additionally, the system operates to allow crowd sourcing of data for the input of exam data, including date and time of the exam in order to automatically extend exam reminders.

A group study system is described. The group study system allows for students to form study groups with other students in the same class or related classes by utilizing various technologies that make such a system possible. The system provides for an anonymous creation of the study sessions and maintains the anonymity until all students joining the study session participate in the study session. These sessions can be in person and virtual. Hybrid and virtual sessions are becoming more and more important with the increasing presence of online education. This system harnesses technology in an innovative way to do something that was not possible years ago by enabling students to gather in a learning environment with other students who previously did not know each other. Additionally, the system operates to allow crowd sourcing of data for the input of exam data, including date and time of the exam in order to automatically extend exam reminders.

A method of creating a scalable dynamic jointed skeleton (DJS) model for enhancing psychomotor leaning using augmented cognition methods realized by an artificial intelligence (AI) engine or image processor. The method involves extracting a DJS model from either live motion images of video files of an athlete, teacher, or expert to create a scalable reference model for using in training, whereby the AI engine extracts physical attributes of the subject including arm length, length, torso length as well as capturing successive movements of a motor skill such as swinging a gold club including position, stance, club position, swing velocity and acceleration, twisting, and more.

There is provided an information processing apparatus including a processor that obtains, from a first P2P database, evaluation information for evaluating learning of a user, which is obtained by an acquisition device, and performs evaluation on learning performed by the user on the basis of the evaluation information.

A modular and reconfigurable chassis enables minimalization of inventory while facilitating ease of conversion into multiple configurations of a welding simulator. The modular and reconfigurable chassis permits both hardware and software related system configurations. The chassis is adapted to receive hardware interfaces for single and multiple user configurations.

Disclosed herein are embodiments for managing a task including one or more skills. A server stores a virtual environment, software agents configured to collect data generated when a user interacts with the virtual environment to perform the task, and a predictive machine learning model. The server generates virtual entities during the performance of the task, and executes the predictive machine learning model to configure the virtual entities based upon data generated when the user interacts with the virtual environment. The server generates the virtual environment and the virtual entities configured for interaction with the user during display by the client device, and receives the data collected by the software agents. The system displays a user interface at the client device to indicate a measurement of each of the skills during performance of the task. The server trains the predictive machine learning model using this measurement of skills during task performance.

Described herein are improved systems and methods for overcoming technical problems associated with limited collaborative learning functionality in educational programming platforms.

Described herein are improved systems and methods for overcoming technical problems associated with limited collaborative learning functionality in educational programming platforms.

A skills learning method for a student gathers objective data relating to the student in response to various stimuli, and produces a predicted feedback units as a function of the objective data using a machine learning-base classifier. The method can include training a neural network using objective data of student interactions and associated subjective assessments of a skill of each objective data. The method includes receiving a new dataset with objective data of a new student and an associated subjective assessment of a skill of the first student represented by the new objective data. A predicted assessment of the skill of the new objective data is calculated by inputting the new objective data into the neural network. The method can include updating the neural network by combining the initial dataset and the new dataset and recompiling the neural network to fit the model dataset based on a learning algorithm.

A skills learning method for a student gathers objective data relating to the student in response to various stimuli, and produces a predicted feedback units as a function of the objective data using a machine learning-base classifier. The method can include training a neural network using objective data of student interactions and associated subjective assessments of a skill of each objective data. The method includes receiving a new dataset with objective data of a new student and an associated subjective assessment of a skill of the first student represented by the new objective data. A predicted assessment of the skill of the new objective data is calculated by inputting the new objective data into the neural network. The method can include updating the neural network by combining the initial dataset and the new dataset and recompiling the neural network to fit the model dataset based on a learning algorithm.