The disclosure provides a calculation practicing method, a system, an electronic device and a computer readable storage medium, the calculation practicing method includes: providing a calculation question; identifying the type and content of the calculation question; generating an answer area according to the type and content of the calculation question; receiving an answering operation in which the user inputs the answer string for the calculation question in the answer area; identifying the answer string inputted by the user; and determining whether each of the answer characters in the answer string is correct, if there is an incorrect answer character, it will be marked, so that the calculation practice can be realized through the electronic device, which is convenient for students to carry out training.

An educational system may include a contextualizer that accesses and processes content items and build a knowledge base including educational content and relationships between content items. The system may include a user assessor unit that determines user assessment variables based on user responses to previous educational content items and generate a user model based on the user assessment variables. The system may include a recommender that navigates the knowledge base and generates recommendations of new educational content based on the user model. The recommendation can assess the user's proficiency on a given concept based on the user model and maximize the user's probability of success around the concept of which the user shows proficiency, or improve the user's proficiency around the concept the user shows weakness. In some examples, the system may acquire an expert's knowledge about certain concepts to refine the user model, increasing the accuracy of the recommender.

An educational system may include a contextualizer that accesses and processes content items and build a knowledge base including educational content and relationships between content items. The system may include a user assessor unit that determines user assessment variables based on user responses to previous educational content items and generate a user model based on the user assessment variables. The system may include a recommender that navigates the knowledge base and generates recommendations of new educational content based on the user model. The recommendation can assess the user's proficiency on a given concept based on the user model and maximize the user's probability of success around the concept of which the user shows proficiency, or improve the user's proficiency around the concept the user shows weakness. In some examples, the system may acquire an expert's knowledge about certain concepts to refine the user model, increasing the accuracy of the recommender.

A method for improving learning comprehension regarding a lesson includes issuing a first learning illustrative model to a set of learner computing entities. The method further includes obtaining a first comprehension evaluation associated with a first learner computing entity for the first learning illustrative model. The method further includes obtaining a second comprehension evaluation associated with a second learner computing entity for the first learning illustrative model. The method further includes modifying the fundamental illustrative model to illustrate a second set of learning assets of a second learning object based on at least one of the first and second comprehension evaluations to produce a second learning illustrative model. The method further includes sending the second learning illustrative model to the set of learner computing entities.

Classification and visualization of user interactions with an interactive computing platform is provided by applying machine learning (ML) model(s) to user transcripts, the ML model(s) trained to classify interactions with an interactive computing platform, the user transcripts including user interactions between users and the interactive computing platform in progression of the users through tasks based on the user interactions, where the applying classifies the user interactions and identifies features of the user interactions, and building and providing a graphical user interface (GUI) of graphical elements for display on a display device, the graphical elements presenting visualizations of the user interactions and the identified features thereof relative to the tasks and progression of the users therethrough, the GUI including, for each of the tasks, a respective task element that reflects identified features of a set of user interactions of user(s) in progressing through that task.

Classification and visualization of user interactions with an interactive computing platform is provided by applying machine learning (ML) model(s) to user transcripts, the ML model(s) trained to classify interactions with an interactive computing platform, the user transcripts including user interactions between users and the interactive computing platform in progression of the users through tasks based on the user interactions, where the applying classifies the user interactions and identifies features of the user interactions, and building and providing a graphical user interface (GUI) of graphical elements for display on a display device, the graphical elements presenting visualizations of the user interactions and the identified features thereof relative to the tasks and progression of the users therethrough, the GUI including, for each of the tasks, a respective task element that reflects identified features of a set of user interactions of user(s) in progressing through that task.

A computing system helps a user in an extended reality (XR) learning experience achieve mastery through personalized, programmable and conversational coaching. One aspect is that the XR learning experience may consist of a plurality of tasks associated with the user. A second aspect is that the XR learning experience can define different types of conversational interventions triggered by the computing system at various times. A third aspect is that some tasks or interventions can make use of a conversational assistant. A fourth aspect is that as the user is going through the XR learning experience, the system determines which, if any, interventions can be triggered based on the user's state.

A method for generating a virtual reality environment includes detecting an illustrative asset that is common to a first set of assets and a second sets of assets. The method further includes rendering a three-dimensional (3-D) model of the illustrative asset and a 3-D model of the first set of assets using an illustration approach to produce 3-D frames of a first descriptive asset. The method further includes obtaining an evaluation of the first descriptive asset based on a first assessment response associated with a portrayal of the first descriptive asset. The method further includes modifying the illustration approach based on the evaluation to produce an updated illustration approach. The method further includes rendering the 3-D model of the illustrative asset and a 3-D model of the second set of assets using the updated illustration approach to produce 3-D frames of a second descriptive asset.

An adaptive feedback timing system and method includes receiving, by a performance observation system, monitoring data associated with electronically monitoring a lesson by a variable feedback teaching device. Adaptive feedback timing also includes receiving, by the performance observation system, error detection data associated with the variable feedback teaching device automatically detecting an error made by a student during the lesson. After receiving the error detection data, a feedback pattern is automatically selected based on a performance history criterion. Feedback data is then communicated to the variable feedback teaching device for presentation to the student according to the automatically selected feedback pattern.

Methods and systems provide an adaptive method of language learning using automatic speech recognition that allows a user to learn a new language using only their voice—and without using their hands or eyes. The system may be implemented in an application for a smartphone. Each lesson comprises a series of questions that adapt to the user's knowledge. The questions ask for the translation of a word or phrase by playing an audio prompt in the origin language, recording the user speaking the translation in the target language, indicating whether the utterance was correct or incorrect, and providing feedback related to the user's utterance. Each user response is evaluated in real time, and the application provides individualized feedback to the user based on their response. Subsequent questions in the lesson and future lessons are dynamically ordered to adapt to the user's knowledge.