Learning systems and methods of the present disclosure include generating a text document based on a digital file, tokenizing the text document, generating a semantic model based on the tokenized text document using an unsupervised machine learning algorithm, assigning a plurality of passage scores to a corresponding plurality of passages of the tokenized text document, selecting one or more candidate knowledge items from the tokenized text document based on the plurality of passage scores, filtering the one or more candidate knowledge items based on user data, generating one or more structured learning assets based on the one or more filtered candidate knowledge items, generating an interaction based at least on the one or more structured learning assets, and transmitting the interaction to a user device. Each passage score is assigned based on a relationship between a corresponding passage and the semantic model.
G09B 7/04 - Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by the student characterised by modifying the teaching programme in response to a wrong answer, e.g. repeating the question, supplying a further explanation
G09B 5/06 - Electrically-operated educational appliances with both visual and audible presentation of the material to be studied
G09B 7/07 - Electrically-operated teaching apparatus or devices working with questions and answers of the multiple-choice answer type, i.e. where a given question is provided with a series of answers and a choice has to be made from the answers providing for individual presentation of questions to a plurality of student stations
G09B 7/08 - Electrically-operated teaching apparatus or devices working with questions and answers of the multiple-choice answer type, i.e. where a given question is provided with a series of answers and a choice has to be made from the answers characterised by modifying the teaching programme in response to a wrong answer, e.g. repeating the question, supplying further information
G09B 5/12 - Electrically-operated educational appliances providing for individual presentation of information to a plurality of student stations different stations being capable of presenting different information simultaneously
A method for generating a set of concepts related to a target concept includes accessing a set of candidate concepts, embedding the target concept and the set of candidate concepts in a semantic vector space, selecting one or more intermediate concepts from the set of candidate concepts in response to determining whether each embedded candidate concept in the set of embedded candidate concepts satisfies a predetermined relationship with the embedded target concept, and filtering the one or more intermediate concepts to yield the set of concepts related to the target concept. The method may further include generating a multiple-choice question in which the target concept corresponds to a correct answer choice and the set of concepts related to the target concept correspond to distractors.
G06F 16/00 - Information retrievalDatabase structures thereforFile system structures therefor
G06F 16/36 - Creation of semantic tools, e.g. ontology or thesauri
G09B 7/08 - Electrically-operated teaching apparatus or devices working with questions and answers of the multiple-choice answer type, i.e. where a given question is provided with a series of answers and a choice has to be made from the answers characterised by modifying the teaching programme in response to a wrong answer, e.g. repeating the question, supplying further information
G09B 7/04 - Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by the student characterised by modifying the teaching programme in response to a wrong answer, e.g. repeating the question, supplying a further explanation
A learning system includes a non-transitory memory, and one or more hardware processors configured or programmed to read instructions from the non-transitory memory to cause the learning system to perform operations including generating a user knowledge mesh including generating topic nodes each corresponding to a topic included in the user knowledge mesh, and generating concept nodes each corresponding to a key learnable concept, wherein each of the topic nodes is connected to another one of the topic nodes, each of the concept nodes is connected to one of the topic nodes, and each of the key learnable concepts includes one or more interactions related to the key learnable concept.
G06T 11/20 - Drawing from basic elements, e.g. lines or circles
G09B 7/04 - Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by the student characterised by modifying the teaching programme in response to a wrong answer, e.g. repeating the question, supplying a further explanation
G09B 7/08 - Electrically-operated teaching apparatus or devices working with questions and answers of the multiple-choice answer type, i.e. where a given question is provided with a series of answers and a choice has to be made from the answers characterised by modifying the teaching programme in response to a wrong answer, e.g. repeating the question, supplying further information
4.
System and method for automatically generating concepts related to a target concept
A method for generating a set of concepts related to a target concept includes accessing a set of candidate concepts, embedding the target concept and the set of candidate concepts in a semantic vector space, selecting one or more intermediate concepts from the set of candidate concepts in response to determining whether each embedded candidate concept in the set of embedded candidate concepts satisfies a predetermined relationship with the embedded target concept, and filtering the one or more intermediate concepts to yield the set of concepts related to the target concept. The method may further include generating a multiple-choice question in which the target concept corresponds to a correct answer choice and the set of concepts related to the target concept correspond to distractors.
G06F 16/00 - Information retrievalDatabase structures thereforFile system structures therefor
G06F 16/36 - Creation of semantic tools, e.g. ontology or thesauri
G09B 7/08 - Electrically-operated teaching apparatus or devices working with questions and answers of the multiple-choice answer type, i.e. where a given question is provided with a series of answers and a choice has to be made from the answers characterised by modifying the teaching programme in response to a wrong answer, e.g. repeating the question, supplying further information
G09B 7/04 - Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by the student characterised by modifying the teaching programme in response to a wrong answer, e.g. repeating the question, supplying a further explanation
Learning systems and methods of the present disclosure include generating a text document based on a digital file, tokenizing the text document, generating a semantic model based on the tokenized text document using an unsupervised machine learning algorithm, assigning a plurality of passage scores to a corresponding plurality of passages of the tokenized text document, selecting one or more candidate knowledge items from the tokenized text document based on the plurality of passage scores, filtering the one or more candidate knowledge items based on user data, generating one or more structured learning assets based on the one or more filtered candidate knowledge items, generating an interaction based at least on the one or more structured learning assets, and transmitting the interaction to a user device. Each passage score is assigned based on a relationship between a corresponding passage and the semantic model.
G09B 7/04 - Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by the student characterised by modifying the teaching programme in response to a wrong answer, e.g. repeating the question, supplying a further explanation
G09B 5/06 - Electrically-operated educational appliances with both visual and audible presentation of the material to be studied
G09B 7/07 - Electrically-operated teaching apparatus or devices working with questions and answers of the multiple-choice answer type, i.e. where a given question is provided with a series of answers and a choice has to be made from the answers providing for individual presentation of questions to a plurality of student stations
G09B 7/08 - Electrically-operated teaching apparatus or devices working with questions and answers of the multiple-choice answer type, i.e. where a given question is provided with a series of answers and a choice has to be made from the answers characterised by modifying the teaching programme in response to a wrong answer, e.g. repeating the question, supplying further information
G09B 5/12 - Electrically-operated educational appliances providing for individual presentation of information to a plurality of student stations different stations being capable of presenting different information simultaneously
A method for predictively updating one or more user parameters associated with a user of a learning system includes predicting, based on the one or more user parameters, a predicted activity of the user, receiving an actual activity of the user, comparing the predicted activity to the actual activity, and updating the one or more user parameters in response to determining that the predicted activity does not match the actual activity. The method may further include scheduling one or more learning interactions based on the one or more updated learning parameters, where the scheduling includes selecting at least one of a timing of the one or more learning interactions or a type of the one or more learning interactions.
G09B 7/00 - Electrically-operated teaching apparatus or devices working with questions and answers
G09B 7/06 - Electrically-operated teaching apparatus or devices working with questions and answers of the multiple-choice answer type, i.e. where a given question is provided with a series of answers and a choice has to be made from the answers
G06N 7/00 - Computing arrangements based on specific mathematical models
G06F 17/18 - Complex mathematical operations for evaluating statistical data
G09B 7/04 - Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by the student characterised by modifying the teaching programme in response to a wrong answer, e.g. repeating the question, supplying a further explanation
G06N 7/01 - Probabilistic graphical models, e.g. probabilistic networks
7.
System and method for automatically generating concepts related to a target concept
A method for generating a set of concepts related to a target concept includes accessing a set of candidate concepts, embedding the target concept and the set of candidate concepts in a semantic vector space, selecting one or more intermediate concepts from the set of candidate concepts in response to determining whether each embedded candidate concept in the set of embedded candidate concepts satisfies a predetermined relationship with the embedded target concept, and filtering the one or more intermediate concepts to yield the set of concepts related to the target concept. The method may further include generating a multiple-choice question in which the target concept corresponds to a correct answer choice and the set of concepts related to the target concept correspond to distractors.
G06F 16/00 - Information retrievalDatabase structures thereforFile system structures therefor
G06F 16/36 - Creation of semantic tools, e.g. ontology or thesauri
G09B 7/08 - Electrically-operated teaching apparatus or devices working with questions and answers of the multiple-choice answer type, i.e. where a given question is provided with a series of answers and a choice has to be made from the answers characterised by modifying the teaching programme in response to a wrong answer, e.g. repeating the question, supplying further information
G09B 7/04 - Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by the student characterised by modifying the teaching programme in response to a wrong answer, e.g. repeating the question, supplying a further explanation
Learning systems and methods of the present disclosure include generating a text document based on a digital file, tokenizing the text document, generating a semantic model based on the tokenized text document using an unsupervised machine learning algorithm, assigning a plurality of passage scores to a corresponding plurality of passages of the tokenized text document, selecting one or more candidate knowledge items from the tokenized text document based on the plurality of passage scores, filtering the one or more candidate knowledge items based on user data, generating one or more structured learning assets based on the one or more filtered candidate knowledge items, generating an interaction based at least on the one or more structured learning assets, and transmitting the interaction to a user device. Each passage score is assigned based on a relationship between a corresponding passage and the semantic model.
G09B 7/04 - Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by the student characterised by modifying the teaching programme in response to a wrong answer, e.g. repeating the question, supplying a further explanation
G09B 5/06 - Electrically-operated educational appliances with both visual and audible presentation of the material to be studied
G09B 7/07 - Electrically-operated teaching apparatus or devices working with questions and answers of the multiple-choice answer type, i.e. where a given question is provided with a series of answers and a choice has to be made from the answers providing for individual presentation of questions to a plurality of student stations
G09B 7/08 - Electrically-operated teaching apparatus or devices working with questions and answers of the multiple-choice answer type, i.e. where a given question is provided with a series of answers and a choice has to be made from the answers characterised by modifying the teaching programme in response to a wrong answer, e.g. repeating the question, supplying further information
G09B 5/12 - Electrically-operated educational appliances providing for individual presentation of information to a plurality of student stations different stations being capable of presenting different information simultaneously
Learning systems and methods of the present disclosure include generating a text document based on a digital file, tokenizing the text document, generating a semantic model based on the tokenized text document using an unsupervised machine learning algorithm, assigning a plurality of passage scores to a corresponding plurality of passages of the tokenized text document, selecting one or more candidate knowledge items from the tokenized text document based on the plurality of passage scores, filtering the one or more candidate knowledge items based on user data, generating one or more structured learning assets based on the one or more filtered candidate knowledge items, generating an interaction based at least on the one or more structured learning assets, and transmitting the interaction to a user device. Each passage score is assigned based on a relationship between a corresponding passage and the semantic model.
G09B 7/04 - Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by the student characterised by modifying the teaching programme in response to a wrong answer, e.g. repeating the question, supplying a further explanation
G09B 5/06 - Electrically-operated educational appliances with both visual and audible presentation of the material to be studied
G09B 7/07 - Electrically-operated teaching apparatus or devices working with questions and answers of the multiple-choice answer type, i.e. where a given question is provided with a series of answers and a choice has to be made from the answers providing for individual presentation of questions to a plurality of student stations
G09B 7/08 - Electrically-operated teaching apparatus or devices working with questions and answers of the multiple-choice answer type, i.e. where a given question is provided with a series of answers and a choice has to be made from the answers characterised by modifying the teaching programme in response to a wrong answer, e.g. repeating the question, supplying further information
G09B 5/12 - Electrically-operated educational appliances providing for individual presentation of information to a plurality of student stations different stations being capable of presenting different information simultaneously
A method for generating a set of concepts related to a target concept includes accessing a set of candidate concepts, embedding the target concept and the set of candidate concepts in a semantic vector space, selecting one or more intermediate concepts from the set of candidate concepts in response to determining whether each embedded candidate concept in the set of embedded candidate concepts satisfies a predetermined relationship with the embedded target concept, and filtering the one or more intermediate concepts to yield the set of concepts related to the target concept. The method may further include generating a multiple-choice question in which the target concept corresponds to a correct answer choice and the set of concepts related to the target concept correspond to distractors.
G06F 16/00 - Information retrievalDatabase structures thereforFile system structures therefor
G06F 16/36 - Creation of semantic tools, e.g. ontology or thesauri
G09B 7/08 - Electrically-operated teaching apparatus or devices working with questions and answers of the multiple-choice answer type, i.e. where a given question is provided with a series of answers and a choice has to be made from the answers characterised by modifying the teaching programme in response to a wrong answer, e.g. repeating the question, supplying further information
G09B 7/04 - Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by the student characterised by modifying the teaching programme in response to a wrong answer, e.g. repeating the question, supplying a further explanation
A method for predictively updating one or more user parameters associated with a user of a learning system includes predicting, based on the one or more user parameters, a predicted activity of the user, receiving an actual activity of the user, comparing the predicted activity to the actual activity, and updating the one or more user parameters in response to determining that the predicted activity does not match the actual activity. The method may further include scheduling one or more learning interactions based on the one or more updated learning parameters, where the scheduling includes selecting at least one of a timing of the one or more learning interactions or a type of the one or more learning interactions.
G09B 7/00 - Electrically-operated teaching apparatus or devices working with questions and answers
G09B 7/06 - Electrically-operated teaching apparatus or devices working with questions and answers of the multiple-choice answer type, i.e. where a given question is provided with a series of answers and a choice has to be made from the answers
G06N 7/00 - Computing arrangements based on specific mathematical models
G06F 17/18 - Complex mathematical operations for evaluating statistical data
G09B 7/04 - Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by the student characterised by modifying the teaching programme in response to a wrong answer, e.g. repeating the question, supplying a further explanation
12.
Personalized learning system and method for the automated generation of structured learning assets based on user data
Learning systems and methods of the present disclosure include generating a text document based on a digital file, tokenizing the text document, generating a semantic model based on the tokenized text document using an unsupervised machine learning algorithm, assigning a plurality of passage scores to a corresponding plurality of passages of the tokenized text document, selecting one or more candidate knowledge items from the tokenized text document based on the plurality of passage scores, filtering the one or more candidate knowledge items based on user data, generating one or more structured learning assets based on the one or more filtered candidate knowledge items, generating an interaction based at least on the one or more structured learning assets, and transmitting the interaction to a user device. Each passage score is assigned based on a relationship between a corresponding passage and the semantic model.
G09B 7/04 - Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by the student characterised by modifying the teaching programme in response to a wrong answer, e.g. repeating the question, supplying a further explanation
G09B 5/06 - Electrically-operated educational appliances with both visual and audible presentation of the material to be studied
G09B 7/07 - Electrically-operated teaching apparatus or devices working with questions and answers of the multiple-choice answer type, i.e. where a given question is provided with a series of answers and a choice has to be made from the answers providing for individual presentation of questions to a plurality of student stations
G09B 7/08 - Electrically-operated teaching apparatus or devices working with questions and answers of the multiple-choice answer type, i.e. where a given question is provided with a series of answers and a choice has to be made from the answers characterised by modifying the teaching programme in response to a wrong answer, e.g. repeating the question, supplying further information
G09B 5/12 - Electrically-operated educational appliances providing for individual presentation of information to a plurality of student stations different stations being capable of presenting different information simultaneously
patents