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.
1. A learning system comprising:
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 semantic model based on a source material using a machine learning algorithm;
selecting one or more candidate knowledge items from the source material using the semantic model; and
generating an interaction based at least on the one or more candidate knowledge items.
2. The learning system of claim 1 , wherein the operations further include:
transmitting the interaction to a user device; and
generating analytics data based on a response to the interaction by a user of the user device.
3. The learning system of claim 2 , wherein
the analytics data includes learner analytics data and content analytics data;
the learner analytics data includes information regarding the user of the user device; and
the content analytics data includes information based on interaction responses from a plurality of users including the user of the user device.
4. The learning system of claim 3 , wherein
the operations further include generating an additional interaction; and
the additional interaction is generated based on the learner analytics data and/or the content analytics data.
5. The learning system of claim 1 , wherein
the source material includes unstructured content and/or structured content;
the unstructured content includes at least one of audio, video, text, picture, or metadata related to a learning material; and
the structured content includes at least one of database content, a knowledge graph, and content that has been previously retrieved and processed by the learning system.
6. The learning system of claim 5 , wherein
the selecting of the one or more candidate knowledge items includes extracting the one or more candidate knowledge items from the unstructured content and/or selecting the one or more candidate knowledge items from the structured content.
7. The learning system of claim 1 , wherein
the source material includes structured content; and
the selecting of the one or more candidate knowledge items includes traversing a knowledge graph and/or searching a database to select the one or more candidate knowledge items from the structured content.
8. The learning system of claim 1 , wherein
the source material includes unstructured content; and
the selecting of the one or more candidate knowledge items includes at least one of using an image recognition machine learning model to label an object included in an image or a video, using a speech recognition machine learning model to transcribe spoken-word audio, and using an artificial intelligence model to automatically summarize digital content to extract the one or more candidate knowledge items from the unstructured content.
9. The learning system of claim 1 , wherein the operations further include:
generating a text document based on the source material;
tokenizing the text document; and
generating the semantic model based on the tokenized text document using the machine learning algorithm.
10. The learning system of claim 9 , wherein
the operations further include determining supplemental information corresponding to tokens included in the tokenized text document; and
the tokenized text document includes one or more data structures that collectively provide access to raw text of the text document and the supplemental information.
11. The learning system of claim 9 , wherein the operations further comprise:
identifying a plurality of concepts in the tokenized text document;
identifying structural and/or syntactic features of the tokenized text document; and
modeling the tokenized text document based on the structural and/or syntactic features.
12. The learning system of claim 9 , wherein the operations further comprise:
identifying a plurality of concepts in the tokenized text document;
identifying relationships among the plurality of concepts in the tokenized text document; and
building a syntactic graph associated with the tokenized text document, in which the plurality of concepts form nodes and the relationships among the plurality of concepts form vertices.
13. The learning system of claim 1 , wherein
the selecting of the one or more candidate knowledge items from the source material is based on user data that identifies one or more learning objectives of a user.
14. The learning system of claim 13 , wherein
the one or more learning objectives of the user correspond to a query provided by the user and/or are generated automatically based on existing knowledge of the user.
15. The learning system of claim 13 , wherein
the one or more learning objectives are automatically generated by comparing an existing knowledge of the user of the one or more candidate knowledge items with a threshold;
when the existing knowledge of the user of the one or more candidate knowledge items is greater than the threshold, the one or more processors determine that re-learning of the one or more candidate knowledge items is not necessary.
16. The learning system of claim 1 , wherein the operations further comprise:
generating user data that includes one or more metrics that indicate a level of retention or understanding of the one or more candidate knowledge items by a user; and
determining the one or more metrics based on the user's responses to one or more previous interactions.
17. A method comprising:
generating a semantic model based on a source material using a machine learning algorithm;
selecting one or more candidate knowledge items from the source material using the semantic model; and
generating an interaction based at least on the one or more candidate knowledge items.
18. The method of claim 17 , wherein
the selecting of the one or more candidate knowledge items from the source material is based on user data that identifies one or more learning objectives of a user.
19. A non-transitory machine-readable medium having stored thereon machine-readable instructions executable to cause a machine to perform operations comprising:
generating a semantic model based on a source material using a machine learning algorithm;
selecting one or more candidate knowledge items from the source material using the semantic model; and
generating an interaction based at least on the one or more candidate knowledge items.
20. The non-transitory machine-readable medium of claim 19 , wherein
the selecting of the one or more candidate knowledge items from the source material is based on user data that identifies one or more learning objectives of a user.