Abstract: An automated lesson generation learning system extracts text-based content from a digital media file. The system parses the extracted content to identify key words to use with prompts in the lesson. The system also may automatically generate a clip from the digital media file, so that the clip is the portion of the file in which the sentence is spoken. The system then automatically generates and outputs a lesson containing the prompt, and optionally the clip. The system may process a user's spoken response to the prompt to determine whether the user correctly pronounced the response.

Abstract: An automated lesson generation learning system extracts text-based content from a digital programming file. The system parses the extracted content to identify a sentence, and key words within the sentences, to use as prompts in the lesson. The system also automatically generates a clip from the digital programming file, so that the clip is the portion of the file in which the sentence is spoken. The system then automatically generates and outputs a lesson containing the prompt and the clip.

Abstract: An automated lesson generation learning system extracts text-based content from a digital programming file. The system parses the extracted content to identify a sentence, and key words within the sentences, to use as prompts in the lesson. The system also automatically generates a clip from the digital programming file, so that the clip is the portion of the file in which the sentence is spoken. The system then automatically generates and outputs a lesson containing the prompt and the clip.

Abstract: An automated lesson generation learning system extracts text-based content from a digital programming file. The system parses the extracted content to identify one or more topics, parts of speech, named entities and/or other material in the content. The system then automatically generates and outputs a lesson containing content that is relevant to the content that was extracted from the digital programming file.

Abstract: A method and system for matching users of a network, such as a language learning network, employs user profile data to determine point match scores and/or compatibility match scores between users of the network. A point match score is a measure of strength for a single interest that two users share. A compatibility match score is an aggregate measure of similarity of multiple interests that two users share. When a first user asks the system to propose another user or users for interaction, the system uses the point match scores, compatibility match scores, or both to determine which additional users to recommend to the first user. The system may present the first user with a profile for each recommended user. The first user may select one of the recommended users and engage in interaction, such as language skills learning or practice, with that user via an interface.

Abstract: A method and system for matching users of a network, such as a language learning network, employs user profile data to determine point match scores and/or compatibility match scores between users of the network such as a social network. A point match score is a measure of strength for a single interest that two users share. A compatibility match score is an aggregate measure of similarity of multiple interests that two users share. When a first user asks the system to propose another user or users for interaction, the system uses the point match scores, compatibility match scores, or both to determine which additional users to recommend to the first user. The system may present the first user with a profile for each recommended user. The first user may select one of the recommended users and engage in interaction, such as language skills learning or practice, with that user via a text, audio and/or video interface.

Abstract: A method and system for matching users of a network, such as a language learning network, employs user profile data to determine point match scores and/or compatibility match scores between users of the network such as a social network. A point match score is a measure of strength for a single interest that two users share. A compatibility match score is an aggregate measure of similarity of multiple interests that two users share. When a first user asks the system to propose another user or users for interaction, the system uses the point match scores, compatibility match scores, or both to determine which additional users to recommend to the first user. The system may present the first user with a profile for each recommended user. The first user may select one of the recommended users and engage in interaction, such as language skills learning or practice, with that user via a text, audio and/or video interface.