Patent Application
20230368690
Educational systems may utilize computer applications to instruct a user in communicating in one or more languages. For example, the educational system may use multimedia presentations to instruct the user, including audio, video, and text-based learning material.
One aspect of the disclosure provides a method comprising: receiving lesson information comprising a plurality of lessons; receiving student information associated with a student; preparing a lesson plan based in part on the lesson information and the student information; presenting a first lesson of the plurality of lessons to the student; determining, based on an interaction between the student and the first lesson, a student engagement information; applying the student engagement information as input to a machine learning model, where applying the student engagement information to the machine learning model causes the machine learning model to output a lesson success evaluation; determining a lesson success based on the lesson success evaluation; modifying the lesson plan based on the lesson success to generate an adjusted lesson plan; and presenting a second lesson of the plurality of lessons to the student based on the adjusted lesson plan.
The method of the preceding paragraph can include any sub-combination of the following features: where the lesson information further comprises a lesson level associated with a difficulty of the plurality of lessons; where the method further comprises: transmitting a request to record a video to a teacher, receiving the video from the teacher, generating a lesson based in part on the received video to create a new lesson, and adding the new lesson to the plurality of lessons; where the request is transmitted in response to the determination of the lesson success; where the method further comprises: applying the lesson plan as input to a machine learning model, where application of the lesson plan to the machine learning model causes the machine learning model to output first information, comparing the first information to the plurality of lessons, and determining, based on the comparison of the first information to the plurality of lessons, the first information is not contained in the plurality of lessons, where the new lesson comprises the first information; where transmitting a request further comprises: applying a third lesson to the machine learning model, where application of the third lesson to the machine learning model causes the machine learning model to output a lesson completeness, and determining the third lesson is an incomplete lesson based on the lesson completeness; where the method further comprises: applying a third lesson to the machine learning model, where application of the third lesson to the machine learning model causes the machine learning model to output an incorrect lesson element, and determining the third lesson is an incorrect lesson based on the incorrect lesson element, where the incorrect lesson is not suitable for presentation to the student; where the incorrect lesson element is one of a position of a teacher, an extraneous sensory stimuli, or an incorrect information item; and where the method further comprises: presenting to a teacher a prompt, the prompt requesting the teacher record the first lesson, displaying to the teacher one or more instructions, the instructions indicating a set of recommendations for recording a first video segment, receiving, from the teacher, a start indication indicating a request to start recording the first video segment, presenting, to the teacher, a video recording interface comprising a position indicator indicating a head position in a video frame, a body position indicator indicating a body position in the video frame, and instructions, recording the first video segment, receiving from the teacher a stop indication indicating the teacher has completed recording the first video segment, terminating recording the first video segment, presenting to the teacher an editing interface comprising a trim option, the trim option allowing the teacher to edit at least a portion of the first video, receiving from the teacher a completion indication indicating the teacher has completed editing the first video segment, and combining the first video segment with a second video segment where the first video segment and the second video segment are associated with a topic of the first lesson to generate the first lesson.
Another aspect of the disclosure provides a system comprising a memory storing computer-executable instruction. The system further comprises a processor in communication with the memory, where the computer-executable instructions, when executed by the processor, cause the processor to: receive lesson information comprising a plurality of lessons; receive student information associated with a student; prepare a lesson plan based in part on the lesson information and the student information; present a first lesson of the plurality of lessons to the student; determine, based on an interaction between the student and the first lesson, a student engagement information; apply the student engagement information as input to a machine learning model, where applying the student engagement information to the machine learning model causes the machine learning model to output a lesson success evaluation; determine a lesson success based on the lesson success evaluation; modify the lesson plan based on the lesson success to generate an adjusted lesson plan; and present a second lesson of the plurality of lessons to the student based on the adjusted lesson plan.
The system of the preceding paragraph can include any sub-combination of the following features: where the lesson information further comprises a lesson level associated with a difficulty of the plurality of lessons; where the computer-executable instructions, when executed by the processor, further cause the processor to: transmit a request to record a video to a teacher, receive the video from the teacher, generate a lesson based in part on the received video to create a new lesson, and add the new lesson to the plurality of lessons; where the computer-executable instructions, when executed by the processor, further cause the processor to: apply the lesson plan as input to a machine learning model, where application of the lesson plan to the machine learning model causes the machine learning model to output a first information, compare the first information to the plurality of lessons, and determine, based on the comparison of the first information to the plurality of lessons, the first information is not contained in the plurality of lessons, where the new lesson comprises the first information; where the computer-executable instructions, when executed by the processor, further cause the processor to: apply a third lesson to the machine learning model, where application of the third lesson to the machine learning model causes the machine learning model to output a lesson completeness, and determine the third lesson is an incomplete lesson based on the lesson completeness; where the computer-executable instructions, when executed by the processor, further cause the processor to: apply a third lesson to the machine learning model, where application of the third lesson to the machine learning model causes the machine learning model to output an incorrect lesson element, and determine the third lesson is an incorrect lesson based on the incorrect lesson element, where the incorrect lesson is not suitable for presentation to the student; where the computer-executable instructions, when executed by the processor, further cause the processor to: present to a teacher a prompt, the prompt requesting the teacher record the first lesson, display to the teacher one or more instructions, the instructions indicating a set of recommendations for recording a first video segment, receive, from the teacher, a start indication indicating a request to start recording the first video segment, present, to the teacher, a video recording interface comprising a position indicator indicating a head position in a video frame, a body position indicator indicating a body position in the video frame, and instructions, record the first video segment, receive from the teacher a stop indication indicating the teacher has completed recording the first video segment, terminate recording the first video segment, present to the teacher an editing interface comprising a trim option, the trim option allowing the teacher to edit at least a portion of the first video, receive from the teacher a completion indication indicating the teacher has completed editing the first video segment, and combine the first video segment with a second video segment where the first video segment and the second video segment are associated with a topic of the first lesson to generate the first lesson; where the video recording interface further comprises a recording status indicator; and where the instructions comprise one of a script or a pronunciation.
Another aspect of the disclosure provides a non-transitory computer-readable storage medium comprising computer-executable instructions, where the computer-executable instructions, when executed by a computer system, cause the computer system to: receive, at a computing device having a processor and a memory, lesson information comprising a plurality of lessons; receive, at the computing device, student information associated with a student; prepare a lesson plan based in part on the lesson information and the student information; present, by a display of the computing device, a first lesson of the plurality of lessons to the student; determine, based on an interaction between the student and the first lesson, a student engagement information; apply the student engagement information as input to a machine learning model, where applying the student engagement information to the machine learning model causes the machine learning model to output a lesson success evaluation; determine a lesson success based on the lesson success evaluation; modify the lesson plan based on the lesson success to generate an adjusted lesson plan; and present, by the display of the computing device, a second lesson of the plurality of lessons to the student based on the adjusted lesson plan.
The non-transitory computer-readable storage medium of the preceding paragraph can include any sub-combination of the following features: where the computer-executable instructions, when executed, further cause the computer system to: transmit a request to record a video to a teacher, present to a teacher a prompt, the prompt requesting the teacher record the first lesson, display to the teacher one or more instructions, the instructions indicating a set of recommendations for recording a first video segment, receive, from the teacher, a start indication indicating a request to start recording the first video segment, present, to the teacher, a video recording interface comprising a position indicator indicating a head position in a video frame, a body position indicator indicating a body position in the video frame, and instructions, record the first video segment, receive from the teacher a stop indication indicating the teacher has completed recording the first video segment, terminate recording the first video segment, present to the teacher an editing interface comprising a trim option, the trim option allowing the teacher to edit at least a portion of the first video, receive from the teacher a completion indication indicating the teacher has completed editing the first video segment, and combine the first video segment with a second video segment where the first video segment and the second video segment are associated with a topic of the first lesson to generate the first lesson.
Embodiments of various inventive features will now be described with reference to the following drawings. Throughout the drawings, reference numbers may be re-used to indicate correspondence between referenced elements. The drawings are provided to illustrate example embodiments described herein and are not intended to limit the scope of the disclosure. To easily identify the discussion of any particular element or act, the most significant digit(s) in a reference number typically refers to the figure number in which that element is first introduced.
The present disclosure generally relates to a mobile application that can improve the ability of a user (e.g., a child, a student, etc.) to speak one or more words of a particular language and/or to understand spoken words of the particular language. For example, the mobile application allows a teacher, parent, or other like individual to generate a video clip that demonstrates how to pronounce the name of an item in a particular language and/or allows a teacher, parent, or other like individual to generate one or more tests using video clips generated via the mobile application to test a user's ability to comprehend the meaning of a word spoken in a particular language.
There are several factors that make this an improvement over the techniques that might be implemented by a real person. Many of these improvements come from the fact that the functionality of the mobile application and associated system are designed for those with learning disabilities. These people, by definition, have trouble learning from real people in standard settings. This platform uses many techniques that are specifically more effective than any real person could be.
Once such technique for ensuring the lessons provided by the system described herein are more effective than prior lessons or live teaching for individuals with learning disabilities is that the presentation filmed via the mobile application is filmed in a sensory-managed way. Most people with learning disabilities have problems with sensory overload. Neurological research has shown that when senses are overloaded, learning is difficult. As many “extraneous” sensory stimuli are removed in this methodology: the system described herein implements processing operations to ensure that the background is all white, makeup is minimized, hair is minimized, facial hair or ornaments (piercings) are taken away, extraneous words are not used, audio is crisp and clear and no extra words are used. No real-life situation allows for sensory inputs to be this controlled, so no real-life teacher could be this effective.
Another technique implemented by the system disclosed herein is that the mobile application and associated system allow for the pre-recording of videos in all languages in native accents. The mobile application and associated system also allows for the presentation of multiple languages at a time in the same lesson—in native accents—for bilingual children. Almost no therapist has the ability to present different languages in perfectly correct native accents. The mobile application and system therefore allow greater access for bilingual children to lessons incorporating their native languages in native accents, and allows for more consistent education without the potential gaps associated with finding therapists qualified to assist bilingual children whose educational needs change over time.
Additionally, the mobile application and system described herein ensure consistent delivery of educational material. People with special needs often have trouble learning unless information is presented in the exact same way, often to the point that flashcards need to be presented in the exact same way, or that an object they are learning about be placed in the exact same spot, which is nearly impossible for a human to accomplish. The anxiety these students experience when lessons are not presented as expected can be overwhelming and shut down all possibility of learning. Therapists often experience these clinical “meltdowns.” No human can ever replicate the exact consistency of pre-recorded lessons, where the lessons are recorded and presented in a consistent format as described below.
The mobile application provides an improvement over existing techniques for helping users pronounce and understand the meanings of words. For example, users like children often have specific interests that, when introduced in a learning experience, improve the likelihood that the users will retain information learned during the experience. Such interests can include vehicles, toys, animals, dolls, and/or the like. When trying to teach hundreds of users in person, however, it may be impractical for a teacher to customize the learning experience to include specific interests of each individual user. In addition, some users with learning disabilities (e.g., autism, down syndrome, etc.) may be in their most learnable state at random times during the day. A teacher trying to teach such users in person may happen to be teaching at a time at which the users are in a less learnable state, and it is impractical for the teacher to be on stand-by and able to teach when the users are in a more learnable state.
The mobile application and associated system described herein can overcome these deficiencies in in-person teaching by providing custom testing and continuous access to teaching resources, while providing additional technical benefits. For example, the mobile application can use an artificial intelligence model (e.g., machine learning model, neural network, etc.) to analyze viewing patterns and/or test results of various users and to automatically adjust the order of content of video clips presented during testing to improve the likelihood that the words and language being taught are retained by a user. In particular, the artificial intelligence model (e.g., machine learning model, neural network, etc.) may output an indication of an adjustment to be made to the order of content of a video clip based on various inputs to the model, such as the success (or failure) of one or more users taking a test that tests the users' knowledge after watching the video clip, an amount of time that one or more users have spent viewing a screen while watching the video clip (which may indicate user attention span), one or more users' viewing patterns of the video clip, one or more users' eye gaze when watching the video clip (e.g., what percentage of time taken to complete playing the video clip did a user look at the screen of the user device running the mobile application, how often did a user look away from the screen of the user device running the mobile application while the video clip was playing, etc.), speeds at which one or more users have answered questions that test the users' knowledge after watching the video clip, and/or the like. In fact, it may be impossible for a human to even use the time to answer a question as a metric in determining how to adjust the content of a video clip because the difference in time between a user that answers quickly a question that is based on the content of a video clip explaining how to pronounce the name of an item in a particular language (e.g., indicating that the order of the content in the video clip was effective) and a user that answers slowly a question that is based on the content of a video clip explaining how to pronounce the name of an item in a particular language (e.g., indicating that the order of the content in the video clip was ineffective) may be too short to be perceptible by a human (e.g., microseconds, nanoseconds, etc.).
As described herein, the mobile application may provide a video clip generation function and a testing function. In the video clip generation function, the mobile application may request certain information from the teacher, parent, or other like individual, such as the language(s) in which to view text in the mobile application and the language to which the teacher, parent, or other like individual will be translating names of items. The mobile application may display a list of items for which a video clip has already been generated and/or a list of items for which a video clip has not yet been generated. The teacher, parent, or other like individual can select an item for which a video clip has not yet been generated, which may cause the mobile application to display prompts and/or other information that explains how the teacher, parent, or other like individual should capture video to be used in generating the video clip. For example, the mobile application may prompt the teacher, parent, or other like individual to capture one or more video segments. A first video segment may be a mid-shot video segment in which the captured video includes the head and shoulders of the teacher, parent, or other like individual as the teacher, parent, or other like individual is pronouncing the name of the item in the selected translation language. A second video segment may be a close-up video segment in which the captured video includes the mouth of the teacher, parent, or other like individual as the teacher, parent, or other like individual is pronouncing the name of the item in the selected translation language. A third video segment may be a skit video segment in which the captured video includes two or more persons conversing in the selected translation language.
The mobile application may include an outline or shape within which the teacher, parent, or other like individual is to position himself or herself to capture the first, second, and/or third video segments. The mobile application can use image processing techniques to determine whether the person's head is positioned within an area of the screen at which the head should be positioned, whether the user's shoulders are positioned within an area of the screen at which the shoulders should be positioned, and/or the like. This consistency in location for the person's face, mouth, shoulders, etc. that the mobile application enforces is important for sensory processing and cannot be replicated by a real-life therapist. For example, the mobile application can use image processing techniques and/or facial recognition technology to identify a person's head in an image or video captured by a camera of the user device running the mobile application and displayed on the screen of the user device. The screen may further overlay an outline of a head, shoulders, a mouth, and/or the like over the image or video captured by the camera of the user device and displayed on the screen (see
Once captured, the mobile application and/or the associated system may stitch some or all of the video segments together with other data to form a video clip. For example, the mobile application and/or associated system may extract the audio from the first video segment and/or the second video segment, and generate a fourth video segment (also referred to herein as a “generalization” video segment) that depicts a graphical image or video of the item spoken in the first and/or second video segment and that includes the extracted audio as the audio track of the video segment. The mobile application and/or associated system may also modify the first video segment to include a graphical image or video of the item spoken in the first video segment positioned adjacent to the head or shoulders of the person speaking in the first video segment (e.g., to the right or left of the head or shoulders of the person speaking in the first video segment), thereby forming a modified first video segment. Optionally, the mobile application and/or associated system can use audio filtering techniques to reduce background noise and/or to increase the decibel level of one or more frequencies corresponding to the voice of the teacher, parent, or other like individual.
The mobile application and/or associated system can then generate a video clip that includes a combination of the first video segment, the modified first video segment, the second video segment, the third video segment, and/or the fourth video segment. As an illustrative example, the mobile application and/or associated system can generate the video clip such that a first portion of the video clip is the modified first video segment, a second portion of the video clip is the second video segment (which is stitched to the modified first video segment), a third portion of the video clip is the fourth video segment (which is stitched to the stitched modified first and second video segments), a fourth portion of the video clip is the second video segment (which is stitched to the stitched modified first, second, and fourth video segments), and a fifth portion of the video clip is the first video segment (which is stitched to the stitched modified first, second, fourth, and second video segments).
While the present disclosure provides an example of an order in which the first video segment, the modified first video segment, the second video segment, the third video segment, and/or the fourth video segment are stitched together, this is not meant to be limiting. For example, the mobile application and/or associated system can stitch the video segments in any possible order and/or include any one video segment one or more times in the generated video clip. In fact, as described herein, the mobile application and/or associated system can use artificial intelligence to set and/or modify the order in which the video segments are stitched. For example, the associated system can train an artificial intelligence model to output an indication of which video segments to include in a video clip and/or the order in which the video segments should be stitched together to form the video clip using training data. The training data can include, for one or more video clips, the prior success (or failure) of one or more users taking a test that tests the users' knowledge after watching the respective video clip, an amount of time that one or more users have spent viewing a screen while watching the respective video clip (which may indicate user attention span), one or more users' viewing patterns of the respective video clip, one or more users' eye gaze when watching the respective video clip (e.g., what percentage of time taken to complete playing the respective video clip did a user look at the screen of the user device running the mobile application, how often did a user look away from the screen of the user device running the mobile application while the respective video clip was playing, etc.), speeds at which one or more users have answered questions that test the users' knowledge after watching the respective video clip, and/or the like, where each portion of the training data associated with a particular video clip may be labeled with an indication of the ordering of the video clips that form the particular video clip. Once trained, a mobile application and/or the associated system can access the artificial intelligence model and use the artificial intelligence model to determine how to adjust the order of the video segments of the video clip, if at all, in a manner as described above.
The associated system can store video clips generated by one or more teachers, parents, or other like individuals. Thus, the associated system may store a library of video clips, with each video clip corresponding to a particular item, a particular language, a particular age of a person featured in the video clip, a particular race of a person featured in the video clip, a particular regional accent or dialect of a person featured in the video clip, a particular sex of a person featured in the video clip, a particular speech impairment of a person featured in the video clip, and/or the like.
A mobile application can access one or more video clips via a network connection with the associated system. When a video clip is generated, the teacher, parent, or other like individual can select whether the video clip is to be shared with the community or whether the video clip is to be private to the teacher, parent, or other like individual and/or particular user. When logged into the mobile application, the teacher, parent, or other like individual can view a list of video clips that have been generated and have been shared with the community, a list of video clips generated by the teacher, parent, or other like individual (that may or may not have been shared with the community), and/or a list of video clips that have yet to be generated. Each video clip may be associated with a particular item and/or language. A dropdown menu or other similar user interface element may be present adjacent to one or more listed video clips that, when selected, allow a teacher, parent, or other like individual to view different versions of the video clip if available. Different versions can include versions generated by a person of a particular age, of a particular race, having a particular regional accent or dialect, of a particular sex, having a particular speech impairment, and/or the like. When setting up an account or when logging in, the mobile application may ask the teacher, parent, or other like individual of biographical characteristics of the user that will be watching video clips (e.g., age, race, regional accent or dialect to be spoken, sex, speech impairment, etc.) (see
In the testing function, the mobile application provides a teacher, parent, or other like individual with one or more sliders that allow the teacher, parent, or other like individual to customize the test to a particular user (see
The mobile application may run on a user device, such as a desktop computer, laptop, and a mobile phone. In general, the user device can be any computing device such as a desktop, laptop or tablet computer, personal computer, wearable computer, server, personal digital assistant (PDA), hybrid PDA/mobile phone, mobile phone, electronic book reader, set-top box, voice command device, camera, digital media player, and the like. A user device may execute the mobile application or a third-party application (e.g., a browser) that can access the functionality of the mobile application described herein via a network page (e.g., a web page).
The mobile application, via the user device, can communicate with the associated system over a network. The network may include any wired network, wireless network, or combination thereof. For example, the network may be a personal area network, local area network, wide area network, over-the-air broadcast network (e.g., for radio or television), cable network, satellite network, cellular telephone network, or combination thereof. As a further example, the network may be a publicly accessible network of linked networks, possibly operated by various distinct parties, such as the Internet. In some embodiments, the network may be a private or semi-private network, such as a corporate or university intranet. The network 6270 may include one or more wireless networks, such as a Global System for Mobile Communications (GSM) network, a Code Division Multiple Access (CDMA) network, a Long Term Evolution (LTE) network, or any other type of wireless network. The network 6270 can use protocols and components for communicating via the Internet or any of the other aforementioned types of networks. For example, the protocols used by the network 6270 may include Hypertext Transfer Protocol (HTTP), HTTP Secure (HTTPS), Message Queue Telemetry Transport (MQTT), Constrained Application Protocol (CoAP), and the like. Protocols and components for communicating via the Internet or any of the other aforementioned types of communication networks are well known to those skilled in the art and, thus, are not described in more detail herein.
The associated system may be a computing system that is a single computing device, or it may include multiple distinct computing devices, such as computer servers, logically or physically grouped together to collectively operate as a server system. The components of the computing system can each be implemented in application-specific hardware (e.g., a server computing device with one or more ASICs) such that no software is necessary, or as a combination of hardware and software. In addition, the modules and components of the computing system can be combined on one server computing device or separated individually or into groups on several server computing devices.
In some embodiments, the features and services provided by the computing system may be implemented as web services consumable via the network. In further embodiments, the computing system is provided by one more virtual machines implemented in a hosted computing environment. The hosted computing environment may include one or more rapidly provisioned and released computing resources, which computing resources may include computing, networking and/or storage devices. A hosted computing environment may also be referred to as a cloud computing environment.
The student 6205 may be any individual using the mobile application to learn. The student device 6210 may be running the adaptive teaching system 6215 locally and may be a mobile device, laptop computing device, desktop computing device, touchscreen monitor in communication with a remote computing device (e.g., a cloud computing environment where the adaptive teaching system 6215 is running), or any other device configured to display video lessons and accept input from the student 6205. In some embodiments, the adaptive teaching system 6215 may run remotely, such as on the server 6220 or in a cloud computing environment. In the remote operation example, input from the student 6205 may be transmitted to the remote device by the network 6270 and videos and other information from the adaptive teaching system 6215 may be transmitted to the student device 6210 via the network 6270.
The teacher 6260 may be a parent, educator, tutor, or any like individual involved in the instruction of the student 6205 using the adaptive teaching system 6215. The teacher device 6250 may be running the adaptive teaching system 6215 locally and may be a mobile device, laptop computing device, desktop computing device, touchscreen monitor in communication with a remote computing device (e.g., a cloud computing environment where the adaptive teaching system 6215 is running), or any other device configured to display instructions (e.g., those shown in
The server 6220 may be a computing device in communication with the student device 6210 and/or the teacher device 6250 remotely via the network. In some embodiments, there may be a physical connection used where the server 6220 is collocated with the student device 6210 and/or the teacher device 6250.
The server 6220 may be a computing system that is a single computing device, or it may include multiple distinct computing devices, such as computer servers, logically or physically grouped together to collectively operate as a server system. The components of the computing system can each be implemented in application-specific hardware (e.g., a server computing device with one or more ASICs) such that no software is necessary, or as a combination of hardware and software. In addition, the modules and components of the computing system can be combined on one server computing device or separated individually or into groups on several server computing devices.
In some embodiments, the features and services provided by the server 6220 may be implemented as web services consumable via the network. In further embodiments, the computing system is provided by one more virtual machines implemented in a hosted computing environment. The hosted computing environment may include one or more rapidly provisioned and released computing resources, which computing resources may include computing, networking and/or storage devices. A hosted computing environment may also be referred to as a cloud computing environment.
In some embodiments, at least some of the functionality of the operates on each of the student device 6210, teacher device 6250, and the server 6220. In some embodiments, the adaptive teaching system 6215 may run on only one device, for example the adaptive teaching system 6215 may be run only on the server 6220 and communicate information via the network 6270 with the student device 6210 to present information to and receive input from student 6205, and communicate via the network 6270 with the adaptive teaching system 6215 to present information to and receive input from the teacher 6260.
Routine 6300 begins at block 6305. In some embodiments routine 6300 begins in response to a request from the student 6205 to initiate a lesson plan. Alternatively, routine 6300 may begin in response to a request from the teacher 6260 to begin the lesson plan. When the routine 6300 has begun, the routine 6300 moves to block 6310.
At block 6310, the server 6220 receives lesson information. In some embodiments, lesson information is received from one or more of the student device 6210, teacher device 6250, or another computing device associated with the adaptive teaching environment 6200 (e.g., a second teacher device, a tutor device, or a like individual participating in the education of the student 6205). Lesson information may comprise, for example, video lesson information (e.g., video clips recorded by a teacher, parent, or like individual using the adaptive teaching environment 6200), text information associated with a lesson, student preference information, student performance information, student learning style, a lesson level indicating the difficulty associated with a lesson or topic, student engagement information, or other information associated with a lesson which may be used to teach the student 6205 (e.g., a lesson time, lesson length, lesson goal, lesson category, lesson difficulty level, lesson language, etc.). In some embodiments, the system will receive student information, such as biographical information entered into the system by the user interfaces of
At block 6315, the server 6220 prepares an initial lesson plan. The initial lesson plan may be prepared based on, for example, biographical information entered by a user (e.g., the student 6205, the teacher 6260, a parent, a tutor, etc.) such as the information entered in the user interfaces presented in
In some embodiments, preparing the lesson plan may comprise requesting from the teacher 6260, for example by display of a request on the teacher device 6250, the recording of one or more video clips. The teacher 6260 may then record video clips for one or more of the lessons of the lesson plan as described herein. The adaptive teaching system 6215 may receive one or more video segments from the teacher 6260 in response to the request and incorporate the received one or more video segments into a video for a new lesson. In some embodiments, the adaptive teaching system 6215 may analyze the lessons of the lesson plan, such as by applying the lessons to an artificial intelligence model, to determine the lesson comprises correct information. If the artificial intelligence learning model indicates the lesson contains incorrect information, such as by indicating an incorrect lesson element, the teacher 6260 may be asked to record a video segment which may be combined with correct video segments of the lesson to generate a new lesson, or the teacher 6260 may be asked to record all video segments of the incorrect lesson again to correct the incorrect learning element. A learning element may comprise, for example, a position of the teacher 6260 in the video frame, an extraneous sensory stimuli (e.g., a flashing light behind the teacher 6260), or an incorrect information item (e.g., a word in the video segment that is not expected to be in the video segment, or an incorrect pronunciation). When the lesson plan has been prepared, the routine 6300 moves to block 6320.
At block 6320, the next lesson in the lesson plan is transmitted from the server 6220 to the student device 6210. The lesson may be transmitted via the network 6270, by a physical connection between the student device 6210 and the server 6220, or by any other communication method between the server 6220 and the student device 6210. Transmitting the lesson to the student device 6210 may additionally cause the student device 6210 to begin presentation of the lesson to the student 6205, or may cause the student device 6210 to present a request for confirmation that the student 6205 is prepared to begin the lesson and after receiving such confirmation the student device 6210 may then present the lesson. The lesson may comprise video, audio, text, including instruction and evaluation (e.g., a question testing the material presented in a video lesson). Where the routine 6300 is operating on the student device 6210, block 6320 is optional, and the routine 6300 may instead present the next lesson to the student 6205 directly. When the lesson has been transmitted to the student device 6210 and presented to the student 6205, the routine 6300 moves to block 6325.
At block 6325, the student is evaluated by the adaptive teaching system 6215. In some embodiments, evaluation may comprise determining a student's engagement with the lesson material, such as by recording the level of eye contact with the video by the student 6205, speed of reaction to lesson prompts presented to the student 6205, time spent responding to an evaluation question, or a number of times a video lesson was viewed. In some embodiments, evaluating the student may comprise determining the number of correct or incorrect responses to evaluation questions, a number of times a student correctly or incorrectly answered one or more questions, and the like. Evaluation of the student 6205 may be performed by an artificial intelligence model trained to effectively instruct the 6205. In some embodiments, a lesson success is evaluated by the adaptive teaching system 6215 indicating how successful a lesson was in instructing the student on the topic associated with the lesson. In some embodiments, student engagement information based on a student's engagement with the lesson may be applied to the artificial intelligence model and the artificial intelligence model may output a lesson success evaluation indicating, in part, an indication of how successful the lesson was in teaching the student. When the student has been evaluated, the routine 6300 moves to block 6330.
At block 6330, the adaptive teaching system 6215 adjusts the lesson plan to create an adjusted lesson plan. The lesson plan may be adjusted based on, for example, the result of the evaluation of the student performed at block 6325, a request from the teacher 6260, a request from the student 6205, or based on another determination made by the adaptive teaching system 6215. The adaptive teaching system 6215 adjusts the lesson plan to more efficiently or effectively teach the student 6205, for example by determining that the student finds a particular color or object distracting, or is more engaged by a particular teacher 6260 or object as described previously herein. Adjusting the lesson plan may comprise adding videos or tests, removing videos or tests, reordering videos or tests, or otherwise altering the previous lesson plan. In some embodiments, the adaptive teaching system 6215 may determine a time for instruction is nearing completion (e.g., when one hour is set aside for instruction and 58 minutes have passed) or has been completed, and may adjust the lesson plan to end by removing any remaining lessons from the lesson plan. As discussed above in relation to block 6315, a teacher may be asked to provide one or more video segments to create a video for a new lesson. The request for the teacher to record one or more video segments may be sent at block 6330 in response to the evaluation of block 6325 indicating a new lesson should be created. When the lesson plan has been adjusted the routine 6300 moves to decision block 6335.
At decision block 6335, the adaptive teaching system 6215 determines whether there are additional lessons to be completed in the lesson plan. Lessons may comprise video lessons, audio lessons, text lessons, or testing material. When the adaptive teaching system 6215 determines there are additional lessons in the lesson plan to be completed, the routine 6300 moves to block 6320 and transmits the next lesson in the lesson plan. When the adaptive teaching system 6215 determines there are no more lessons for the student 6205 to complete at this time, the routine 6300 moves to block 6340 and ends.
In some examples, the features and services provided by the computing device 6400 may be implemented as web services consumable via one or more communication networks. In further embodiments, the computing device 6400 is provided by one or more virtual machines implemented in a hosted computing environment. The hosted computing environment may include one or more rapidly provisioned and released computing resources, such as computing devices, networking devices, and/or storage devices. A hosted computing environment may also be referred to as a “cloud” computing environment.
In some embodiments, as shown, a communication device 110A may include: one or more computer processors 6402, such as physical central processing units (“CPUs”); one or more network interfaces 6404, such as a network interface cards (“NICs”); one or more computer readable medium drives 6406, such as a high density disk (“HDDs”), solid state drives (“SSDs”), flash drives, and/or other persistent non-transitory computer readable media; one or more input/output device interfaces 6408, such as a display, a speaker, a microphone, a camera, and/or other components configured to allow the input or output of information; and one or more computer-readable memories 6410, such as random access memory (“RAM”) and/or other volatile non-transitory computer readable media.
The computer-readable memory 6410 may include computer program instructions that one or more computer processors 6402 execute and/or data that the one or more computer processors 6402 use in order to implement one or more embodiments. For example, the computer-readable memory 6410 can store an operating system 6412 to provide general administration of the computing device 6400. As another example, the computer-readable memory 6410 may store a video clip generation system 6414 configured to enable the recording, editing, processing, and display of videos used by the system described herein to create lesson video clips. In some examples, a video lesson is received from the server 6220 by the one or more network interfaces 6404 based on an input of the owner or user of the computing device 6400 (e.g., a student, teacher, or other like individual). In other examples, the video lesson or clip may be stored in the computer-readable memory 6410 of the computing device 6400 for as long as a user owns or controls the computing device 6400 (e.g., until the computing device 6400 is transferred to another user).
As another example, the computer-readable memory 6410 may store an adaptive teaching system 6215. The adaptive teaching system 6215 may be received from the server 6220 by the one or more network interfaces 6404 of the computing device 6400. Alternatively, the adaptive teaching system 6215 may be stored in the computer-readable memory 6410 of the computing device 6400, such as when installed by a user (e.g., the student 6205). The adaptive teaching system 6215 stored in the computer-readable memory 6410 may differ from a second adaptive teaching system 6215 that would be used by a second computing device 6400, for example the adaptive teaching system 6215 installed on the student device 6210 may provide functionality associated with instructing the student 6205 and the adaptive teaching system 6215 installed on the teacher device 6250 may provide functionality associated with recording video lessons by the teacher 6260.
All of the methods and tasks described herein may be performed and fully automated by a computer system. The computer system may, in some cases, include multiple distinct computers or computing devices (e.g., physical servers, workstations, storage arrays, cloud computing resources, etc.) that communicate and interoperate over a network to perform the described functions. Each such computing device typically includes a processor (or multiple processors) that executes program instructions or modules stored in a memory or other non-transitory computer-readable storage medium or device (e.g., solid state storage devices, disk drives, etc.). The various functions disclosed herein may be embodied in such program instructions, or may be implemented in application-specific circuitry (e.g., ASICs or FPGAs) of the computer system. Where the computer system includes multiple computing devices, these devices may, but need not, be co-located. The results of the disclosed methods and tasks may be persistently stored by transforming physical storage devices, such as solid-state memory chips or magnetic disks, into a different state. In some embodiments, the computer system may be a cloud-based computing system whose processing resources are shared by multiple distinct business entities or other users.
Depending on the embodiment, certain acts, events, or functions of any of the processes or algorithms described herein can be performed in a different sequence, can be added, merged, or left out altogether (e.g., not all described operations or events are necessary for the practice of the algorithm). Moreover, in certain embodiments, operations or events can be performed concurrently, e.g., through multi-threaded processing, interrupt processing, or multiple processors or processor cores or on other parallel architectures, rather than sequentially.
The various illustrative logical blocks, modules, routines, and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware, or combinations of electronic hardware and computer software. To clearly illustrate this interchangeability, various illustrative components, blocks, modules, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware, or as software that runs on hardware, depends upon the particular application and design conditions imposed on the overall system. The described functionality can be implemented in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the disclosure.
Moreover, the various illustrative logical blocks and modules described in connection with the embodiments disclosed herein can be implemented or performed by a machine, such as a processor device, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A processor device can be a microprocessor, but in the alternative, the processor device can be a controller, microcontroller, or state machine, combinations of the same, or the like. A processor device can include electrical circuitry configured to process computer-executable instructions. In another embodiment, a processor device includes an FPGA or other programmable device that performs logic operations without processing computer-executable instructions. A processor device can also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Although described herein primarily with respect to digital technology, a processor device may also include primarily analog components. For example, some or all of the algorithms described herein may be implemented in analog circuitry or mixed analog and digital circuitry. A computing environment can include any type of computer system, including, but not limited to, a computer system based on a microprocessor, a mainframe computer, a digital signal processor, a portable computing device, a device controller, or a computational engine within an appliance, to name a few.
The elements of a method, process, routine, or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor device, or in a combination of the two. A software module can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of a non-transitory computer-readable storage medium. An exemplary storage medium can be coupled to the processor device such that the processor device can read information from, and write information to, the storage medium. In the alternative, the storage medium can be integral to the processor device. The processor device and the storage medium can reside in an ASIC. The ASIC can reside in a user terminal. In the alternative, the processor device and the storage medium can reside as discrete components in a user terminal.
Conditional language used herein, such as, among others, “can,” “could,” “might,” “may,” “e.g.,” and the like, unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments include, while other embodiments do not include, certain features, elements and/or steps. Thus, such conditional language is not generally intended to imply that features, elements and/or steps are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without other input or prompting, whether these features, elements and/or steps are included or are to be performed in any particular embodiment. The terms “comprising,” “including,” “having,” and the like are synonymous and are used inclusively, in an open-ended fashion, and do not exclude additional elements, features, acts, operations, and so forth. Also, the term “or” is used in its inclusive sense (and not in its exclusive sense) so that when used, for example, to connect a list of elements, the term “or” means one, some, or all of the elements in the list.
Disjunctive language such as the phrase “at least one of X, Y, Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present.
Unless otherwise explicitly stated, articles such as “a” or “an” should generally be interpreted to include one or more described items. Accordingly, phrases such as “a device configured to” are intended to include one or more recited devices. Such one or more recited devices can also be collectively configured to carry out the stated recitations. For example, “a processor configured to carry out recitations A, B and C” can include a first processor configured to carry out recitation A working in conjunction with a second processor configured to carry out recitations B and C.
While the above detailed description has shown, described, and pointed out novel features as applied to various embodiments, it can be understood that various omissions, substitutions, and changes in the form and details of the devices or algorithms illustrated can be made without departing from the spirit of the disclosure. As can be recognized, certain embodiments described herein can be embodied within a form that does not provide all of the features and benefits set forth herein, as some features can be used or practiced separately from others. The scope of certain embodiments disclosed herein is indicated by the appended claims rather than by the foregoing description. All changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope.
This application claims the benefit of U.S. Provisional Patent Application No. 63/342,506, entitled “MOBILE APPLICATION FOR GENERATING AND VIEWING VIDEO CLIPS IN DIFFERENT LANGUAGES” and filed on May 16, 2022, which is hereby incorporated by reference herein in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 63342506 | May 2022 | US |
