PERSONALIZED ONLINE LEARNING MANAGEMENT SYSTEM AND METHOD

• US 20150206441A1
• Filed: 03/08/2014
• Published: 07/23/2015
• Est. Priority Date: 01/18/2014
• Status: Abandoned Application

• Current Assignee: Invent.ly LLC
• Original Assignee: Invent.ly LLC
• Inventors: Brown, Stephen J.
• Application Number:
  US14/201,810
• Publication Number:
  US 20150206441A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 50/2053 Education institution selection, admissions, or financial aid
• CPC Class Codes: G09B 5/00 Electrically-operated educational appliances working with questions and answers G09B7/00; simulators G09B9/00; advertising or displaying in general G09F
• CPC Class Codes: G09B 5/065 Combinations of audio and video presentations, e.g. videotapes, videodiscs, television systems

Abstract

A system and method for adaptive online learning management for creating and/or publishing customized, personalized, adaptive, and student-specific online course content to enable and provide an efficient learning environment to each student based on the dynamically changing self-evolving student'"'"'s profile. A student'"'"'s profile can be created based on factors such as demographic profile, psychographic profile, learning style, personality traits, interests, social networking profile, social media interactions, online interaction characteristics, social networking circle attributes, prerequisite knowledge assessments, social profile, skill, and performance of the student, wherein the student'"'"'s profile can be processed with respect to a learning object repository to generate a defined set of student-specific learning objects that best suit the profile of the student. A online course content can accordingly be generated based on the defined set of student-specific learning objects.

Claims

1. A method for generating student-specific online course content, said method comprising:

generating an electronically represented student profile vector of a student based on attributes representative of one or a combination of a demographic profile, psychographic profile, learning style, personality traits, interests, social networking profile, social media interactions, online interaction characteristics, social networking circle attributes, prerequisite knowledge assessments, social profile, skill, and performance of said student, wherein said student profile vector comprises one or more of said attributes along with associated quantitative values thereof for said student;

generating an electronically represented learning objects matrix based on one or more learning objectives of at least one course, wherein each learning objective comprises a plurality of learning objects, and wherein each learning object comprises an electronically represented vector of one or more of said attributes along with weights thereof;

processing said student profile vector with said learning objects matrix for said one or more learning objectives to generate an electronically represented student-specific list of learning objects, wherein said student-specific list of learning objects is generated based on values of attributes of said student and weights of corresponding attributes of said learning objects of said learning objects matrix;

evaluating said student-specific list of learning objects to select a set of final learning objects from said student-specific list of learning objects;

assembling said final list of learning objects;

generating said student-specific online course content for said student based on the assembled final list of learning objects; and

delivering said student-specific online course content to said student through an online content transmission means.

2. The method of claim 1, wherein the processing of said student profile vector with said learning objects matrix comprises multiplying a value of each attribute of said student profile vector with the weight of each corresponding attribute of learning objects of said learning objects matrix to retrieve said student-specific list of learning objects.

3. The method of claim 1, further comprising delivering said student-specific online course content in one or a combination of video format, text format, and audio format.

4. The method of claim 1, further comprising presenting said student-specific online course content to said student in a format and manner defined by said student profile vector.

5. The method of claim 1, further comprising generating one or more of said plurality of learning objects based on one or a combination of inputs from third-party content providers, content from publishers, online content, shared notes of other students, core course material, supplemental content, case studies, teacher-authored material, curated material, existing literature, student feedback, dynamically retrieved stakeholder content, and dynamically generated relevant content.

6. The method of claim 1, further comprising changing said student-specific online course content in real-time based on changes in one or more of said student profile vector and said learning objects matrix.

7. The method of claim 6, further comprising computing said changes based on feedback, response, or interactions from one or a combination of students, teachers, publishers third-party evaluators, and stakeholders in said student-specific online course content generation.

8. The method of claim 1, wherein the number of attributes in each learning object that is processed with said student profile vector is the same as the number of attributes in said student profile vector.

9. The method of claim 1, wherein one or more learning objects of said learning objective have different number of attributes.

10. The method of claim 1, wherein the weight of each attribute across learning objects is equal.

11. The method of claim 1, wherein the weight of each attribute across learning objects is different.

12. The method of claim 1, further comprising continuously updating the weights of attributes of learning objects for matching between vectors of said learning objects and said student profile vector.

13. The method of claim 1, wherein the weight of each attribute for said learning object is based on a relevance of said attribute for said learning object.

14. The method of claim 1, wherein the assembling of said final list of learning objects comprises processing a subset of said final list of learning objects.

15. The method of claim 1, further comprising identifying said learning objectives based on relevance of tasks in a current course, tasks in a previous courses, performance of one or more students in said courses, and interest of one or more students in said courses.

16. The method of claim 1, wherein the delivering of said student-specific online course content to said student comprises encrypting said student-specific online course content before transmission to student terminal.

17. The method of claim 1, further comprising delivering said student-specific online course content to one or more communication devices comprising a mobile phone, tablet computer, personal computer, smart phone, laptop, and display-enabled computing device.

18. The method of claim 1, further comprising sorting said student-specific list of learning objects to obtain said final list of learning objects.

19. The method of claim 1, further comprising authorizing said student before delivering said student-specific online course content.

20. The method of claim 1, wherein said student forms part of a group of students, and wherein said student-specific online course content is delivered to said group of students.

21. A system for generating and delivering student-specific online course content for a student, said system comprising:

a student profile vector generation module that generates an electronically represented student profile vector of said student based on attributes representative of one or a combination of a demographic profile, psychographic profile, learning style, personality traits, interests, social networking profile, social media interactions, online interaction characteristics, social networking circle attributes, prerequisite knowledge assessments, social profile, skill, and performance of said student, wherein said student profile vector comprises one or more of said attributes along with associated quantitative values thereof for said student;

a first database that stores said student profile vector;

an electronically represented learning object matrix creation module that creates a learning objects matrix based on one or more learning objectives of at least one course, wherein each learning objective comprises a plurality of learning objects, and wherein each learning object comprises an electronically represented vector of one or more of said attributes along with weights thereof;

a second database that stores said learning objects matrix;

a processing module that processes said student profile vector retrieved from said first database with said learning objects matrix retrieved from said second database for said one or more learning objectives to generate a student-specific list of learning objects, wherein said student-specific list of learning objects is generated based on values of attributes of said student and weights of corresponding attributes of said learning objects of said learning objects matrix; and

a course content generation module that generates said student-specific online course content for said student based on said student-specific list of learning objects, and delivers said student-specific online course content to said student at a client device through an online content transmission means.

22. The system of claim 21, further comprising a prioritization module that selects one or more learning objects from said student-specific list of learning objects to generate said student-specific online course content, wherein said one or more learning objects are selected based on one or a combination of relevance of said learning objects to said student and number of learning objects to be incorporated for generation of said student-specific online course content.

23. The system of claim 21, wherein said processing module multiplies a value of each attribute of said student profile vector with weight of each corresponding attribute of learning objects of said learning objects matrix to retrieve said student-specific list of learning objects.

24. The system of claim 21, wherein said student-specific online course content is delivered in one or a combination of video format, text format, and audio format.

25. The system of claim 21, wherein said student-specific online course content is presented to said student in a format and manner defined based on said student profile vector.

26. The system of claim 21, wherein one or more of said plurality of learning objects are generated based on one or a combination of inputs from third-party content providers, content from publishers, online content, shared notes of other students, core course material, supplemental content, case studies, teacher-authored material, curated material, existing literature, student feedback, dynamically retrieved stakeholder content, and dynamically generated relevant content.

27. The system of claim 21, wherein said student-specific online course content is adapted in real-time based on changes in one or more of said student profile vector and said learning objects matrix.

28. The system of claim 27, wherein said changes are computed based on feedback, response, or interactions from one or a combination of students, teachers, publishers third-party evaluators, and stakeholders in said student-specific online course content generation.

29. The system of claim 21, wherein number of attributes in each learning object that is processed with said student profile vector is the same as the number of attributes in said student profile vector.

30. The system of claim 21, wherein one or more learning objects of said learning objective have different number of attributes.

31. The system of claim 21, wherein the weight of each common attribute across learning objects is equal.

32. The system of claim 21, wherein the weight of each attribute across learning objects is different.

33. The system of claim 21, wherein weights of attributes of learning objects are continuously updated for matching between vectors of said learning objects and said student profile vector.

34. The system of claim 21, wherein the weight of each attribute for said learning object is based on a relevance of said attribute for said learning object.

35. The system of claim 21, wherein said student-specific online course content delivered to said student is encrypted before transmission to said client device.

36. The system of claim 21, wherein said student-specific online course content is delivered to one or more communication devices comprising mobile phone, tablet electronic device, personal computer, smart phone, laptop, and display-enabled computing device.

37. The system of claim 21, wherein said student is authenticated prior to delivering said student-specific online course content.

38. A method for creating adaptive student-specific online course content, said method comprising:

generating an electronically represented student profile vector of a student based on attributes representative of one or a combination of a demographic profile, psychographic profile, learning style, personality traits, interests, social networking profile, social media interactions, online interaction characteristics, social networking circle attributes, prerequisite knowledge assessments, social profile, skill, and performance of said student, wherein said student profile vector comprises one or more of said attributes along with associated quantitative values thereof for said student;

generating an electronically represented learning objects matrix based on one or more learning objectives of at least one course, wherein each learning objective comprises a plurality of learning objects, wherein each learning object comprises an electronically represented vector of one or more of said attributes along with weights thereof, and wherein said plurality of learning objects are selected based on one or a combination of inputs from third-party content providers, content from publishers, online content, shared notes of other students, core course material, supplemental content, case studies, teacher-authored material, curated material, existing literature, student feedback, dynamically retrieved stakeholder content, and dynamically generated relevant content;

processing said student profile vector with said learning objects matrix for said one or more learning objectives to generate an electronically represented student-specific list of learning objects, wherein said student-specific list of learning objects is generated based on values of attributes of said student and weights of corresponding attributes of said learning objects of said learning objects matrix;

generating said student-specific online course content for said student based on said student-specific list of learning objects;

adapting and updating said student-specific online course content based on one or a combination of changes in said values of said attributes for said student profile vector and changes in said weights of said attributes for learning objects of said learning objects matrix; and

presenting said student-specific online course content through a communications network.

39. The method of claim 38, wherein the adapting and updating of said student-specific online course content occurs in real-time.

40. The method of claim 38, wherein the adapting and updating of said student-specific online course content occurs over a period of time based on a regression analysis of weights associated with one or more learning objects of at least one learning objective of one or more courses.

41. The method of claim 38, wherein the adapting and updating of said student-specific online course content occurs over a period of time based on machine learning implementation on student-specific list of learning objects associated with a plurality of students.