US-12626608-B2 - System to determine a personalized learning pathway

Abstract

An electronic learning system and method for providing at least one recommended personalized learning pathway to a user. The system may include one or more computing devices that communicate input data, and receive output data, over a network with a learning management system. A server may be configured to: provide the learning management system over the network; store data, including user data, education provider data, and/or industry data, on at least one storage device; and implement at least one analytics engine. The analytics engine may be configurable to: analyze the data associated with the learning management system; generate personalized data indicative of a personalized learning pathway that enables a user to build the competencies for a desired career and/or grow an individual's career, the personalized data based at least in part on the analyzed data; and generate at least one recommendation for the user based on the personalized data.

Inventors

• John Baker
• Brian Cepuran
• Jeremy Auger

Assignees

• John Baker
• Brian Cepuran
• Jeremy Auger

Dates

Publication Date

20260512

Application Date

20230131

Claims (16)

1. 1 . An electronic learning system, comprising: one or more computing devices that communicate over a network with a learning management system, at least one computing device comprising a graphical user interface for providing data to the learning management system and outputting data to at least one user of a plurality of users; at least one server configured to: provide the learning management system over the network; communicate with the one or more computing devices; store data associated with the learning management system on at least one storage device that is coupled to the at least one server, the data comprising user data, education provider data, and/or industry data; implement at least one analytics engine, wherein the at least one analytics engine comprises a probabilistic model for recommending learning pathways, the probabilistic model being a trained statistical model configured to analyze and identify data correlations, wherein the at least one analytics engine is configurable to: analyze the data associated with the learning management system; generate personalized data based at least in part on the analyzed data associated with the learning management system, the personalized data being indicative of a personalized learning pathway that enables each user of a plurality of users to build the competencies for a desired career and/or grow an individual's career; and generate at least one recommendation for each user based on statistical analysis, by the probabilistic model, of the personalized data associated with the learning management system, wherein each recommendation is provided on the at least one computing device.
2. 2 . The system of claim 1 , wherein the at least one analytics engine comprises a probabilistic model is configured to recommend the learning pathways based on characteristics pertaining to the individual and historical information pertaining to others that followed similar paths or developed similar competencies.
3. 3 . The system of claim 1 , wherein the data associated with the learning management system further comprises at least one of: (i) user personal profile data; (ii) education provider data; (iii) crowd sourcing tagging of skills and/or competencies; (iv) internet sources using semantic analysis; (v) information pertaining to skill gaps at industry level; and (vi) historical data pertaining to other users that followed similar paths or developed similar competencies.
4. 4 . The system of claim 3 , wherein each user personal profile data comprises role, interests, background education, competencies, and competency gaps of each user.
5. 5 . The system of claim 3 , wherein the education provider data comprises information from education providers including colleges and universities, the information indicating what programs lead into certain skills.
6. 6 . The system of claim 3 , wherein the education provider data comprises information from private education providers including training and certification providers, the information indicating what programs lead into certain skills.
7. 7 . The system of claim 1 , wherein the at least one recommendation includes at least one of: (i) data indicative of one or more courses or development pathways including certificates and degrees; (ii) recommendations for careers; and/or (iii) data indicative of a competency gap between a desired outcome and individual's current competency, and programs or pathways that best overlay the gap.
8. 8 . The system of claim 7 , wherein the server is further configured to generate a recommendation report for each recommendation and provide the recommendation report on the at least one computing device.
9. 9 . A method for analyzing information captured in an electronic learning system, the method comprising: identifying at least one user of a plurality of users associated with a learning management system; providing, over a network, the learning management system to at least one computing device associated with each user associated with the learning management system; communicating, over the network, with the at least one computing device, the at least one computing device comprising a graphical user interface for providing data to the learning management system and outputting data to each user of a plurality of users; storing data associated with the learning management system on at least one storage device that is coupled to at least one server, the data comprising user data, education provider data, and/or industry data; implementing at least one analytics engine, wherein the at least one analytics engine comprises a probabilistic model for recommending learning pathways, the probabilistic model being a trained statistical model configured to analyze and identify data correlations, wherein the at least one analytics engine is configurable to: analyze the data associated with the learning management system; generate personalized data, by the probabilistic model, based at least in part on the analyzed data associated with the learning management system, the personalized data being indicative of a personalized learning pathway that enables each user of a plurality of users to build the competencies for a desired career and/or grow an individual's career; and generate at least one recommendation for each user based on statistical analysis, by the probabilistic model, of the personalized data associated with the learning management system, wherein each recommendation is provided on the at least one computing device.
10. 10 . The method of claim 9 , wherein the at least one analytics engine comprises a probabilistic mode is configured to recommend the learning pathways based on characteristics pertaining to the individual and historical information pertaining to others that followed similar paths or developed similar competencies.
11. 11 . The method of claim 9 , wherein the data associated with the learning management system further comprises at least one of: (i) user personal profile data; (ii) education provider data; (iii) crowd sourcing tagging of skills and/or competencies; (iv) internet sources using semantic analysis; (v) information pertaining to skill gaps at industry level; and (vi) historical data pertaining to other users that followed similar paths or developed similar competencies.
12. 12 . The method of claim 11 , wherein each user personal profile data comprises role, interests, background education, competencies, and competency gaps of each user.
13. 13 . The method of claim 11 , wherein the education provider data comprises information from education providers including colleges and universities, the information indicating what programs lead into certain skills.
14. 14 . The method of claim 11 , wherein the education provider data comprises information from private education providers including training and certification providers, the information indicating what programs lead into certain skills.
15. 15 . The method of claim 9 , wherein the recommendation includes at least one of: (i) data indicative of one or more courses or development pathways including certificates and degrees; (ii) recommendations for careers; and/or (iii) data indicative of a competency gap between desired outcome and individual's current competency, and programs or pathways that best overlay the gap.
16. 16 . The method of claim 15 , further comprising: generating a recommendation report for each recommendation; and providing, over the network, each recommendation report to the at least one computing device of each user.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Patent Application No. 63/304,902 filed on Jan. 31, 2022. The entire contents of U.S. Provisional Patent Application No. 63/304,902 is hereby incorporated herein by reference for all purposes. TECHNICAL FIELD The embodiments herein relate to the field of electronic learning and, in particular, to systems and methods for determining a personalized learning pathway. BACKGROUND Electronic learning (also called e-Learning or eLearning) generally refers to education or learning where users engage in education related activities using computers and other computer devices. For example, users may enroll or participate in a course or program of study offered by an educational institution (e.g., a college, university, or grade school) through a web interface that is accessible over the Internet. Similarly, users may receive assignments electronically, participate in group work and projects by collaborating online, and be graded based on assignments and examinations that are submitted using an electronic dropbox. Electronic learning is not limited to use by educational institutions, however, and may also be used in governments or in corporate environments. For example, employees at a regional branch office of a particular company may use electronic learning to participate in a training course offered by their company's head office without ever physically leaving the branch office. Electronic learning can also be an individual activity with no institution driving the learning. For example, individuals may participate in self-directed study (e.g., studying an electronic textbook or watching a recorded or live webcast of a lecture) that is not associated with a particular institution or organization. Electronic learning often occurs without any face-to-face interaction between the users in the educational community. Accordingly, electronic learning overcomes some of the geographic limitations associated with more traditional learning methods, and may eliminate or greatly reduce travel and relocation requirements imposed on users of educational services. Furthermore, because course materials can be offered and consumed electronically, there are fewer physical restrictions on learning. For example, the number of students that can be enrolled in a particular course may be practically limitless, as there may be no requirement for physical facilities to house the students during lectures. Furthermore, learning materials (e.g., handouts, textbooks, etc.) may be provided in electronic formats so that they can be reproduced for a virtually unlimited number of students. Finally, lectures may be recorded and accessed at varying times (e.g., at different times that are convenient for different users), thus accommodating users with varying schedules, and allowing users to be enrolled in multiple courses that might have a scheduling conflict when offered using traditional techniques. At many stages throughout a user's educational and professional development, decisions may be made pertaining to where to go next and how to get there. For example, toward the end of a standard educational degree, such as high school, undergraduate at a college or university, and the like, students often attempt to identify a career to pursue and/or the competencies required to qualify for such a career. However, it is often the case that students do not know their educational and professional options based on their currently held competencies, nor do they know what competencies are required for a particular career. Similarly, professional users may wish to grow within their current career or investigate other careers that their currently held competencies may qualify them for. However, such users may not know which direction to go, which competencies to build on, and/or which additional competencies are required. These decisions may be substantial and should be made accordingly. They can have significant financial and temporal implications, as well as impact the user's professional options. If chosen incorrectly, these decisions can further lead to low educational and/or professional satisfaction and, in turn, negatively impact one's life satisfaction and mental health. Further, the sunk cost fallacy may tend to keep individuals in an unsatisfactory position due to the resources invested, exacerbating the negative impacts misguided, misinformed, or uninformed educational and professional decisions. Accordingly, the inventors have identified a need for systems, methods, and apparatuses that attempt to address at least some of the above-identified challenges. SUMMARY According to one broad aspect, there is provided an electronic learning system. The electronic learning system may include one or more computing devices that communicate over a network with a learning management system, at least one computing device having a graphical user interface for providing d