Automated course deconstruction into learning units in digital education platforms
View Patent ↗An educational course is automatically deconstructed into discrete learning units. Content related to the course that has been stored by an integrated education platform is analyzed, and distinct concepts are extracted from the content. In addition, the learning activities in which users engage while accessing integrated learning services from the platform are recorded. These activities can generally be divided into passive, active, and recall activities. By deconstructing educational courses into individual concepts, a general model of learning is then applied that connects concepts to the activities undertaken by students to learn those concepts. As a result, a model of learning is developed where courses are atomized into individual learning units, each of which comprises a concept and at least one learning activity. The learning units then can be delivered independently or aggregated as desired.
1. A method for automatically deconstructing an educational course into discrete learning units, the method comprising:
analyzing content related to an educational course stored by an education platform;
extracting distinct concepts from the content;
identifying passive, active, and recall user activities associated with respective distinct concepts, by:
extracting a time at which passive, active, and recall activities are performed by a plurality of users from users activity logs,
normalizing the extracted times by school, and
reporting the normalized extracted times;
generating a plurality of learning units, each learning unit comprising a distinct concept and the passive, active, and recall user activities associated with the distinct concept; and
delivering at least one discrete learning unit to a registered user through the education platform.
2. The method of claim 1 , wherein the content related to the course includes content added by registered users through interactions with the education platform during on-line sessions.
3. The method of claim 1 , wherein passive activities comprise accessing pages of reading material, active activities comprise creating user-generated content, and recall activities comprise answering test questions.
4. The method of claim 1 , wherein a time allotted in a schedule for the learning unit is predicted based on reported activities of a plurality of users.
5. The method of claim 1 , wherein extracting distinct concepts from the content comprises:
extracting combinations of operands and operators that characterize the educational course from course descriptions and academic content material;
forming a plurality of distinct concepts from the combinations of operands and operators; and
indexing the plurality of distinct concepts.
6. The method of claim 1 , wherein identifying passive, active, and recall activities associated with respective distinct concepts comprises:
extracting a time duration for each passive, active, and recall activity from users activity logs;
normalizing the extracted time durations across users; and
reporting the normalized extracted time durations.
7. The method of claim 1 , further comprising:
sorting content related to a course by media type;
processing at least the primary content sources into key phrases;
storing the key phrases in a concept data record; and
analyzing the concept data record to combine concept data into distinct concepts.
8. A non-transitory computer-readable storage medium storing executable computer program instructions for automatically deconstructing an educational course into discrete learning units, the computer program instructions comprising instructions for:
analyzing content related to an educational course stored by an education platform;
extracting distinct concepts from the content;
identifying passive, active, and recall user activities associated with respective distinct concepts, including instructions for:
extracting a time at which passive, active, and recall activities are performed by a plurality of users from users activity logs,
normalizing the extracted times by school, and
reporting the normalized extracted times;
generating a plurality of learning units, each learning unit comprising a distinct concept and the passive, active, and recall user activities associated with the distinct concept; and
delivering at least one discrete learning unit to a registered user through the education platform.
9. The non-transitory computer-readable storage medium of claim 8 , wherein the content related to the course includes content added by registered users through interactions with the education platform during on-line sessions.
10. The non-transitory computer-readable storage medium of claim 8 , wherein passive activities comprise accessing pages of reading material, active activities comprise creating user-generated content, and recall activities comprise answering test questions.
11. The non-transitory computer-readable storage medium of claim 8 , wherein a time allotted in a schedule for the learning unit is predicted based on reported activities of a plurality of users.
12. The non-transitory computer-readable storage medium of claim 8 , wherein the instructions for extracting distinct concepts from the content comprise instructions for:
extracting combinations of operands and operators that characterize the educational course from course descriptions and academic content material;
forming a plurality of distinct concepts from the combinations of operands and operators; and
indexing the plurality of distinct concepts.
13. The non-transitory computer-readable storage medium of claim 8 , wherein the instructions for identifying passive, active, and recall activities associated with respective distinct concepts comprise instructions for:
extracting a time duration for each passive, active, and recall activity from users activity logs;
normalizing the extracted time durations across users; and
reporting the normalized extracted time durations.
14. The non-transitory computer-readable storage medium of claim 8 , the instructions further comprising instructions for:
sorting content related to a course by media type;
processing at least the primary content sources into key phrases;
storing the key phrases in a concept data record; and
analyzing the concept data record to combine concept data into distinct concepts.
15. A method for automatically deconstructing an educational course into discrete learning units, the method comprising:
analyzing content related to an educational course stored by an education platform;
extracting distinct concepts from the content;
identifying passive, active, and recall user activities associated with respective distinct concepts, by:
extracting a time duration for each passive, active, and recall activity from users activity logs,
normalizing the extracted time durations across users, and
reporting the normalized extracted time durations;
generating a plurality of learning units, each learning unit comprising a distinct concept and the passive, active, and recall user activities associated with the distinct concept; and
delivering at least one discrete learning unit to a registered user through the education platform.
16. The method of claim 15 , wherein the content related to the course includes content added by registered users through interactions with the education platform during on-line sessions.
17. The method of claim 15 , wherein passive activities comprise accessing pages of reading material, active activities comprise creating user-generated content, and recall activities comprise answering test questions.
18. The method of claim 15 , wherein a time allotted in a schedule for the learning unit is predicted based on reported activities of a plurality of users.
19. The method of claim 15 , wherein extracting distinct concepts from the content comprises:
extracting combinations of operands and operators that characterize the educational course from course descriptions and academic content material;
forming a plurality of distinct concepts from the combinations of operands and operators; and
indexing the plurality of distinct concepts.
20. The method of claim 15 , wherein identifying passive, active, and recall activities associated with respective distinct concepts comprises:
extracting a time at which passive, active, and recall activities are performed by a plurality of users from users activity logs;
normalizing the extracted times by school; and
reporting the normalized extracted times.
21. The method of claim 15 , further comprising:
sorting content related to a course by media type;
processing at least the primary content sources into key phrases;
storing the key phrases in a concept data record; and
analyzing the concept data record to combine concept data into distinct concepts.
22. A non-transitory computer-readable storage medium storing executable computer program instructions for automatically deconstructing an educational course into discrete learning units, the computer program instructions comprising instructions for:
analyzing content related to an educational course stored by an education platform;
extracting distinct concepts from the content;
identifying passive, active, and recall user activities associated with respective distinct concepts, including instructions for:
extracting a time duration for each passive, active, and recall activity from users activity logs,
normalizing the extracted time durations across users, and
reporting the normalized extracted time durations;
generating a plurality of learning units, each learning unit comprising a distinct concept and the passive, active, and recall user activities associated with the distinct concept; and
delivering at least one discrete learning unit to a registered user through the education platform.
23. The non-transitory computer-readable storage medium of claim 22 , wherein the content related to the course includes content added by registered users through interactions with the education platform during on-line sessions.
24. The non-transitory computer-readable storage medium of claim 22 , wherein passive activities comprise accessing pages of reading material, active activities comprise creating user-generated content, and recall activities comprise answering test questions.
25. The non-transitory computer-readable storage medium of claim 22 , wherein a time allotted in a schedule for the learning unit is predicted based on reported activities of a plurality of users.
26. The non-transitory computer-readable storage medium of claim 22 , wherein the instructions for extracting distinct concepts from the content comprise instructions for:
extracting combinations of operands and operators that characterize the educational course from course descriptions and academic content material;
forming a plurality of distinct concepts from the combinations of operands and operators; and
indexing the plurality of distinct concepts.
27. The non-transitory computer-readable storage medium of claim 22 , wherein the instructions for identifying passive, active, and recall activities associated with respective distinct concepts comprise instructions for:
extracting a time at which passive, active, and recall activities are performed by a plurality of users from users activity logs;
normalizing the extracted times by school; and
reporting the normalized extracted times.
28. The non-transitory computer-readable storage medium of claim 22 , the instructions further comprising instructions for:
sorting content related to a course by media type;
processing at least the primary content sources into key phrases;
storing the key phrases in a concept data record; and
analyzing the concept data record to combine concept data into distinct concepts.