Evaluating learning progress and making recommendations in a computerized learning environment

• US 20080254432A1
• Filed: 04/13/2007
• Published: 10/16/2008
• Est. Priority Date: 04/13/2007
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Woolf, Susan D., Moraveji, Neema, O'Kelley, Patrick, Ganapathy, Mythreyee, Pal, Joyojeet
• Application Number:
  US11/786,888
• Publication Number:
  US 20080254432A1
• Time in Patent Office: Days
• US Class Current: 434/327
• 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 7/00 Electrically-operated teaching apparatus or devices working with questions and answers mechanically operated G09B3/00; computing arrangements G06F

Abstract

A computerized learning method is provided. The method may include receiving performance data for a user from at least one of a plurality of learning application programs, at a learning service program. The method may also include updating a learning level of the user based on the performance data from at least one of the plurality of learning applications, and determining a recommendation for the user based on the updated learning level. The recommendation may be displayed on a graphical user interface of the learning service program. The determination of the recommendation may be based on a measured change in a learning level for a group of users with at least one profile element similar to the user. In this manner, effective recommendations for the user may be made.

Claims

1. A computerized learning method, comprising:

receiving performance data for a user from at least one of a plurality of learning application programs, at a learning service program;

updating a learning level and/or other learning data of the user based on the performance data from at least one of the plurality of learning applications; and

determining a recommendation for the user based on the updated learning data.

2. The method of claim 1, wherein the determination of the recommendation is further based on comparisons to patterns of advancement shown by aggregated performance data from other users using the plurality of learning application programs, the other users sharing one or more matching profile attributes with the user.

3. The method of claim 1 further comprising, displaying the recommendation on a graphical user interface of the learning service program.

4. The method of claim 1, further comprising:

updating a learning level for a group of users from a previous learning level at an online learning service program; and

measuring a change in the learning level for the group of users between the previous learning level and the updated learning level attributable to at least a selected one of the plurality of learning application programs.

5. The method of claim 4, wherein the determination of the recommendation is further based on the measured change in the learning level and/or other learning data for the group of users.

6. The method of claim 4, wherein the group of users have at least one matching profile characteristic with the user.

7. The method of claim 6, wherein the matching profile characteristic is received as input from the user or is inferred from user activity by the learning service program.

8. The method of claim 4, wherein the group of users is selected based on native language and target language of study.

9. The method of claim 1, wherein the recommendation indicates content selected from the group consisting of at least one of the plurality of learning application programs, printed publications, electronic publications, websites.

10. The method of claim 1, wherein the recommendation further indicates a particular challenge of a learning application program.

11. The method of claim 1, wherein the recommendation indicates at least two of the plurality of learning application programs;

and wherein the method further comprises;

assigning a recommendation value to each learning application program of the recommendation; and

displaying each learning application program of the recommendation on a graphical user interface;

wherein a relative position of each of the learning application programs on the graphical user interface is based on the assigned recommendation value of each learning application program.

12. The method of claim 1, wherein the recommendation does not include a learning application program that was previously used by the user.

13. A computerized learning system, comprising:

a learning service program configured to receive performance data for a user from at least one of a plurality of learning application programs, the learning service program including;

an assessment engine configured to determine a learning level and/or other learning data about the user based on performance data of the user received from said at least one of the plurality of learning application programs; and

a recommendation engine configured to determine a recommendation for the user based on the learning level and/or other learning data and a profile characteristic of the user.

14. The computerized learning system of claim 13, further comprising a graphical user interface for displaying the recommendation.

15. The computerized learning system of claim 13, further comprising an online learning service for receiving a learning level update for a group of users and for measuring a change between the updated learning level and a previous learning level for the group of users attributable to at least a selected one of the plurality of learning application programs.

16. The computerized learning system of claim 15, wherein the recommendation engine is further configured to determine the recommendation based on the measured change between the updated learning level and the previous learning level for the group of users.

17. The computerized learning system of claim 16, wherein the recommendation indicates a learning application program that is attributed to the greatest measured change between the updated learning level and the previous learning level for the group of users.

18. The computerized learning system of claim 13, wherein the online learning service is configured to determine the group of users based on at least one profile characteristic.

19. The computerized learning system of claim 13, wherein the recommendation determined by the recommendation engine indicates at least one learning application program or other learning content of the plurality of learning application programs or other learning content.

20. A computerized learning system for recommending a learning application program for a user, comprising:

an online learning service configured to;

identify a group of users having at least a similar profile characteristic as the user;

measure a change in a learning level of the group of users attributable to a selected one of a plurality of learning application programs; and

determine a recommendation for the user based on the measured change in the learning level of the group of users, wherein the recommendation indicates the selected one of the plurality of learning application programs attributed to the measured change; and

a learning service program for receiving the recommendation from the online learning service, said learning service program including a graphical user interface for displaying the recommendation.