RUBRIC-BASED ASSESSMENT WITH PERSONALIZED LEARNING RECOMMENDATIONS

• US 20100316986A1
• Filed: 06/12/2009
• Published: 12/16/2010
• Est. Priority Date: 06/12/2009
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: de la Chica, Sebastian, Jenkins, Eric A. JR., Alexander, Zubin
• Application Number:
  US12/483,251
• Publication Number:
  US 20100316986A1
• Time in Patent Office: Days
• US Class Current: 434/362
• 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 rubric-based assessment and personalized learning recommendation system and method to aid an educator in teaching an entity in an efficient manner. Embodiments of the system and method include a computational representation of a rubric that is composed of composable rubric constructs. Each composable rubric construct corresponds to a particular sub-area of a skill being learned. Embodiments of the system and method also allow the educator to select a level of granularity of the rubric. This allows grouping together of entities that are having similar problems learning the skill and are performing similarly in certain areas. Embodiments of the system and method can suggest available learning resources for a single or groups of entities struggling in the same or similar areas based on their assessment results. The idea is for the entity to use these learning resources to improve its performance and competency in a given subject area.

Claims

1. A method implemented on a computing device having a processor for creating a personalized pedagogical plan for an entity, comprising:

using the computing device having the processor to perform the following;

generating an initial pedagogical plan to teach the entity a desired skill;

generating a computational representation of a rubric for the skill that provides a benchmark to which a performance of the skill by the entity can be compared;

assessing the performance using the rubric to generate assessment results;

identifying available learning resources that can be used improve the entity'"'"'s performance of the skill; and

recommending at least some of the learning resources based on the assessment results to allow the entity to improve its mastery of the skill.

2. The method of claim 1, further comprising:

selecting composable rubric constructs to use in the assessment of the learning of the skill; and

constructing the rubric using a plurality of the selected composable rubric constructs to generate the computational representation of the rubric.

3. The method of claim 2, further comprising associating multiple rubric performance levels with each of the composable rubric constructs.

4. The method of claim 3, further comprising generating multiple exemplars for each of the rubric performance levels as examples of a performance at a certain rubric performance level.

5. The method of claim 4, further comprising generating the rubric performance levels using the rubric and the exemplars.

6. The method of claim 1, further comprising selecting a granularity of grouping to generate a number of similar learning groups in which entities having similar deficiencies in learning the skill are placed, such that a coarser granularity provides a lesser number of similar learning groups as compared to a finer granularity that provides a greater number of similar learning groups.

7. The method of claim 6, further comprising assigning each entity to one of the similar learning groups based on assessment results for that entity as measured by the rubric.

8. The method of claim 7, further comprising refining the initial pedagogical plan based on the recommended learning resources to generate a refined pedagogical plan.

9. The method of claim 8, further comprising generating a refined pedagogical plan for each of the similar learning groups.

10. A method implemented on a computing device having a processor for assessing performance of an entity in performing a particular skill, comprising:

using the computing device having the processor to perform the following;

generating a rubric for the skill that provided a benchmark of how the entity'"'"'s performance in the skill will be assessed;

defining a composable rubric construct for each sub-area of the skill, where each sub-area corresponds to a discrete portion of the skill;

composing a rubric from each of the composable rubric constructs such that the rubric contains each of the composable rubric constructs; and

assessing the performance of the entity using the rubric to generate assessment results for the entity.

11. The method of claim 10, further comprising generating a plurality of rubric performance levels for each of the composable rubric constructs to aid in assessing the performance of the entity and indicate how well the entity learned the skill.

12. The method of claim 11, further comprising generating exemplars for each of the rubric performance levels to provides examples of a performance by an entity as a particular rubric performance level.

13. The method of claim 12, further comprising using the exemplars and the rubric to construct the rubric performance levels.

14. The method of claim 13, further comprising:

determining available learning resources; and

recommending at least some of the available learning resources to the entity based on the assessment results and the rubric performance levels to generate personalized learning resource recommendations for the entity.

15. The method of claim 14, further comprising:

assessing a performance of several different entities using the rubric to generate a plurality of assessment results for each of the entities in the skill; and

selecting a granularity of grouping to generate a number of similar learning groups, where each similar learning group contains entities that have similar assessment results.

16. The method of claim 15, further comprising assigning each of the entities to one of the similar learning groups based on the assessment results for a particular entity.

17. A computer-implemented method for helping a group of students learn a skill, comprising:

generating an initial pedagogical plan designed to help an educator teach the skill to the group of students;

defining a rubric for the skill by which each student'"'"'s mastery of the skill can be determined;

generating a computational representation of the rubric using composable rubric constructs, where each composable rubric construct corresponds to a sub-area of the skill;

testing a knowledge of the skill of each of the students in the form of a test;

assessing each student'"'"'s performance on the test using the rubric to generate assessment results;

identifying available learning resources that will help each student improve in the skill; and

providing personalized learning resource recommendations of at least some of the available learning resources to each of the students to help each student improve in an area of the skill in which the student is having trouble so as to improve in the skill.

18. The computer-implemented method of claim 17, further comprising:

allowing the educator to select a granularity of grouping desired by the educator to obtain a number of similar learning groups such that a coarser granularity provides fewer similar learning groups and a finer granularity provides more similar learning groups; and

grouping the group of students into the number of similar learning groups based on each student'"'"'s performance on the test such that students having trouble in similar sub-areas of the skill are grouped together in similar learning groups.

19. The computer-implemented method of claim 18, further comprising providing personalized learning resource recommendations to each of the similar learning groups based on the assessment results of each student in a particular similar learning group.

20. The computer-implemented method of claim 19, further comprising refining the initial pedagogical plan to include a refined pedagogical plan for each of the similar learning groups based on the personalized learning resource recommendations for each of the similar learning groups.