Creating an abstract concept multi-disciplined learning tool

Abstract

A method for execution by a computing entity to create a multi-disciplined learning tool regarding an abstract environment topic includes creating first-passes of a first and second learning objects for first and second pieces of information regarding the abstract environment topic to include first and second sets of knowledge bullet-points regarding the first and second pieces of information. The method further includes obtaining a synthetic asset based on the first and second set of knowledge bullet-points. The method further includes creating second-passes of the first and second learning objects to further include first and second descriptive assets regarding the first and second pieces of information based on the first and second sets of knowledge bullet-points and the synthetic asset. The method further includes linking the second-passes of the first and second learning objects together to form at least a portion of the multi-disciplined learning tool.

What is claimed is: 
     
         1 . A method for creating a multi-disciplined learning tool regarding an abstract environment topic, the method comprises:
 creating, by a computing entity, a first-pass of a first learning object for a first piece of information regarding the abstract environment topic to include a first set of knowledge bullet-points regarding the first piece of information;   creating, by the computing entity, a first-pass of a second learning object for a second piece of information regarding the abstract environment topic to include a second set of knowledge bullet-points regarding the second piece of information;   obtaining, by the computing entity, a synthetic asset based on the first and second set of knowledge bullet-points, wherein the synthetic asset depicts an abstractive aspect regarding the abstract environment topic pertaining to the first and second pieces of information;   creating, by the computing entity, a second-pass of the first learning object to further include a first descriptive asset regarding the first piece of information based on the first set of knowledge bullet-points and the synthetic asset;   creating, by the computing entity, a second-pass of the second learning object to further include a second descriptive asset regarding the second piece of information based on the second set of knowledge bullet-points and the synthetic asset; and   linking, by the computing entity, the second-passes of the first and second learning objects together to form at least a portion of the multi-disciplined learning tool.   
     
     
         2 . The method of  claim 1 , wherein the obtaining the synthetic asset comprises:
 identifying the abstractive aspect regarding the abstract environment topic based on the first and second pieces of information; and   generating the synthetic asset to represent the first and second set of knowledge bullet-points in accordance with the abstractive aspect regarding the abstract environment topic.   
     
     
         3 . The method of  claim 1  further comprises:
 generating, by the computing entity, a representation of the first descriptive asset; and 
 updating, by the computing entity, the first learning object with the representation of the first descriptive asset to produce the second-pass of the first learning object. 
 
     
     
         4 . The method of  claim 1 , wherein the linking the second-passes of the first and second learning objects together to form the at least the portion of the multi-disciplined learning tool comprises:
 generating index information for the second-passes of first and second learning objects to indicate sharing of the synthetic asset; and   facilitating storage of the index information and the first and second learning objects in a learning assets database to enable subsequent utilization of the multi-disciplined learning tool.   
     
     
         5 . The method of  claim 1 , wherein the creating the second-pass of the first learning object comprises:
 generating a representation of the synthetic asset based on a first knowledge bullet-point of the first set of knowledge bullet-points.   
     
     
         6 . The method of  claim 5  further comprises one or more of:
 generating the first descriptive asset utilizing the representation of the synthetic asset; 
 outputting the representation of the synthetic asset as instructor output information; 
 receiving instructor input information in response to the instructor output information; and 
 interpreting the instructor input information to produce the first descriptive asset. 
 
     
     
         7 . A computing device comprises:
 an interface;   a local memory; and   a processing module operably coupled to the interface and the local memory, wherein the processing module functions to:
 create a first-pass of a first learning object for a first piece of information regarding an abstract environment topic to include a first set of knowledge bullet-points regarding the first piece of information; 
 create a first-pass of a second learning object for a second piece of information regarding the abstract environment topic to include a second set of knowledge bullet-points regarding the second piece of information; 
 obtain a synthetic asset based on the first and second set of knowledge bullet-points, wherein the synthetic asset depicts an abstractive aspect regarding the abstract environment topic pertaining to the first and second pieces of information; 
 create a second-pass of the first learning object to further include a first descriptive asset regarding the first piece of information based on the first set of knowledge bullet-points and the synthetic asset; 
 create a second-pass of the second learning object to further include a second descriptive asset regarding the second piece of information based on the second set of knowledge bullet-points and the synthetic asset; and 
 link the second-passes of the first and second learning objects together to form at least a portion of a multi-disciplined learning tool. 
   
     
     
         8 . The computing device of  claim 7 , wherein the processing module functions to obtain the synthetic asset by:
 identifying the abstractive aspect regarding the abstract environment topic based on the first and second pieces of information; and   generating the synthetic asset to represent the first and second set of knowledge bullet-points in accordance with the abstractive aspect regarding the abstract environment topic.   
     
     
         9 . The computing device of  claim 7 , wherein the processing module further functions to:
 generate a representation of the first descriptive asset; and   update the first learning object with the representation of the first descriptive asset to produce the second-pass of the first learning object.   
     
     
         10 . The computing device of  claim 7 , wherein the processing module functions to link the second-passes of the first and second learning objects together to form the at least the portion of the multi-disciplined learning tool by:
 generating index information for the second-passes of first and second learning objects to indicate sharing of the synthetic asset; and   facilitating storage, via the interface, of the index information and the first and second learning objects in a learning assets database to enable subsequent utilization of the multi-disciplined learning tool.   
     
     
         11 . The computing device of  claim 7 , wherein the processing module functions to create the second-pass of the first learning object by:
 generating a representation of the synthetic asset based on a first knowledge bullet-point of the first set of knowledge bullet-points.   
     
     
         12 . The computing device of  claim 11 , wherein the processing module further functions to:
 generate the first descriptive asset utilizing the representation of the synthetic asset;   output, via the interface, the representation of the synthetic asset as instructor output information;   receive, via the interface, instructor input information in response to the instructor output information; and   interpret the instructor input information to produce the first descriptive asset.   
     
     
         13 . A computer readable memory comprises:
 a first memory element that stores operational instructions that, when executed by a processing module, causes the processing module to:
 create a first-pass of a first learning object for a first piece of information regarding an abstract environment topic to include a first set of knowledge bullet-points regarding the first piece of information; and 
 create a first-pass of a second learning object for a second piece of information regarding the abstract environment topic to include a second set of knowledge bullet-points regarding the second piece of information; 
   a second memory element that stores operational instructions that, when executed by the processing module, causes the processing module to:
 obtain a synthetic asset based on the first and second set of knowledge bullet-points, wherein the synthetic asset depicts an abstractive aspect regarding the abstract environment topic pertaining to the first and second pieces of information; 
   a third memory element that stores operational instructions that, when executed by the processing module, causes the processing module to:
 create a second-pass of the first learning object to further include a first descriptive asset regarding the first piece of information based on the first set of knowledge bullet-points and the synthetic asset; and 
 create a second-pass of the second learning object to further include a second descriptive asset regarding the second piece of information based on the second set of knowledge bullet-points and the synthetic asset; and 
   a fourth memory element that stores operational instructions that, when executed by the processing module, causes the processing module to:
 link the second-passes of the first and second learning objects together to form at least a portion of a multi-disciplined learning tool. 
   
     
     
         14 . The computer readable memory of  claim 13 , wherein the processing module functions to execute the operational instructions stored by the second memory element to cause the processing module to obtain the synthetic asset by:
 identifying the abstractive aspect regarding the abstract environment topic based on the first and second pieces of information; and   generating the synthetic asset to represent the first and second set of knowledge bullet-points in accordance with the abstractive aspect regarding the abstract environment topic.   
     
     
         15 . The computer readable memory of  claim 13  further comprises:
 a fifth memory element that stores operational instructions that, when executed by the processing module, causes the processing module to:
 generate a representation of the first descriptive asset; and 
 update the first learning object with the representation of the first descriptive asset to produce the second-pass of the first learning object. 
 
 
     
     
         16 . The computer readable memory of  claim 13 , wherein the processing module functions to execute the operational instructions stored by the fourth memory element to cause the processing module to link the second-passes of the first and second learning objects together to form the at least the portion of the multi-disciplined learning tool by:
 generating index information for the second-passes of first and second learning objects to indicate sharing of the synthetic asset; and   facilitating storage of the index information and the first and second learning objects in a learning assets database to enable subsequent utilization of the multi-disciplined learning tool.   
     
     
         17 . The computer readable memory of  claim 13 , wherein the processing module functions to execute the operational instructions stored by the third memory element to cause the processing module to create the second-pass of the first learning object by:
 generating a representation of the synthetic asset based on a first knowledge bullet-point of the first set of knowledge bullet-points.   
     
     
         18 . The computer readable memory of  claim 17 , wherein the processing module further functions to execute the operational instructions stored by the third memory element to cause the processing module to:
 generate the first descriptive asset utilizing the representation of the synthetic asset;   output the representation of the synthetic asset as instructor output information;   receive instructor input information in response to the instructor output information; and   interpret the instructor input information to produce the first descriptive asset.