Patent Publication Number: US-2007112703-A1

Title: Adaptive teaching material generation methods and systems

Description:
BACKGROUND OF THE INVENTION  
      1. Field of the Invention  
      The present invention relates to computer techniques, and more particularly to adaptive teaching material generation methods and systems.  
      2. Description of the Related Art  
      The shareable content object reference model (SCORM) 2004 proposed by advanced distributed learning (ADL) adopts the IMS simple sequencing specification, which is absent in the previous version of SCORM 1.2.  
      The IMS simple sequencing specification developed by IMS Global Learning Consortium defines a method for representing the intended behavior of an authored learning experience utilizing extensible markup language (XML). SCORM 2004 is increasingly supported by various authoring tools. Simple sequence editing with current authoring tools, such as Reload 2004, however, is still complex, time-consuming, and difficult work.  
     BRIEF SUMMARY OF THE INVENTION  
      An exemplary embodiment of an adaptive teaching material generation system comprises a database, an authoring tool, and a course structure generator. The database provides a plurality of graphical templates for e-learning sequencing. The authoring tool interface provides graphical templates draggable for forming a structure diagram for sequencing e-learning nodes comprised therein. The course structure generator automatically generates a course structure corresponding to the structure diagram, comprising sequencing settings of each node.  
      An exemplary embodiment of an adaptive teaching material generation method is provided. A plurality of graphical templates for e-learning sequencing is provided draggable for forming a structure diagram for sequencing e-learning nodes. A course structure corresponding to the structure diagram is automatically generated, comprising sequencing settings of each node.  
      The method can be implemented with a computer application recorded in a storage medium such as a memory or a memory device. The computer application, when loaded into a computer, directs the computer to execute the described adaptive teaching material method.  
      A detailed description is given in the following embodiments with reference to the accompanying drawings. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
      The invention can be more fully understood by reading the subsequent detailed description and examples with references made to the accompanying drawings, wherein:  
       FIG. 1  is a block diagram of an exemplary embodiment of an adaptive teaching material generation system;  
       FIG. 2  is a schematic view showing a plurality of graphical templates for e-learning sequencing;  
       FIG. 3  is a schematic view showing a plurality of template objects;  
       FIG. 4  is a schematic view showing interfaces for editing course structures and content;  
       FIG. 5  is a schematic diagram of an example of a course structure;  
       FIG. 6  is an example of structure codes of the course structure;  
       FIG. 7  is a schematic diagram of exemplary course content generated by a course generator;  
       FIG. 8  is an XML file of a course;  
       FIG. 9  is a flowchart of an exemplary embodiment of an adaptive teaching material generation method; and  
       FIG. 10  is a schematic diagram of a storage medium. 
    
    
     DETAILED DESCRIPTION OF THE INVENTION  
      The following description is of the best-contemplated mode of carrying out the invention. This description is made for the purpose of illustrating the general principles of the invention and should not be taken in a limiting sense. The scope of the invention is best determined by reference to the appended claims.  
      In  FIG. 1 , adaptive teaching material generation system  100  comprises a sequencing database  1 , authoring tool  2 , course structure generator  3 , course generator  4 , and packager  5 . System  100  is coupled to display  6 .  
      With reference to an adaptive teaching material generation method shown in  FIG. 9 , authoring tool  2  and course generator  4  display modules for course structure and content editing (step S 2 ).  
      Sequencing database  1  provides a plurality of graphical templates for sequencing e-learning experiences. Each template comprises three parts: &lt;title&gt;, &lt;item&gt;, and &lt;imsss:sequencing&gt;, wherein &lt;imsss:sequencing&gt; comprises sequencing settings, such as settings of “controlMode”, “sequencingRules”, “limitCondition”, “auxiliaryResources”, “rollupRules”, “objectives”, “randomizationControls”, “deliveryControls”, “constrainedChoiceConsiderations”, and “rollupConsiderations”.  
      The graphical templates for e-learning sequencing comprise five types of templates: serial learning; selective learning; looped learning; random learning; and smart sequencing. As shown in  FIG. 2 , authoring tool  2  may display serial learning templates  21 , selective learning templates  22 , looped learning templates  23 , random learning templates  24 , and smart sequencing template  25  on area  61  of display  6 , respectively corresponding to these five types (step S 2 ). Serial learning template  21  comprises learning nodes  211  and  212 . When learning node  211  has been opened by a learner, learning node  212  can be successively opened. Exemplary sequencing settings of serial learning template  21  is provides as follows:  
      &lt;imsss:controlMode flow=“true”&gt; 
      Selective learning template  22  comprises learning nodes  221 ,  222  and  223 . When learning node  221  has been opened by a learner, learning node  222  or  223  can be selected to open. Exemplary sequencing settings of selective learning template  22  is provided as follows:  
      &lt;imsss:controlMode choice=“true”&gt; 
      Looped learning templates  23  comprise learning nodes  231  and  232 . When learning node  231  has been opened by a learner, learning node  232  can be successively opened. When learning node  232  has been opened by the learner, learning node  231  can be repeatedly opened based on the learning status of the learner, which is stored in a corresponding data model. Exemplary sequencing settings of looped learning template  23  is provides as follows:  
                                                  &lt;imsss:postConditionRule&gt;            &lt;imsss:ruleConditions&gt;             &lt;imsss:ruleCondition condition=“satisfied” /&gt;            &lt;/imsss:ruleConditions&gt;            &lt;imsss:ruleAction action=“retryAll” /&gt;           &lt;/ imsss:postConditionRule &gt;                      
 
      Random learning templates  24  comprises learning nodes  241 ˜ 244 . When learning node  241  has been opened by a learner, learning node  242 ,  243 , or  244  can be opened randomly. Exemplary sequencing settings of random learning template  24  is provides as follows:  
                                                  &lt;imsss:randomizationControls           randomizationTiming = “onEachNewAttempt”           reorderChildren = “true” selectCount=“1”           selectionTiming=“onEachNewAttempt” /&gt;                      
 
      Smart sequencing template  25  comprises learning nodes  251  and  252 . When a test question in learning node  251  has been answered by a learner, whether learning node  252  next to learning node  251  is to be opened is determined based on the answer to the test question. Exemplary sequencing settings of learning node  251  is provides as follows:  
                                   &lt;imsss:objectives&gt;         &lt;imsss:primaryObjective objectiveID=“PRIMARYOBJ”          satisfiedByMeasure=“true”&gt;        &lt;imsss:minNormalizedMeasure&gt;0.6&lt;/imsss:minNormalizedMeasure&gt;         &lt;imsss:mapInfo targetObjectiveID=“obj_module_1“         writeSatisfiedStatus=“true” writeNormalizedMeasure=“true” /&gt;         &lt;/imsss:primaryObjective&gt;        &lt;/imsss:objectives&gt;        &lt;imsss:controlMode flow = “true”&gt;        Exemplary sequencing settings of learning node 252 is provides        as follows:        &lt;imsss:objectives&gt;         &lt;imsss:primaryObjective    objectiveID=“PRIMARYOBJ”       satisfiedByMeasure=“true”&gt;        &lt;imsss:minNormalizedMeasure&gt;0.6&lt;/imsss:minNormalizedMeasure&gt;         &lt;imsss:mapInfo targetObjectiveID=“obj_module_1“&gt;         &lt;/imsss:primaryObjective&gt;        &lt;/imsss:objectives&gt;        &lt;imsss:preConditionRule&gt;         &lt;imsss:ruleConditions&gt;         &lt;imsss:ruleCondition condition=“satisfied” /&gt;         &lt;/imsss:ruleConditions&gt;         &lt;imsss:ruleAction action=“skip” /&gt;        &lt;/ imsss:preConditionRule &gt;                  
 
      Smart sequencing template  26  comprises learning nodes  260 ˜ 262 . When a test question in learning node  260  has been answered by a learner, whether learning node  261  next to learning node  260  is to be opened is determined based on the answer to the test question. Learning node  262  is opened after learning node  261 . After the learner is tested in learning node  262 , whether learning node  261  is to be opened again is determined based on the answer in learning node  262 . Learning nodes  251  and  260  is referred to as pretests. Learning node  262  is referred to as a posttest.  
      Authoring tool  2  provides interfaces allowing the graphical templates to be dragged to form a course structure diagram for sequencing e-learning nodes, and further modify the course structure diagram, such as learning node insertion and sequencing settings adjustment (step S 4 ).  
      For example, with reference to  FIG. 3 , when serial learning template  21  is dragged to area  62 , a corresponding template object  21 A is generated. Similarly, template objects  22 A and  22 B are generated by dragging selective learning template  22  to area  62 . Template object  26 A is generated by dragging smart sequencing template  26  to area  62 . Next, template object  22 A is dragged into learning node  212 A; template object  22 B into learning node  261 A; template object  26 A into learning node  211 A. The structure diagram of template object  21 A shown in  FIG. 4  is generated via the dragging operations and may be further modified. For example, the titles of template objects  21 A,  26 A,  22 B, and  22 A (delimited by &lt;title&gt; tags) may be “Course”, “Chapter 1”, “Text”, and “Chapter 2” respectively. The titles of learning node  260  and  261 A may be “Pretest” and “Posttest” respectively. The titles of learning node  221 B and  222 B may be “Part one” and “Part two” respectively. The titles of learning node  221 A and  222 A may be “Part three” and “Part four” respectively. Authoring tool  2  may transform the course structure diagram to course structure diagram  200  in  FIG. 5 .  
      With reference to  FIGS. 1 and 4 , for each learning node within the course structure diagram, course generator  4  generates a course and course content corresponding thereto (step S 6 ) and associates the course to a corresponding learning node thereof on the course structure diagram. For example, course generator  4  provides interface  64  for editing learning node  262 A as a posttest. Course generator  4  provides control elements in area  63 . Title  641  and button  643  are generated by dragging control elements  631  and  633 . Questions  642 A and  642 B are generated by dragging control element  632 . Course generator  4  may generate courses utilizing the techniques disclosed by an U.S. PAT application entitled “SYSTEMS AND METHODS FOR ESTABLISHING EDUCATION WEB PAGE TEMPLATES”.  
      Course generator  4  provides table  65  for configuring attributes of question  642 B, comprising allocated points and a corresponding key learning point thereof (step S 8 ). A key learning point (such as obj_module_ 1  in  FIG. 4 ) corresponds to a learning concept. For example, key point obj_module_ 1  may correspond to a learning concept included in another course within the course structure. Each course and each test question may correspond to one or more key points. Different test question or different courses may correspond to the same key point.  
      Sequencing rules associated with the key point are edited utilizing interfaces provided by authoring tool  2  (step S 10 ). Course structure generator  3  and course generator  4  respectively generate course structure and course content (step S 12 ).  
      Course structure generator  3  associates the course structure diagram with corresponding sequencing settings and outputs course structure in the form of an XML file. The course structure comprises structure codes and sequencing settings of learning nodes. The structure codes describe the organization of learning nodes. Sequencing settings corresponding to each learning node and each template in the course structure diagram may be referred to the described examples. Note that course structure generator  3  adjusts sequencing settings according to modification on the course structure diagram. Simplified structure codes of course structure diagram  200  are shown in  FIG. 6 . Blocks  70 ˜ 73  respectively correspond to template objects  21 A,  26 A,  22 B, and  22 A. Sequencing settings of each template object are included in the corresponding block thereof. The corresponding sequencing settings are previously described and thus areomitted in the schematic diagram. Course structure generator  3  automatically generates a course structure corresponding to the course structure diagram, which comprises sequencing settings of each node in the course structure diagram.  
      Course generator  4  generates course content of learning node  262 A, comprising HTML code and program code, such as javascript code, according to interface  64  and table  65 .  
      With reference to  FIG. 7 , course generator  4  generates course content  80  of learning node  262 A according to settings on interface  64  and table  65 . Block  82  comprises functions to calculate scores. Block  83  comprises program code of test questions. Block  81  comprises a learning status function configuring learning status based on answers to test questions. For example, the learning status function configures data model indexOfFirstObjective based on key learning point obj_module_ 1  corresponding thereto. Parameter set1Status of key point obj_module_ 1  reflects the answer to question  642 B. Sequencing settings of the course structure may direct the learning order of the learning nodes based on the data model.  
      At last, packager  5  packs the course structure and the course content generated by course structure generator  3  and course generator  4  into a course unit (step S 14 ).  FIG. 8  shows an XML file of a course unit  9 , comprising course structure and course content  80  of block  70 . A learning node of the course structure is associated with resources (denoted by &lt;resource&gt;) of course content  80  through identification “S — 00001”.  
      As shown in  FIG. 10 , adaptive teaching material generation system  100  can be implemented by a computer program stored in storage medium  300 . When storage medium  300  is loaded to computer  400 , computer  400  executes the adaptive teaching material generation method. Storage medium  300  may comprise a disc drive, a hard disk, a flash memory, or another storage device.  
      Thus, adaptive teaching material generation system  100  provides sequencing templates for forming a course structure diagram by dragging operations, generates a course structure accordingly, and provides an interface for setting key points of learning in the aspect of course authoring. Adaptive teaching material generation system  100  finally generates course content and packages the course structure and the course content.  
      While the invention has been described by way of example and in terms of the preferred embodiments, it is to be understood that the invention is not limited to the disclosed embodiments. To the contrary, it is intended to cover various modifications and similar arrangements (as would be apparent to those skilled in the art). Therefore, the scope of the appended claims should be accorded the broadest interpretation so as to encompass all such modifications and similar arrangements.