Patent Publication Number: US-11651700-B2

Title: Assessing learning session retention utilizing a multi-disciplined learning tool

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     The present U.S. Utility Patent Application claims priority pursuant to 35 U.S.C. § 119(e) to U.S. Provisional Application No. 62/858,647, entitled “GENERATING AND EXECUTING A LEARNING EXPERIENCE,” filed Jun. 7, 2019, which is hereby incorporated herein by reference in its entirety and made part of the present U.S. Utility Patent Application for all purposes. 
    
    
     STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT 
     Not Applicable. 
     INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC 
     Not Applicable. 
     BACKGROUND OF THE INVENTION 
     Technical Field of the Invention 
     This invention relates generally to computer systems and more particularly to computer systems providing educational, training, and entertainment content. 
     Description of Related Art 
     Computer systems communicate data, process data, and/or store data. Such computer systems include computing devices that range from wireless smart phones, laptops, tablets, personal computers (PC), work stations, personal three-dimensional (3-D) content viewers, and video game devices, to data centers where data servers store and provide access to digital content. Some digital content is utilized to facilitate education, training, and entertainment. Examples of visual content includes electronic books, reference materials, training manuals, classroom coursework, lecture notes, research papers, images, video clips, sensor data, reports, etc. 
     A variety of educational systems utilize educational tools and techniques. For example, an educator delivers educational content to students via an education tool of a recorded lecture that has built-in feedback prompts (e.g., questions, verification of viewing, etc.). The educator assess a degree of understanding of the educational content and/or overall competence level of a student from responses to the feedback prompts. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S) 
         FIG.  1    is a schematic block diagram of an embodiment of a computing system in accordance with the present invention; 
         FIG.  2 A  is a schematic block diagram of an embodiment of a computing entity of a computing system in accordance with the present invention; 
         FIG.  2 B  is a schematic block diagram of an embodiment of a computing device of a computing system in accordance with the present invention; 
         FIG.  3    is a schematic block diagram of another embodiment of a computing device of a computing system in accordance with the present invention; 
         FIG.  4    is a schematic block diagram of an embodiment of an environment sensor module of a computing system in accordance with the present invention; 
         FIG.  5 A  is a schematic block diagram of another embodiment of a computing system in accordance with the present invention; 
         FIG.  5 B  is a schematic block diagram of an embodiment of a representation of a learning experience in accordance with the present invention; 
         FIG.  6    is a schematic block diagram of another embodiment of a representation of a learning experience in accordance with the present invention; 
         FIG.  7 A  is a schematic block diagram of another embodiment of a computing system in accordance with the present invention; 
         FIG.  7 B  is a schematic block diagram of another embodiment of a representation of a learning experience in accordance with the present invention; 
         FIGS.  8 A- 8 C  are schematic block diagrams of another embodiment of a computing system illustrating an example of creating a learning experience in accordance with the present invention; 
         FIG.  8 D  is a logic diagram of an embodiment of a method for creating a learning experience within a computing system in accordance with the present invention; 
         FIGS.  8 E,  8 F,  8 G,  8 H,  8 J, and  8 K  are schematic block diagrams of another embodiment of a computing system illustrating another example of creating a learning experience within the computing system in accordance with the present invention; 
         FIGS.  9 A- 9 B  are schematic block diagrams of an embodiment of an instruction module of a computing system illustrating an example of creating a learning experience assessment in accordance with the present invention; 
         FIG.  9 C  is a logic diagram of an embodiment of a method for creating a learning experience assessment within a computing system in accordance with the present invention; 
         FIGS.  9 D,  9 E,  9 F, and  9 G  are schematic block diagrams of another embodiment of a computing system illustrating another example of creating a learning experience assessment within the computing system in accordance with the present invention; 
         FIGS.  10 A- 10 B  are schematic block diagrams of an embodiment of an experience execution module of a computing system illustrating an example of executing a learning experience in accordance with the present invention; 
         FIG.  10 C  is a logic diagram of an embodiment of a method for executing a learning experience within a computing system in accordance with the present invention; 
         FIGS.  10 D,  10 E,  10 F,  10 G, and  10 H  are schematic block diagrams of another embodiment of a computing system illustrating another example of executing a learning experience within the computing system in accordance with the present invention; 
         FIGS.  11 A- 11 B  are schematic block diagrams of another embodiment of an experience execution module of a computing system illustrating an example of assessing execution of a learning experience in accordance with the present invention; 
         FIG.  11 C  is a logic diagram of an embodiment of a method for assessing execution of a learning experience within a computing system in accordance with the present invention; 
         FIGS.  11 D and  11 E  are schematic block diagrams of another embodiment of a computing system illustrating another example of assessing learning experience retention within the computing system in accordance with the present invention; 
         FIGS.  12 A- 12 B  are schematic block diagrams of another embodiment of an experience execution module of a computing system illustrating another example of executing a learning experience in accordance with the present invention; 
         FIG.  12 C  is a logic diagram of another embodiment of a method for executing a learning experience within a computing system in accordance with the present invention; 
         FIGS.  12 D- 12 G  are schematic block diagrams of another embodiment of a computing system illustrating an example of improving learning comprehension of a lesson within the computing system in accordance with the present invention; 
         FIGS.  13 A- 13 B  are schematic block diagrams of another embodiment of an experience execution module of a computing system illustrating an example of sharing an assessment of a learning experience in accordance with the present invention; 
         FIG.  13 C  is a schematic block diagram of another embodiment of a computing system illustrating an example of sharing an assessment of a learning experience in accordance with the present invention; 
         FIG.  13 D  is a schematic block diagram of an embodiment of a transcript information blockchain of a computing system illustrating an example of sharing an assessment of a learning experience in accordance with the present invention; 
         FIG.  13 E  is a logic diagram of an embodiment of a method for sharing an assessment of a learning experience within a computing system in accordance with the present invention; 
         FIG.  14 A  is a schematic block diagram of another embodiment of an experience creation module of a computing system illustrating an example of creating a lesson in accordance with the present invention; 
         FIG.  14 B  is a schematic block diagram of another embodiment of a representation of a learning experience in accordance with the present invention; 
         FIG.  14 C  is a logic diagram of an embodiment of a method for creating a lesson within a computing system in accordance with the present invention; 
         FIGS.  15 A- 15 B  are schematic block diagrams of another embodiment of an experience creation module of a computing system illustrating an example of creating a lesson in accordance with the present invention; 
         FIG.  15 C  is a logic diagram of another embodiment of a method for creating a lesson within a computing system in accordance with the present invention; 
         FIGS.  16 A- 16 B  are schematic block diagrams of another embodiment of an experience creation module of a computing system illustrating an example of creating a lesson in accordance with the present invention; 
         FIG.  16 C  is a logic diagram of another embodiment of a method for creating a lesson within a computing system in accordance with the present invention; 
         FIGS.  17 A- 17 B  are schematic block diagrams of another embodiment of an experience execution module of a computing system illustrating another example of executing a learning experience in accordance with the present invention; and 
         FIG.  17 C  is a logic diagram of another embodiment of a method for executing a learning experience within a computing system in accordance with the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
       FIG.  1    is a schematic block diagram of an embodiment of a computing system  10  that includes a real world environment  12 , an environment sensor module  14 , and environment model database  16 , a human interface module  18 , and a computing entity  20 . The real-world environment  12  includes places  22 , objects  24 , instructors  26 - 1  through  26 -N, and learners  28 - 1  through  28 -N. The computing entity  20  includes an experience creation module  30 , an experience execution module  32 , and a learning assets database  34 . 
     The places  22  includes any area. Examples of places  22  includes a room, an outdoor space, a neighborhood, a city, etc. The objects  24  includes things within the places. Examples of objects  24  includes people, equipment, furniture, personal items, tools, and representations of information (i.e., video recordings, audio recordings, captured text, etc.). The instructors includes any entity (e.g., human or human proxy) imparting knowledge. The learners includes entities trying to gain knowledge and may temporarily serve as an instructor. 
     In an example of operation of the computing system  10 , the experience creation module  30  receives environment sensor information  38  from the environment sensor module  14  based on environment attributes  36  from the real world environment  12 . The environment sensor information  38  includes time-based information (e.g., static snapshot, continuous streaming) from environment attributes  36  including XYZ position information, place information, and object information (i.e., background, foreground, instructor, learner, etc.). The XYZ position information includes portrayal in a world space industry standard format (e.g., with reference to an absolute position). 
     The environment attributes  36  includes detectable measures of the real-world environment  12  to facilitate generation of a multi-dimensional (e.g., including time) representation of the real-world environment  12  in a virtual reality and/or augmented reality environment. For example, the environment sensor module  14  produces environment sensor information  38  associated with a medical examination room and a subject human patient (e.g., an MM). The environment sensor module  14  is discussed in greater detail with reference to  FIG.  4   . 
     Having received the environment sensor information  38 , the experience creation module  30  accesses the environment model database  16  to recover modeled environment information  40 . The modeled environment information  40  includes a synthetic representation of numerous environments (e.g., model places and objects). For example, the modeled environment information  40  includes a 3-D representation of a typical human circulatory system. The models include those that are associated with certain licensing requirements (e.g., copyrights, etc.). 
     Having received the modeled environment information  40 , the experience creation module  30  receives instructor information  44  from the human interface module  18 , where the human interface module  18  receives human input/output (I/O)  42  from instructor  26 - 1 . The instructor information  44  includes a representation of an essence of communication with a participant instructor. The human I/O  42  includes detectable fundamental forms of communication with humans or human proxies. The human interface module  18  is discussed in greater detail with reference to  FIG.  3   . 
     Having received the instructor information  44 , the experience creation module  30  interprets the instructor information  44  to identify aspects of a learning experience. A learning experience includes numerous aspects of an encounter between one or more learners and an imparting of knowledge within a representation of a learning environment that includes a place, multiple objects, and one or more instructors. The learning experience further includes an instruction portion (e.g., acts to impart knowledge) and an assessment portion (e.g., further acts and/or receiving of learner input) to determine a level of comprehension of the knowledge by the one or more learners. The learning experience still further includes scoring of the level of comprehension and tallying multiple learning experiences to facilitate higher-level competency accreditations (e.g., certificates, degrees, licenses, training credits, experiences completed successfully, etc.). 
     As an example of the interpreting of the instructor information  44 , the experience creation module  30  identifies a set of concepts that the instructor desires to impart upon a learner and a set of comprehension verifying questions and associated correct answers. The experience creation module  30  further identifies step-by-step instructor annotations associated with the various objects within the environment of the learning experience for the instruction portion and the assessment portion. For example, the experience creation module  30  identifies positions held by the instructor  26 - 1  as the instructor narrates a set of concepts associated with the subject patient circulatory system. As a further example, the experience creation module  30  identifies circulatory system questions and correct answers posed by the instructor associated with the narrative. 
     Having interpreted the instructor information  44 , the experience creation module  30  renders the environment sensor information  38 , the modeled environment information  40 , and the instructor information  44  to produce learning assets information  48  for storage in the learning assets database  34 . The learning assets information  48  includes all things associated with the learning experience to facilitate subsequent recreation. Examples includes the environment, places, objects, instructors, learners, assets, recorded instruction information, learning evaluation information, etc. 
     Execution of a learning experience for the one or more learners includes a variety of approaches. A first approach includes the experience execution module  32  recovering the learning assets information  48  from the learning assets database  34 , rendering the learning experience as learner information  46 , and outputting the learner information  46  via the human interface module  18  as further human I/O  42  to one or more of the learners  28 - 1  through  28 -N. The learner information  46  includes information to be sent to the one or more learners and information received from the one or more learners. For example, the experience execution module  32  outputs learner information  46  associated with the instruction portion for the learner  28 - 1  and collects learner information  46  from the learner  28 - 1  that includes submitted assessment answers in response to assessment questions of the assessment portion communicated as further learner information  46  for the learner  28 - 1 . 
     A second approach includes the experience execution module  32  rendering the learner information  46  as a combination of live streaming of environment sensor information  38  from the real-world environment  12  along with an augmented reality overlay based on recovered learning asset information  48 . For example, a real world subject human patient in a medical examination room is live streamed as the environment sensor information  38  in combination with a prerecorded instruction portion from the instructor  26 - 1 . 
       FIG.  2 A  is a schematic block diagram of an embodiment of the computing entity  20  of the computing system  10 . The computing entity  20  includes one or more computing devices  100 - 1  through  100 -N. A computing device is any electronic device that communicates data, processes data, represents data (e.g., user interface) and/or stores data. 
     Computing devices include portable computing devices and fixed computing devices. Examples of portable computing devices include an embedded controller, a smart sensor, a social networking device, a gaming device, a smart phone, a laptop computer, a tablet computer, a video game controller, and/or any other portable device that includes a computing core. Examples of fixed computing devices includes a personal computer, a computer server, a cable set-top box, a fixed display device, an appliance, and industrial controller, a video game counsel, a home entertainment controller, a critical infrastructure controller, and/or any type of home, office or cloud computing equipment that includes a computing core. 
       FIG.  2 B  is a schematic block diagram of an embodiment of a computing device  100  of the computing system  10  that includes one or more computing cores  52 - 1  through  52 -N, a memory module  102 , the human interface module  18 , the environment sensor module  14 , and an I/O module  104 . In alternative embodiments, the human interface module  18 , the environment sensor module  14 , the I/O module  104 , and the memory module  102  may be standalone (e.g., external to the computing device). An embodiment of the computing device  100  will be discussed in greater detail with reference to  FIG.  3   . 
       FIG.  3    is a schematic block diagram of another embodiment of the computing device  100  of the computing system  10  that includes the human interface module  18 , the environment sensor module  14 , the computing core  52 - 1 , the memory module  102 , and the I/O module  104 . The human interface module  18  includes one or more visual output devices  74  (e.g., video graphics display, 3-D viewer, touchscreen, LED, etc.), one or more visual input devices  80  (e.g., a still image camera, a video camera, a 3-D video camera, photocell, etc.), and one or more audio output devices  78  (e.g., speaker(s), headphone jack, a motor, etc.). The human interface module  18  further includes one or more user input devices  76  (e.g., keypad, keyboard, touchscreen, voice to text, a push button, a microphone, a card reader, a door position switch, a biometric input device, etc.) and one or more motion output devices  106  (e.g., servos, motors, lifts, pumps, actuators, anything to get real-world objects to move). 
     The computing core  52 - 1  includes a video graphics module  54 , one or more processing modules  50 - 1  through  50 -N, a memory controller  56 , one or more main memories  58 - 1  through  58 -N (e.g., RAM), one or more input/output (I/O) device interface modules  62 , an input/output (I/O) controller  60 , and a peripheral interface  64 . A processing module is as defined at the end of the detailed description. 
     The memory module  102  includes a memory interface module  70  and one or more memory devices, including flash memory devices  92 , hard drive (HD) memory  94 , solid state (SS) memory  96 , and cloud memory  98 . The cloud memory  98  includes an on-line storage system and an on-line backup system. 
     The I/O module  104  includes a network interface module  72 , a peripheral device interface module  68 , and a universal serial bus (USB) interface module  66 . Each of the I/O device interface module  62 , the peripheral interface  64 , the memory interface module  70 , the network interface module  72 , the peripheral device interface module  68 , and the USB interface modules  66  includes a combination of hardware (e.g., connectors, wiring, etc.) and operational instructions stored on memory (e.g., driver software) that are executed by one or more of the processing modules  50 - 1  through  50 -N and/or a processing circuit within the particular module. 
     The I/O module  104  further includes one or more wireless location modems  84  (e.g., global positioning satellite (GPS), Wi-Fi, angle of arrival, time difference of arrival, signal strength, dedicated wireless location, etc.) and one or more wireless communication modems  86  (e.g., a cellular network transceiver, a wireless data network transceiver, a Wi-Fi transceiver, a Bluetooth transceiver, a 315 MHz transceiver, a zig bee transceiver, a 60 GHz transceiver, etc.). The I/O module  104  further includes a telco interface  108  (e.g., to interface to a public switched telephone network), a wired local area network (LAN)  88  (e.g., optical, electrical), and a wired wide area network (WAN)  90  (e.g., optical, electrical). The I/O module  104  further includes one or more peripheral devices (e.g., peripheral devices  1 -P) and one or more universal serial bus (USB) devices (USB devices  1 -U). In other embodiments, the computing device  100  may include more or less devices and modules than shown in this example embodiment. 
       FIG.  4    is a schematic block diagram of an embodiment of the environment sensor module  14  of the computing system  10  that includes a sensor interface module  120  to output environment sensor information  150  based on information communicated with a set of sensors. The set of sensors includes a visual sensor  122  (e.g., to the camera, 3-D camera, 360° view camera, a camera array, an optical spectrometer, etc.) and an audio sensor  124  (e.g., a microphone, a microphone array). The set of sensors further includes a motion sensor  126  (e.g., a solid-state Gyro, a vibration detector, a laser motion detector) and a position sensor  128  (e.g., a Hall effect sensor, an image detector, a GPS receiver, a radar system). 
     The set of sensors further includes a scanning sensor  130  (e.g., CAT scan, MRI, x-ray, ultrasound, radio scatter, particle detector, laser measure, further radar) and a temperature sensor  132  (e.g., thermometer, thermal coupler). The set of sensors further includes a humidity sensor  134  (resistance based, capacitance based) and an altitude sensor  136  (e.g., pressure based, GPS-based, laser-based). 
     The set of sensors further includes a biosensor  138  (e.g., enzyme, immuno, microbial) and a chemical sensor  140  (e.g., mass spectrometer, gas, polymer). The set of sensors further includes a magnetic sensor  142  (e.g., Hall effect, piezo electric, coil, magnetic tunnel junction) and any generic sensor  144  (e.g., including a hybrid combination of two or more of the other sensors). 
       FIG.  5 A  is a schematic block diagram of another embodiment of a computing system that includes the environment model database  16 , the human interface module  18 , the instructor  26 - 1 , the experience creation module  30 , and the learning assets database  34  of  FIG.  1   . In an example of operation, the experience creation module  30  obtains modeled environment information  40  from the environment model database  16  and renders a representation of an environment and objects of the modeled environment information  40  to output as instructor output information  160 . The human interface module  18  transforms the instructor output information  160  into human output  162  for presentation to the instructor  26 - 1 . For example, the human output  162  includes a 3-D visualization and stereo audio output. 
     In response to the human output  162 , the human interface module  18  receives human input  164  from the instructor  26 - 1 . For example, the human input  164  includes pointer movement information and human speech associated with a lesson. The human interface module  18  transforms the human input  164  into instructor input information  166 . The instructor input information  166  includes one or more of representations of instructor interactions with objects within the environment and explicit evaluation information (e.g., questions to test for comprehension level, and correct answers to the questions). 
     Having received the instructor input information  166 , the experience creation module  30  renders a representation of the instructor input information  166  within the environment utilizing the objects of the modeled environment information  40  to produce learning asset information  48  for storage in the learnings assets database  34 . Subsequent access of the learning assets information  48  facilitates a learning experience. 
       FIG.  5 B  is a schematic block diagram of an embodiment of a representation of a learning experience that includes a virtual place  168  and a resulting learning objective  170 . A learning objective represents a portion of an overall learning experience, where the learning objective is associated with at least one major concept of knowledge to be imparted to a learner. The major concept may include several sub-concepts. The makeup of the learning objective is discussed in greater detail with reference to  FIG.  6   . 
     The virtual place  168  includes a representation of an environment (e.g., a place) over a series of time intervals (e.g., time 0-N). The environment includes a plurality of objects  24 - 1  through  24 -N. At each time reference, the positions of the objects can change in accordance with the learning experience. For example, the instructor  26 - 1  of  FIG.  5 A  interacts with the objects to convey a concept. The sum of the positions of the environment and objects within the virtual place  168  is wrapped into the learning objective  170  for storage and subsequent utilization when executing the learning experience. 
       FIG.  6    is a schematic block diagram of another embodiment of a representation of a learning experience that includes a plurality of modules  1 -N. Each module includes a set of lessons  1 -N. Each lesson includes a plurality of learning objectives  1 -N. The learning experience typically is played from left to right where learning objectives are sequentially executed in lesson  1  of module  1  followed by learning objectives of lesson  2  of module  1  etc. 
     As learners access the learning experience during execution, the ordering may be accessed in different ways to suit the needs of the unique learner based on one or more of preferences, experience, previously demonstrated comprehension levels, etc. For example, a particular learner may skip over lesson  1  of module  1  and go right to lesson  2  of module  1  when having previously demonstrated competency of the concepts associated with lesson  1 . 
     Each learning objective includes indexing information, environment information, asset information, instructor interaction information, and assessment information. The index information includes one or more of categorization information, topics list, instructor identification, author identification, identification of copyrighted materials, keywords, concept titles, prerequisites for access, and links to related learning objectives. 
     The environment information includes one or more of structure information, environment model information, background information, identifiers of places, and categories of environments. The asset information includes one or more of object identifiers, object information (e.g., modeling information), asset ownership information, asset type descriptors (e.g., 2-D, 3-D). Examples include models of physical objects, stored media such as videos, scans, images, digital representations of text, digital audio, and graphics. 
     The instructor interaction information includes representations of instructor annotations, actions, motions, gestures, expressions, eye movement information, facial expression information, speech, and speech inflections. The content associated with the instructor interaction information includes overview information, speaker notes, actions associated with assessment information, (e.g., pointing to questions, revealing answers to the questions, motioning related to posing questions) and conditional learning objective execution ordering information (e.g., if the learner does this then take this path, otherwise take another path). 
     The assessment information includes a summary of desired knowledge to impart, specific questions for a learner, correct answers to the specific questions, multiple-choice question sets, and scoring information associated with writing answers. The assessment information further includes historical interactions by other learners with the learning objective (e.g., where did previous learners look most often within the environment of the learning objective, etc.), historical responses to previous comprehension evaluations, and actions to facilitate when a learner responds with a correct or incorrect answer (e.g., motion stimulus to activate upon an incorrect answer to increase a human stress level). 
       FIG.  7 A  is a schematic block diagram of another embodiment of a computing system that includes the learning assets database  34 , the experience execution module  32 , the human interface module  18 , and the learner  28 - 1  of  FIG.  1   . In an example of operation, the experience execution module  32  recovers learning asset information  48  from the learning assets database  34  (e.g., in accordance with a selection by the learner  28 - 1 ). The experience execution module  32  renders a group of learning objectives associated with a common lesson within an environment utilizing objects associated with the lesson to produce learner output information  172 . The learner output information  172  includes a representation of a virtual place and objects that includes instructor interactions and learner interactions from a perspective of the learner. 
     The human interface module  18  transforms the learner output information  172  into human output  162  for conveyance of the learner output information  172  to the learner  28 - 1 . For example, the human interface module  18  facilitates displaying a 3-D image of the virtual environment to the learner  28 - 1 . 
     The human interface module  18  transforms human input  164  from the learner  28 - 1  to produce learner input information  174 . The learner input information  174  includes representations of learner interactions with objects within the virtual place (e.g., answering comprehension level evaluation questions). 
     The experience execution module  32  updates the representation of the virtual place by modifying the learner output information  172  based on the learner input information  174  so that the learner  28 - 1  enjoys representations of interactions caused by the learner within the virtual environment. The experience execution module  32  evaluates the learner input information  174  with regards to evaluation information of the learning objectives to evaluate a comprehension level by the learner  28 - 1  with regards to the set of learning objectives of the lesson. 
       FIG.  7 B  is a schematic block diagram of another embodiment of a representation of a learning experience that includes the learning objective  170  and the virtual place  168 . In an example of operation, the learning objective  170  is recovered from the learning assets database  34  of  FIG.  7 A  and rendered to create the virtual place  168  representations of objects  24 - 1  through  24 -N in the environment from time references zero through N. For example, a first object is the instructor  26 - 1  of  FIG.  5 A , a second object is the learner  28 - 1  of  FIG.  7 A , and the remaining objects are associated with the learning objectives of the lesson, where the objects are manipulated in accordance with annotations of instructions provided by the instructor  26 - 1 . 
     The learner  28 - 1  experiences a unique viewpoint of the environment and gains knowledge from accessing (e.g., playing) the learning experience. The learner  28 - 1  further manipulates objects within the environment to support learning and assessment of comprehension of objectives of the learning experience. 
       FIGS.  8 A- 8 C  are schematic block diagrams of another embodiment of a computing system illustrating an example of creating a learning experience. The computing system includes the environment model database  16 , the experience creation module  30 , and the learning assets database  34  of  FIG.  1   . The experience creation module  30  includes a learning path module  180 , an asset module  182 , an instruction module  184 , and a lesson generation module  186 . 
     In an example of operation,  FIG.  8    A illustrates the learning path module  180  determining a learning path (e.g., structure and ordering of learning objectives to complete towards a goal such as a certificate or degree) to include multiple modules and/or lessons. For example, the learning path module  180  obtains learning path information  194  from the learning assets database  34  and receives learning path structure information  190  and learning objective information  192  (e.g., from an instructor) to generate updated learning path information  196 . 
     The learning path structure information  190  includes attributes of the learning path and the learning objective information  192  includes a summary of desired knowledge to impart. The updated learning path information  196  is generated to include modifications to the learning path information  194  in accordance with the learning path structure information  190  in the learning objective information  192 . 
     The asset module  182  determines a collection of common assets for each lesson of the learning path. For example, the asset module  182  receives supporting asset information  198  (e.g., representation information of objects in the virtual space) and modeled asset information  200  from the environment model database  16  to produce lesson asset information  202 . The modeled asset information  200  includes representations of an environment to support the updated learning path information  196  (e.g., modeled places and modeled objects) and the lesson asset information  202  includes a representation of the environment, learning path, the objectives, and the desired knowledge to impart. 
       FIG.  8 B  further illustrates the example of operation where the instruction module  184  outputs a representation of the lesson asset information  202  as instructor output information  160 . The instructor output information  160  includes a representation of the environment and the asset so far to be experienced by an instructor who is about to input interactions with the environment to impart the desired knowledge. 
     The instruction module  184  receives instructor input information  166  from the instructor in response to the instructor output information  160 . The instructor input information  166  includes interactions from the instructor to facilitate imparting of the knowledge (e.g., instructor annotations, pointer movements, highlighting, text notes, and speech) and testing of comprehension of the knowledge (e.g., valuation information such as questions and correct answers). The instruction module  184  obtains assessment information (e.g., comprehension test points, questions, correct answers to the questions) for each learning objective based on the lesson asset information  202  and produces instruction information  204  (e.g., representation of instructor interactions with objects within the virtual place, evaluation information). 
       FIG.  8 C  further illustrates the example of operation where the lesson generation module  186  renders (e.g., as a multidimensional representation) the objects associated with each lesson (e.g., assets of the environment) within the environment in accordance with the instructor interactions for the instruction portion and the assessment portion of the learning experience. Each object is assigned a relative position in XYZ world space within the environment to produce the lesson rendering. 
     The lesson generation module  186  outputs the rendering as a lesson package  206  for storage in the learning assets database  34 . The lesson package  206  includes everything required to replay the lesson for a subsequent learner (e.g., representation of the environment, the objects, the interactions of the instructor during both the instruction and evaluation portions, questions to test comprehension, correct answers to the questions, a scoring approach for evaluating comprehension, all of the learning objective information associated with each learning objective of the lesson). 
       FIG.  8 D  is a logic diagram of an embodiment of a method for creating a learning experience within a computing system (e.g., the computing system  10  of  FIG.  1   ). In particular, a method is presented in conjunction with one or more functions and features described in conjunction with  FIGS.  1 - 7 B , and also  FIGS.  8 A- 8 C . The method includes step  220  where a processing module of one or more processing modules of one or more computing devices within the computing system determines updated learning path information based on learning path information, learning path structure information, and learning objective information. For example, the processing module combines a previous learning path with obtained learning path structure information in accordance with learning objective information to produce the updated learning path information (i.e., specifics for a series of learning objectives of a lesson). 
     The method continues at step  222  where the processing module determines lesson asset information based on the updated learning path information, supporting asset information, and modeled asset information. For example, the processing module combines assets of the supporting asset information (e.g., received from an instructor) with assets and a place of the modeled asset information in accordance with the updated learning path information to produce the lesson asset information. The processing module selects assets as appropriate for each learning objective (e.g., to facilitate the imparting of knowledge based on a predetermination and/or historical results). 
     The method continues at step  224  where the processing module obtains instructor input information. For example, the processing module outputs a representation of the lesson asset information as instructor output information and captures instructor input information for each lesson in response to the instructor output information. Further obtain asset information for each learning objective (e.g., extract from the instructor input information). 
     The method continues at step  226  where the processing module generates instruction information based on the instructor input information. For example, the processing module combines instructor gestures and further environment manipulations based on the assessment information to produce the instruction information. 
     The method continues at step  228  where the processing module renders, for each lesson, a multidimensional representation of environment and objects of the lesson asset information utilizing the instruction information to produce a lesson package. For example, the processing module generates the multidimensional representation of the environment that includes the objects and the instructor interactions of the instruction information to produce the lesson package. For instance, the processing module includes a 3-D rendering of a place, background objects, recorded objects, and the instructor in a relative position XYZ world space over time. 
     The method continues at step  230  where the processing module facilitates storage of the lesson package. For example, the processing module indexes the one or more lesson packages of the one or more lessons of the learning path to produce indexing information (e.g., title, author, instructor identifier, topic area, etc.). The processing module stores the indexed lesson package as learning asset information in a learning assets database. 
     The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system  10  of  FIG.  1    or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element, a sixth memory element, etc.) that stores operational instructions can, when executed by one or more processing modules of the one or more computing devices of the computing system  10 , cause the one or more computing devices to perform any or all of the method steps described above. 
       FIGS.  8 E,  8 F,  8 G,  8 H,  8 J, and  8 K  are schematic block diagrams of another embodiment of a computing system illustrating another example of a method to create a learning experience. The embodiment includes creating a multi-disciplined learning tool regarding a topic. The multi-disciplined aspect of the learning tool includes both disciplines of learning and any form/format of presentation of content regarding the topic. For example, a first discipline includes mechanical systems, a second discipline includes electrical systems, and a third discipline includes fluid systems when the topic includes operation of a combustion based engine. The computing system includes the environment model database  16  of  FIG.  1   , the learning assets database  34  of  FIG.  1   , and the experience creation module  30  of  FIG.  1   . 
       FIG.  8 E  illustrates the example of operation where the experience creation module  30  creates a first-pass of a first learning object  700 - 1  for a first piece of information regarding the topic to include a first set of knowledge bullet-points  702 - 1  regarding the first piece of information. The creating includes utilizing guidance from an instructor and/or reusing previous knowledge bullet-points for a related topic. For example, the experience creation module  30  extracts the bullet-points from one or more of learning path structure information  190  and learning objective information  192  when utilizing the guidance from the instructor. As another example, the experience creation module  30  extracts the bullet-points from learning path information  194  retrieved from the learning assets database  34  when utilizing previous knowledge bullet points for the related topic. 
     Each piece of information is to impart additional knowledge related to the topic. The additional knowledge of the piece of information includes a characterization of learnable material by most learners in just a few minutes. As a specific example, the first piece of information includes “4 cycle engine intake cycles” when the topic includes “how a 4 cycle engine works.” 
     Each of the knowledge bullet-points are to impart knowledge associated with the associated piece of information in a logical (e.g., sequential) and knowledge building fashion. As a specific example, the experience creation module  30  creates the first set of knowledge bullet-points  702 - 1  based on instructor input to include a first bullet point “intake stroke: intake valve opens, air/fuel mixture pulled into cylinder by piston” and a second bullet point “compression stroke: intake valve closes, piston compresses air/fuel mixture in cylinder” when the first piece of information includes the “4 cycle engine intake cycles.” 
       FIG.  8 F  further illustrates the example of operation where the experience creation module  30  creates a first-pass of a second learning object  700 - 2  for a second piece of information regarding the topic to include a second set of knowledge bullet-points  702 - 2  regarding the second piece of information. As a specific example, the experience creation module  30  creates the second set of knowledge bullet-points  702 - 2  based on the instructor input to include a first bullet point “power stroke: spark plug ignites air/fuel mixture pushing piston” and a second bullet point “exhaust stroke: exhaust valve opens and piston pushes exhaust out of cylinder, exhaust valve closes” when the second piece of information includes “4 cycle engine outtake cycles.” 
       FIG.  8 G  further illustrates the example of operation where the experience creation module  30  obtains illustrative assets  704  based on the first and second set of knowledge bullet-points  702 - 1  and  702 - 2 . The illustrative assets  704  depicts one or more aspects regarding the topic pertaining to the first and second pieces of information. Examples of illustrative assets includes background environments, objects within the environment (e.g., things, tools), where the objects and the environment are represented by multidimensional models (e.g., 3-D model) utilizing a variety of representation formats including video, scans, images, text, audio, graphics etc. 
     The obtaining of the illustrative assets  704  includes a variety of approaches. A first approach includes interpreting instructor input information to identify the illustrative asset. For example, the experience creation module  30  interprets instructor input information to identify a cylinder asset. 
     A second approach includes identifying a first object of the first and second set of knowledge bullet-points as an illustrative asset. For example, the experience creation module  30  identifies the piston object from both the first and second set of knowledge bullet-points. 
     A third approach includes determining the illustrative assets  704  based on the first object of the first and second set of knowledge bullet-points. For example, the experience creation module  30  accesses the environment model database  16  to extract information about an asset from one or more of supporting asset information  198  and modeled asset information  200  for a sparkplug when interpreting the first and second set of knowledge bullet-points. 
       FIG.  8 H  further illustrates the example of operation where the experience creation module  30  creates a second-pass of the first learning object  700 - 1  to further include first descriptive assets  706 - 1  regarding the first piece of information based on the first set of knowledge bullet-points  702 - 1  and the illustrative assets  704 . Descriptive assets include instruction information that utilizes the illustrative asset  704  to impart knowledge and subsequently test for knowledge retention. The embodiments of the descriptive assets includes multiple disciplines and multiple dimensions to provide improved learning by utilizing multiple senses of a learner. Examples of the instruction information includes annotations, actions, motions, gestures, expressions, recorded speech, speech inflection information, review information, speaker notes, and assessment information. 
     The creating the second-pass of the first learning object  700 - 1  includes generating a representation of the illustrative assets  704  based on a first knowledge bullet-point of the first set of knowledge bullet-points  702 - 1 . For example, the experience creation module  30  renders 3-D frames of a 3-D model of the cylinder, the piston, the spark plug, the intake valve, and the exhaust valve in motion when performing the intake stroke where the intake valve opens and the air/fuel mixture is pulled into the cylinder by the piston. 
     The creating of the second-pass of the first learning object  700 - 1  further includes generating the first descriptive assets  706 - 1  utilizing the representation of the illustrative assets  704 . For example, the experience creation module  30  renders 3-D frames of the 3-D models of the various engine parts without necessarily illustrating the first set of knowledge bullet-points  702 - 1 . 
     In an embodiment where the experience creation module  30  generates the representation of the illustrative assets  704 , the experience creation module  30  outputs the representation of the illustrative asset  704  as instructor output information  160  to an instructor. For example, the 3-D model of the cylinder and associated parts. 
     The experience creation module  30  receives instructor input information  166  in response to the instructor output information  160 . For example, the instructor input information  166  includes instructor annotations to help explain the intake stroke (e.g., instructor speech, instructor pointer motions). The experience creation module  30  interprets the instructor input information  166  to produce the first descriptive assets  706 - 1 . For example, the renderings of the engine parts include the intake stroke as annotated by the instructor. 
       FIG.  8 J  further illustrates the example of operation where the experience creation module  30  creates a second-pass of the second learning object  700 - 2  to further include second descriptive assets  706 - 2  regarding the second piece of information based on the second set of knowledge bullet-points  702 - 2  and the illustrative assets  704 . For example, the experience creation module  30  creates 3-D renderings of the power stroke and the exhaust stroke as annotated by the instructor based on further instructor input information  166 . 
       FIG.  8 K  further illustrates the example of operation where the experience creation module  30  links the second-passes of the first and second learning objects  700 - 1  and  700 - 2  together to form at least a portion of the multi-disciplined learning tool. For example, the experience creation module  30  aggregates the first learning object  700 - 1  and the second learning object  700 - 2  to produce a lesson package  206  for storage in the learning assets database  34 . 
     In an embodiment, the linking of the second-passes of the first and second learning objects  700 - 1  and  700 - 2  together to form the at least the portion of the multi-disciplined learning tool includes generating index information for the second-passes of first and second learning objects to indicate sharing of the illustrative asset  704 . For example, the experience creation module  30  generates the index information to identify the first learning object  700 - 1  and the second learning object  700 - 2  as related to the same topic. 
     The linking further includes facilitating storage of the index information and the first and second learning objects  700 - 1  and  700 - 2  in the learning assets database  34  to enable subsequent utilization of the multi-disciplined learning tool. For example, the experience creation module  30  aggregates the first learning object  700 - 1 , the second learning object  700 - 2 , and the index information to produce the lesson package  206  for storage in the learning assets database  34 . 
     The method described above with reference to  FIGS.  8 E- 8 K  in conjunction with the experience creation module  30  can alternatively be performed by other modules of the computing system  10  of  FIG.  1    or by other devices including various embodiments of the computing entity  20  of  FIG.  2 A . In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element, a sixth memory element, etc.) that stores operational instructions can, when executed by one or more processing modules of the one or more computing entities of the computing system  10 , cause boy one or more computing devices to perform any or all of the method steps described above. 
       FIGS.  9 A- 9 B  are schematic block diagrams of an embodiment of an instruction module  184  of a computing system illustrating an example of creating a learning experience assessment. The instruction module  184  includes an environment generation module  240 , an evaluation information module  242 , and an assessment information module  244 . 
     In an example of operation, the evaluation information module  242  interprets desire knowledge to impart of lesson asset information  202  to produce implicit evaluation information  248 . The implicit evaluation information  248  includes testable points from the desire knowledge to impart. 
     The environment generation module  240  outputs a representation of the lesson environment based on the implicit evaluation information  248  and the lesson asset information  202  to an instructor as instructor output information  160  to facilitate capture of instructor input information  166 . For example, the environment generation module  240  identifies an environment and objects associated with an aggregate of the testable points of the implicit evaluation information  248  and an environment of the lesson asset information to render an aggregate to produce the representation as instructor output information  160 . 
     The evaluation information module  242  analyzes the instructor input information  166  to produce explicit evaluation information  250 . The explicit evaluation information  250  includes further testable points, questions, correct answers to the questions, and interpreted representations of instructor interactions (e.g., instructor asking what is this, how does this interact with that, etc.). The instructor input information  166  further includes pointer motion information, text, speech, body motion, and a sequence of events and actions. 
       FIG.  9 B  further illustrates the example of operation where the assessment information module  244  generates a set of test plans based on the explicit and implicit evaluation information. For example, the assessment information module  244  consolidates test points of implicit and explicit information and integrates with questions from explicit evaluation information. 
     For each test point, the assessment information module  244  determines corresponding knowledge evaluation information for the lesson environment (e.g., starting viewpoint, objects, represented questions, represented demonstration such as show me) to produce assessment information  252 . The assessment information  252  includes one or more of environment manipulations, answers, answer interpretation guidance, scoring information, and how to integrate questions and answers into a comprehension evaluation portion of the learning experience. 
       FIG.  9 C  is a logic diagram of an embodiment of a method for creating a learning experience assessment within a computing system (e.g., the computing system  10  of  FIG.  1   ). In particular, a method is presented in conjunction with one or more functions and features described in conjunction with  FIGS.  1 - 7 B , and also  FIGS.  9 A- 9 B . The method includes step  260  where a processing module of one or more processing modules of one or more computing devices within the computing system interprets desire knowledge to impart to produce implicit evaluation information. For example, the processing module identifies testable points from the desired knowledge to impart. 
     The method continues at step  262  where the processing module outputs a representation of a lesson environment based on the implicit evaluation information as instructor output information. For example, the processing module generates a multidimensional rendering of the lesson environment to include objects associated with the testable points of the implicit evaluation information. 
     The method continues at step  264  where the processing module analyzes instructor input information in response to the instructor output information to produce explicit evaluation information. For example, the processing module identifies explicit questions and interprets representations of instructor interactions associated with further testable points. 
     The method continues at step  268  where the processing module generates a set of test points based on the explicit and implicit evaluation information. For example, the processing module consolidates test points of the implicit and explicit evaluation information and integrates with questions from the explicit question and answer information. 
     For each test point, the method continues at step  270  where the processing module determines corresponding knowledge evaluation information for the lesson environment to produce assessment information. For example, for each test point, the processing module determines a set of multidimensional representations of the environment and the objects as associated with the test point in accordance with a question nature and an answer nature of the test point. The determining further includes identifying scoring information based on multiple-choice or expected open answers. 
     The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system  10  of  FIG.  1    or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element, a sixth memory element, etc.) that stores operational instructions can, when executed by one or more processing modules of the one or more computing devices of the computing system  10 , cause the one or more computing devices to perform any or all of the method steps described above. 
       FIGS.  9 D,  9 E,  9 F, and  9 G  are schematic block diagrams of another embodiment of a computing system illustrating another example of creating a learning experience assessment. The embodiment includes creating an assessment for a multi-disciplined learning tool regarding a topic. The multi-disciplined aspect of the learning tool includes both disciplines of learning and any form/format of presentation of content regarding the topic. For example, a first discipline includes mechanical systems, a second discipline includes electrical systems, and a third discipline includes fluid systems when the topic includes operation of a combustion based engine. The computing system includes the learning assets database  34  of  FIG.  1    and the experience creation module  30  of  FIG.  1   . 
       FIG.  9 D  illustrates the example of operation of the creating the assessment for the multi-disciplined learning tool regarding the topic where the experience creation module  30 , of the computing entity  20  of  FIG.  1   , derives a first set of knowledge test-points  758 - 1  for a first learning object  750 - 1  regarding the topic based on a first set of knowledge bullet-points  752 - 1 , an illustrative asset  754  and a first descriptive asset  756 - 1  of the first learning object  750 - 1 . The first set of knowledge test-points  758 - 1  includes questions and answers to the questions, where the questions are associated with the topic. 
     The experience creation module  30  generates the first set of knowledge bullet-points  752 - 1 , the illustrative asset  754 , and the first descriptive asset  756 - 1  of the first learning object  750 - 1  as discussed with reference to  FIGS.  8 E- 8 K . In an embodiment, the experience creation module  30  extracts the first set of knowledge bullet-points  752 - 1 , the illustrative asset  754 , and the first descriptive asset  756 - 1  from lesson package  206  recovered from the learning asset database  34 . 
     The deriving of the first set of knowledge test-points  758 - 1  includes utilizing guidance from an instructor and interpreting knowledge bullet-points for the topic. For example, the experience creation module  30  interprets instructor input information  166  to identify a knowledge test-point of the first set of knowledge test-points  758 - 1 . For instance, the experience creation module  30  outputs a representation of a third knowledge bullet-point of the first set of knowledge bullet-points  752 - 1  as instructor output information  160  in accordance with the illustrative assets  754  and the first descriptive assets  756 - 1  of the first learning object  750 - 1  to an instructor. In response, the experience creation module  30  receives the instructor input information  166  from the instructor for the interpreting. 
     As another example of the deriving of the first set of knowledge test-points  758 - 1 , the experience creation module  30  interprets a first knowledge bullet-point of the first set of knowledge bullet-points  752 - 1  in accordance with the illustrative asset  754  and the first descriptive assets  756 - 1  of the first learning object  750 - 1  to produce a first knowledge test-point of the first set of knowledge test-points  758 - 1 . For instance, the experience creation module  30  generates a first question to include “what are steps of intake stroke?” and a first answer to the first question to include “intake valve opens, air/fuel mixture pulled into cylinder by piston” when the first knowledge bullet-point includes “intake stroke: intake valve opens, air/fuel mixture pulled into cylinder by piston.” 
     As yet another example of the deriving of the first set of knowledge test-points  758 - 1 , the experience creation module  30  interprets a second knowledge bullet-point of the first set of knowledge bullet-points  752 - 1  in accordance with the illustrative asset  754  and the first descriptive assets  756 - 1  of the first learning object  750 - 1  to produce a second knowledge test-point of the first set of knowledge test-points  758 - 1 . For instance, the experience creation module  30  generates a second question to include “what are steps of intake stroke?” and a first answer to the first question to include “intake valve closes, piston compresses air/fuel mixture in cylinder” when the second knowledge bullet-point includes “compression stroke: intake valve closes, piston compresses air/fuel mixture in cylinder.” 
       FIG.  9 E  further illustrates the example of operation of the creating the assessment for the multi-disciplined learning tool regarding the topic where the experience creation module  30 , of the computing entity  20  of  FIG.  1   , derives a second set of knowledge test-points  758 - 2  for a second learning object  750 - 2  regarding the topic based on a second set of knowledge bullet-points  752 - 2 , the illustrative asset  754 , and a second descriptive asset  756 - 2  of the second learning object  750 - 2 . The second set of knowledge test-points  758 - 2  includes questions and answers to the questions, where the questions are associated with the topic. 
     The experience creation module  30  generates the second set of knowledge bullet-points  752 - 2 , the illustrative asset  754 , and the second descriptive asset  756 - 2  of the second learning object  750 - 2  as discussed with reference to  FIGS.  8 E- 8 K . In an embodiment, the experience creation module  30  extracts the second set of knowledge bullet-points  752 - 2 , the illustrative asset  754 , and the second descriptive asset  756 - 2  from the lesson package  206  recovered from the learning asset database  34 . 
     The deriving of the second set of knowledge test-points  758 - 2  includes utilizing guidance from an instructor and interpreting knowledge bullet-points for the topic. For example, the experience creation module  30  interprets instructor input information  166  to identify a knowledge test-point of the second set of knowledge test-points  758 - 2 . For instance, the experience creation module  30  outputs a representation of a third knowledge bullet-point of the second set of knowledge bullet-points  752 - 2  as instructor output information  160  in accordance with the illustrative assets  754  and the second descriptive assets  756 - 2  of the second learning object  750 - 2  to an instructor. In response, the experience creation module  30  receives the instructor input information  166  from the instructor for the interpreting. 
     As another example of the deriving of the second set of knowledge test-points  758 - 2 , the experience creation module  30  interprets a first knowledge bullet-point of the second set of knowledge bullet-points  752 - 2  in accordance with the illustrative asset  754  and the second descriptive assets  756 - 2  of the second learning object  750 - 2  to produce a first knowledge test-point of the second set of knowledge test-points  758 - 2 . For instance, the experience creation module  30  generates a first question to include “what are steps of power stroke?” and a first answer to the first question to include “spark plug ignites air/fuel mixture pushing piston” when the first knowledge bullet-point includes “power stroke: spark plug ignites air/fuel mixture pushing piston.” 
     As yet another example of the deriving of the second set of knowledge test-points  758 - 2 , the experience creation module  30  interprets a second knowledge bullet-point of the second set of knowledge bullet-points  752 - 2  in accordance with the illustrative asset  754  and the second descriptive assets  756 - 2  of the second learning object  750 - 2  to produce a second knowledge test-point of the second set of knowledge test-points  758 - 2 . For instance, the experience creation module  30  generates a second question to include “what are steps of exhaust stroke?” and a first answer to the first question to include “exhaust valve opens and piston pushes exhaust out of cylinder, exhaust valve closes” when the second knowledge bullet-point includes “exhaust stroke: exhaust valve opens and piston pushes exhaust out of cylinder, exhaust valve closes.” 
       FIG.  9 F  further illustrates the example of operation of the creating the assessment for the multi-disciplined learning tool regarding the topic where the experience creation module  30  generates a knowledge assessment asset based on the first and second knowledge test-points  758 - 1  and  758 - 2 . The knowledge assessment asset, when subsequently executed, provides a test for knowledge retention, where knowledge is imparted from the descriptive assets. The embodiments of the knowledge assessment asset includes multiple disciplines and multiple dimensions to provide improved testing of learning retention by utilizing multiple senses of a learner. 
     The knowledge assessment asset includes one or more of assessment information (e.g., questions and answers) for the knowledge test-points and representations (e.g., multi-dimensional renderings) of the illustrative assets  754  in accordance with the knowledge test-points. Examples of the assessment information includes assessing annotations, actions, motions, gestures, expressions, recorded speech, speech inflection information, review information, and speaker notes, instructor questions, and instructor answers. In an embodiment, the knowledge assessment asset is represented by first knowledge assessment assets  760 - 1  associated with the first learning object  750 - 1  and second knowledge assessment assets  760 - 2  associated with the second learning object  750 - 2 . 
     The generating of the knowledge assessment asset based on the first and second knowledge test-points  758 - 1  and  758 - 2  includes a variety of approaches. A first approach include generating a first representation (e.g., rendering) of the illustrative assets  754  based on a first knowledge test-point of the first set of knowledge test-points  758 - 1 . For example, the experience creation module  30  renders 3-D frames of a 3-D model of the cylinder, the piston, the spark plug, the intake valve, and the exhaust valve in motion performing the intake stroke. A question requests identification of the steps of the intake stroke. 
     A second approach of generating the knowledge assessment includes generating a first portion of the knowledge assessment asset utilizing the first representation of the illustrative asset. For example, the experience creation module  30  generates a first portion of the first knowledge assessment assets  760 - 1  to include the engine rendering for the intake stroke as the first representation. 
     A third approach of generating the knowledge assessment includes generating a second representation of the illustrative assets  754  based on a first knowledge test-point of the second set of knowledge test-points  758 - 2 . For example, the expense module  30  renders 3-D frames of the 3-D model of the cylinder, the piston, the spark plug, the intake valve, and exhaust valve in motion performing the power stroke. A question requests identification of the steps of the power stroke. 
     A fourth approach of generating the knowledge assessment includes generating a second portion of the knowledge assessment asset utilizing the second representation of the illustrative asset. For example, the experience creation module  30  generates a first portion of the second knowledge assessment assets  760 - 2  to include the engine rendering for the power stroke as the second representation. 
     A fifth approach of generating the knowledge assessment includes the experience creation module  30  outputting the first and second representations of the illustrative asset as instructor output information  160  and receiving instructor input information  166  in response to the instructor output information  160 . Having received the instructor input information  166 , the experience creation module  30  interprets the instructor input information  166  to produce the first and second portions of the knowledge assessment asset. For example, the instructor input information  166  includes instructor annotations to pose a question with regards to the intake stroke (e.g., instructor speech, instructor pointer motions). The experience creation module  30  interprets the instructor input information  166  to produce the first knowledge assessment asset  760 - 1  and the second knowledge assessment assets  760 - 2 . For example, the renderings of the engine parts include the intake stroke with a query as to the steps of the intake stroke by the instructor. 
       FIG.  9 G  further illustrates the example of operation of the creating the assessment for the multi-disciplined learning tool regarding the topic where the experience creation module  30  updates the first learning object  750 - 1  to include the first set of knowledge test-points  758 - 1  and a first portion of the knowledge assessment asset (e.g., the first knowledge assessment assets  760 - 1 ). The experience creation module  30  updates the second learning object  750 - 2  to include the second set of knowledge test-points  758 - 2  and a second portion of the knowledge assessment asset (e.g., the second knowledge assessment assets  760 - 2 ). 
     Having updated the first and second learning objects  750 - 1  and  750 - 2 , the experience creation module  30  links the first and second learning objects  750 - 1  and  750 - 2  together to form at least a portion of the multi-disciplined learning tool. For example, the experience creation module  30  aggregates the first learning object  750 - 1  and the second learning object  750 - 2  to produce assessment information  252  for storage in the learning assets database  34 . 
     In an embodiment, the linking of the first and second learning objects  750 - 1  and  750 - 2  together to form the at least the portion of the multi-disciplined learning tool includes generating index information for the first and second learning objects to indicate sharing of the illustrative asset  754 . For example, the experience creation module  30  generates the index information to identify the first learning object  750 - 1  and the second learning object  750 - 2  as related to the same topic. 
     The linking further includes facilitating storage of the index information and the first and second learning objects  750 - 1  and  750 - 2  in the learning assets database  34  to enable subsequent utilization of the multi-disciplined learning tool. For example, the experience creation module  30  aggregates the first learning object  750 - 1 , the second learning object  750 - 2 , and the index information to produce the assessment information  252  for storage in the learning assets database  34 . 
     The method described above with reference to  FIGS.  9 D- 9 G  in conjunction with the experience creation module  30  can alternatively be performed by other modules of the computing system  10  of  FIG.  1    or by other devices including various embodiments of the computing entity  20  of  FIG.  2 A . In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element, a sixth memory element, etc.) that stores operational instructions can, when executed by one or more processing modules of the one or more computing entities of the computing system  10 , cause boy one or more computing devices to perform any or all of the method steps described above. 
       FIGS.  10 A- 10 B  are schematic block diagrams of an embodiment of an experience execution module  32  of a computing system illustrating an example of executing a learning experience. The experience execution module  32  includes the environment generation module  240  and an instance experience module  290 . 
     In an example of operation, the environment generation module  240  generates a baseline environment and object information  292  based on a selected lesson package  206  of learning asset information from the learning assets database  34 . For example, the environment generation module  240  determines default XYZ world space positioning information for each object within the environment from the lesson package  206  of the learning asset information  48 . The environment and object information  292  includes one or more of a list of objects, an identifier of the environment, positioning information of the environment, positioning information for each object, a type identifier for the environment, and type identifiers for each object. 
     The instance experience module  290  outputs an instance experience of the baseline environment and objects for a learner as learner output information  172 . For example, the instance experience module  290  establishes an initial rendering of the instance experience (e.g., a starting viewpoint) for the environment utilizing instruction information  204  (e.g., initial instructor interactions) and the objects and outputs the learner output information  172  to the learner. Having established the initial rendering, the instance experience module  290  proceeds to play the learning experience by rendering further time frames of the learning experience and outputting the further renderings as further learner output information  172 . 
       FIG.  10 B  further illustrates the example of operation where the instance experience module  290  interprets learner input information  174  from the learner to produce learning session control information. For example, the instance experience module  290  interprets the learner input information  174  with respect to the learner output information  172  to identify one or more commands (e.g., change view, zoom, and perspective, snap to a perspective viewpoint, change dimensions, i.e., two-dimensional to three-dimensional, static time, streaming time, jump to a time reference, stop, pause, rewind, fast-forward, set playback rate, set playback direction, object manipulation information, i.e., pointer position, click select, selecting learning objectives, selection of lessons, and accessing index information). 
     The instance experience module  290  updates the instance experience based on the learning session control information. For example, the instance experience module  290  performs one of the following based on the learner input information  174 : change viewpoint, change time perception, change to another learning objective, update the baseline environment, and update object information based on learning session control information, i.e., authorize modifications of the object/environment, improve a starting position, improve ordering, etc. 
     The instance experience module  290  outputs the updated instance experience as further learner output information to the learner. For example, the instance experience module  290  renders the updated instance experience in accordance with the learning session control information and outputs as the further learner output information. 
       FIG.  10 C  is a logic diagram of an embodiment of a method for executing a learning experience within a computing system (e.g., the computing system  10  of  FIG.  1   ). In particular, a method is presented in conjunction with one or more functions and features described in conjunction with  FIGS.  1 - 7 B , and also  FIGS.  10 A- 10 B . The method includes step  310  where a processing module of one or more processing modules of one or more computing devices within the computing system generates a baseline environment and objects based on a selected lesson package of learning asset information. For example, the processing module determines default XYZ positioning information of each object within the environment. 
     The method continues at step  312  where the processing module outputs an instance experience of the baseline environment and objects to a learner as learner output information. For example, the processing module establishes, utilizing default configuration information for the learner, a rendering of the instance experience utilizing a starting viewpoint and outputs the rendering as the learner output information to the learner. 
     The method continues at step  314  where the processing module interprets learner input information from the learner to produce learning session control information. For example, the processing module interprets the learner input information with respect to the learner output information to identify one or more commands. 
     The method continues at step  316  where the processing module updates the instance experience and baseline environment and objects based on the learning session control information. For example, the processing module performs a change of viewpoint and change of timeframe reference of the playing of the learning session. 
     The method continues at step  318  where the processing module outputs the updated instance experience is further learner output information to the learner. For example, the processing module renders the updated instance experience in accordance with the learner session control information and outputs the updated instance experience as the further learner output information. 
     The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system  10  of  FIG.  1    or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element, a sixth memory element, etc.) that stores operational instructions can, when executed by one or more processing modules of the one or more computing devices of the computing system  10 , cause the one or more computing devices to perform any or all of the method steps described above. 
       FIGS.  10 D,  10 E,  10 F,  10 G, and  10 H  are schematic block diagrams of another embodiment of a computing system illustrating another example of executing a learning experience. The computing system includes the experience execution module  32  of  FIG.  1    and the human interface module  18  of  FIG.  1   . The experience execution module  32  includes the environment generation module  240  of  FIG.  10 A  and the instance experience module  290  of  FIG.  10 A . 
       FIG.  10 D  illustrates an example of operation of executing of the learning experience utilizing a multi-disciplined learning tool regarding a topic where the experience execution module  32  obtains the first learning object  750 - 1  of  FIG.  9 D  and the second learning object  750 - 2  of  FIG.  9 D  (e.g., retrieve from the learning assets database  34  of  FIG.  1   ) that share illustrative assets  754  of  FIG.  9 D . For example, the experience execution module  32  obtains the first and second learning objects when initiating the executing of the learning experience. The first learning object  750 - 1  includes first descriptive assets  756 - 1 - 1  through  756 - 1 - m  and first knowledge assessment assets  760 - 1 - 1  through  760 - 1 - n . The second learning object  750 - 2  includes second descriptive assets  756 - 2 - 1  through  756 - 2 - n  and second knowledge assessment assets  760 - 2 - 1  through  760 - 2 - n.    
     Having accessed the illustrative assets  754 , the environment generation module  240  generates a fundamental illustrative model  770  based on the illustrative assets  754  of a lesson that includes a plurality of learning objects (e.g., at least the first and second learning objects  750 - 1  and  750 - 2 ). The plurality of learning objects are created based on at least one illustrative asset of the illustrative assets  754 . An illustrative asset depicts an aspect regarding the topic pertaining to information associated with the first and second learning objects  750 - 1  and  750 - 2 . 
     The generating of the fundamental illustrative model  770  includes a series of steps. A first step includes identifying first and second illustrative assets of the illustrative assets  754 . For example, interpreting objects. Another example includes extracting objects. 
     A second step includes generating a first multi-dimensional representation of the first illustrative asset. For example, a three-dimensional model is rendered. A third step includes generating a second multi-dimensional representation of the second illustrative asset (e.g., another three-dimensional model). 
     A fourth step includes integrating the first multi-dimensional representation of the first illustrative asset and the second multi-dimensional representation of the second illustrative asset to produce the fundamental illustrative model. For example, the environment generation module  240  scales the representations together. As another example, the representations are aggregated. As yet another example, the environment generation module  240  selects common dimensionality for the representations. In an instance, the environment generation module  240  produces a three-dimensional basic engine rendering of multiple objects that make up the engine (e.g., cylinder walls, piston, valves, spark plug). 
       FIG.  10 E  further illustrates the example of operation of the executing of the learning experience where the instance experience module  290  modifies the fundamental illustrative model  770  to illustrate a first set of teaching assets of the first learning object of the plurality of learning objects to produce a first learning illustrative model  772 - 1 . Teaching assets includes descriptive assets and knowledge assessment assets. 
     The modifying of the fundamental illustrative model  770  to illustrate the first set of teaching assets of the first learning object  750 - 1  to produce the first learning illustrative model  772 - 1  includes one or more of the following steps. A first step includes identifying a first descriptive asset  756 - 1 - 1  of the first learning object  750 - 1  as part of the first set of teaching assets. For instance, identifying a next first descriptive asset in a sequential identification approach where the next first descriptive asset pertains to instruction information for the power stroke of the engine. Another instance includes interpreting learner input information  174  to select a particular first descriptive asset (e.g., direct access within the range of 1-m). 
     An alternative first step includes identifying a first knowledge assessment asset  760 - 1 - 1  of the first learning object  750 - 1  as part of the first set of teaching assets. For instance, identifying a next first knowledge assessment asset in a sequential identification approach when performing an assessment and receiving responses as learner input information  174  (e.g., answers to the assessment) where the next first knowledge assessment asset pertains to assessment information for the end take stroke as further discussed with reference to  FIG.  10 F . Another instance includes interpreting the learner input information  174  to select a particular first knowledge assessment asset (e.g., direct access within the range of 1-m). 
     A second step includes generating a multi-dimensional representation of the teaching assets. For example, the instance experience module  290  renders a three-dimensional model of the teaching assets. A third step includes integrating the multi-dimensional representation of the teaching assets and the fundamental illustrative model  770  to produce the first learning illustrative model  772 - 1 . The integrating includes one or more of scaling, aggregating, and selecting a common dimensionality. 
     Having produced the first learning illustrative model  772 - 1 , the instance experience module  290  outputs the first learning illustrative model  772 - 1  to a computing entity associated with the learner  28 - 1  of  FIG.  1   . For example, the instance experience module  290  outputs the first learning illustrative model  772 - 1  to the human interface module  18  of the computing entity and the human interface module  18  formats the first learning illustrative model  772 - 1  to produce human output  162  for the learner  28 - 1 . 
     In response to the human output  162 , the human interface module  18  receives human input  164  (e.g., to manipulate a viewpoint and perspective for the learner  28 - 1 , to control a pace of the learning experience). When receiving input to manipulate the perspective, the integrating of the multi-dimensional representation of the teaching assets and the fundamental illustrative model to produce the first learning illustrative model  772 - 1  includes further steps. A first further step includes determining a learner perspective. For example, the instance experience module  290  interprets the learner input information  174  to identify when the learner  28 - 1  is turning their head or moving a pointer to change the perspective. 
     A second further step includes modifying the first learning illustrative model based on the learner perspective. For example, the instance experience module  290  re-renders the first learning illustrative model  772 - 1  in accordance with the learner perspective to update the first learning illustrative model  772 - 14  further outputting, via the human interface module  18 , as human output  162  to the learner  28 - 1 . For instance, the learner  28 - 1  continues the learning experience by viewing the three-dimensional representation of the engine from a top view versus a side view. 
       FIG.  10 F  further illustrates the example of operation of the executing of the learning experience where the instance experience module  290  modifies the fundamental illustrative model  770  to illustrate a second set of teaching assets of the first learning object  750 - 1  to produce a second learning illustrative model  772 - 2 . For example, further descriptive assets. As another example, further first knowledge assessment assets  760 - 1 - 1  through  760 - 1 - m  such as renderings of assessment information with regards to the intake stroke and the compression stroke of the engine. 
     Having produced the second learning illustrative model  772 - 2 , the instance experience module  290  outputs the second learning illustrative model  772 - 2  to the computing entity associated with the learner  28 - 1 . For example, the instance experience module  290  outputs the second learning illustrative model  772 - 2  to the human interface module  18  and the human interface module  18  produces human output  162  for the learner  28 - 1  based on the second learning illustrative model  772 - 2 . 
     Having output the second learning illustrative model  772 - 2 , the instance experience module  290  indicates to produce a third learning illustrative model when detecting one or more enablement conditions. A first enablement condition includes completion of outputting the second learning illustrative model  772 - 2  to the computing entity associated with the learner  28 - 1 . For example, the instance experience module  290  detects that a desired number of descriptive and/or knowledge assessment assets of the first learning object  750 - 1  has been utilized to produce the output (e.g., ran through all of the assets of the first learning object). 
     A second enablement condition includes an advancement indicator from the computing entity associated with the learner  28 - 1 . For example, the instance experience module  290  interprets learner input information  174  which indicates that the learner  28 - 1  desires to move from the first of the second learning object. 
     A third enablement condition include a favorable assessment of learner comprehension associated with the first learning object  750 - 1 . For example, the instance experience module  290  performs a comprehension assessment based on learner input information  174  received in response to outputting of the learner illustrative model associated with the first knowledge assessment assets. 
       FIG.  10 G  further illustrates the example of operation of the executing of the learning experience where, having completed utilization of the first learning object  750 - 1 , the instance experience module  290  modifies the fundamental illustrative model  770  to illustrate a third set of teaching assets of the second learning object  750 - 2  of the plurality of learning objects to produce a third learning illustrative model  772 - 3 . The third set of teaching assets includes the second descriptive asset  756 - 2 - 1  through  756 - 2 - n  and the second knowledge assessment asset  760 - 2 - 1  through  760 - 2 - n . For example, the instance experience module  290  modifies the fundamental illustrative model  770  to illustrate the second descriptive assets  756 - 2 - 1  through  756 - 2 - n  to produce the third learning illustrative model  772 - 3  when portraying one or more three-dimensional renderings of instruction information for the power stroke and the exhaust stroke of the engine. 
     Having produced the third learning illustrative model  772 - 3 , the instance experience module  290  outputs the third learning illustrative model  772 - 3  to the computing entity associated with the learner  28 - 1 . For example, the instance expense module  290  outputs the third learning illustrative model  772 - 3  to the human interface module  18 . The human interface module  18  formats the third learning illustrative model  772 - 3  into the human output  162 . 
     While outputting the third learning illustrative model  772 - 3 , the instance experience module  290  interprets learner input information  174  based on human input  164  to modify the third learning illustrative model  772 - 3  to accommodate an updated perspective desired by the learner  28 - 1  (e.g., a view of the engine from a different side). For example, having produced the updated third learning illustrative model  772 - 3 , the instance expense module  290  outputs the third learning illustrative model  772 - 3 , based on the updating, to the human interface module  18 . 
       FIG.  10 H  further illustrates the example of operation of the executing of the learning experience where the instance experience module  290  modifies the fundamental illustrative model  770  to illustrate a fourth set of teaching assets of the second learning object  750 - 2  to produce a fourth learning illustrative model  772 - 4 . For example, the instance experience module  290  detects that the outputting of the learning illustrative model based on the second descriptive assets has been completed and modifies the fundamental illustrative model  770  to illustrate the second knowledge assessment assets  760 - 2 - 1  through  760 - 2 - n  to produce the fourth learning illustrative model  772 - 4  with regards to assessment information associated with the power stroke and the exhaust stroke of the engine. 
     Having produced the fourth learning illustrative model  772 - 4 , the instance experience module  290  outputs the fourth learning illustrative model  772 - 42  the computing entity associated with the learner  28 - 1 . For example, the instance expense module  290  outputs the fourth learning illustrative model  772 - 4  to the human interface module  18 . The human interface module  18  formats the fourth learning illustrative model  772 - 4  into the human output  162 . 
     While outputting the fourth learning illustrative model  772 - 4 , the instance experience module  290  interprets learner input information  174  based on human input  164  to modify the fourth learning illustrative model  772 - 4  to accommodate an updated perspective desired by the learner  28 - 1  (e.g., a view of the engine from a different angle). For example, having produced the updated fourth learning illustrative model  772 - 4 , the instance expense module  290  outputs the fourth learning illustrative model  772 - 4 , based on the updating, to the human interface module  18 . 
     Alternatively, or in addition to, while outputting the fourth learning illustrative model  772 - 4 , the instance experience module  290  interprets the learner input information  174  as answers to assessment queries to produce an assessment of comprehension of the second descriptive assets. For example, the instance experience module  290  interprets the learner input information  174  to point to the exhaust valve when the associated query asks which valve is the exhaust valve of the engine. 
       FIGS.  11 A- 11 B  are schematic block diagrams of another embodiment of an experience execution module  32  of a computing system illustrating an example of assessing execution of a learning experience. The experience execution module  32  includes the environment generation module  240 , the instance experience module  290 , and a learning assessment module  330 .  FIG.  11 A  illustrates an example of operation where the instance experience module  290  outputs a representation of a lesson package  206  within a learning environment with objects to a learner as learner output information  172 . For example, the instance experience module  290  generates the representation utilizing baseline environment an object information  292  along with instruction information  204  of the lesson package  206  of the learning asset information  48 . The instance experience module  290  outputs the representation as the learner output information  172  to the learner to initiate the execution of a lesson. 
     The instance experience module  290  collects lesson interactions. For example, the instance experience module  290  further updates the representation of the lesson package based on learner input information  174  (i.e., change view based on updated session control information from the learner), functionality and/or time correlated learner input information to the learner output information to produce correlated lesson learner input information. The correlated lesson learner input information includes timestamp to session control information, time stamped object manipulation information, and time stamped direct input such as text, speech, etc. 
       FIG.  11 B  further illustrates the example of operation where the instance experience module  290  outputs a representation of assessment information  252  of the lesson package  206  to the learner as further learner output information. For example, the instance experience module  290  generates the representation utilizing the baseline environment and object information  292  and assessment information  252  (i.e., questions etc.) and outputs the representation as the further learner output information. 
     The instance experience module outputs learner interaction information based on the lesson interactions and collected assessment interactions. For example, the instance experience module  290  further updates the representation of the assessment information based on learner input information  174  (i.e., change view) and functionality and/or time correlated further learner input information to the further learner output information to produce correlated assessment learner input information (i.e., time stamped manipulation and answer information). The instance experience module  290  further combines the correlated lesson learner input information and the correlated assessment learner input information to produce the learner interaction information. 
     The learning assessment module  330  generates learning assessment results information  334  based on the lesson interactions of the learner interaction information  332 , the assessment information  252  and the assessment interactions of the learner interaction information  332 . The learning assessment results information  334  includes one or more of learner identifier, learning objective identifier, a lesson identifier, and raw learner interaction information (i.e., timestamp record of all learner interactions like points, speech, input text, settings, viewpoints, etc.). 
     The learning assessment results information  334  further includes summarized learner interaction information. For example, averages and tallies of the row interaction information, i.e., how much time per question, how much time spent looking at a view of a learning objective, how fast did the learner proceed, what were the answers given by the learner to the questions, which questions were answered incorrectly, which questions were answered correctly, etc. 
     The generating of the learning assessment results information  334  includes summarizing correlated lesson learner input information of the collected lessons interactions, summarizing the correlated assessment learner input information, and storing the learner assessment results information in the learning assets database  34  to enable subsequent improved learning effectiveness. The improvements include modifying a pace of execution of a lesson, modifying an order of learning objectives within a lesson, modifying an order of lessons within a module, and modifying a default view of the environment for a subsequent lesson experience. 
       FIG.  11 C  is a logic diagram of an embodiment of a method for assessing execution of a learning experience within a computing system (e.g., the computing system  10  of  FIG.  1   ). In particular, a method is presented in conjunction with one or more functions and features described in conjunction with  FIGS.  1 - 7 B , and also  FIGS.  11 A- 11 B . The method includes step  350  where a processing module of one or more processing modules of one or more computing devices within the computing system outputs a representation of a lesson package within a learning environment to a learner as learner output information. For example, the processing module generates the representation utilizing baseline environment and object information along with construction information and outputs the representation as the learner output information to the learner (i.e., initiate a lesson). 
     The method continues at step  352  where the processing module collects lesson interactions from the learner. For example, the processing module further updates the representation of a lesson package based on learner input information and correlates learner input information to the learner output information to produce correlated lesson learner input information. 
     The method continues at step  354  where the processing module outputs a representation of assessment information within the learning environment to the learner as further learner output information. For example, the processing module generates the representation utilizing the baseline environment, object information and the assessment information. The processing module outputs a representation as the further learner output information. 
     The method continues at step  356  where the processing module generates learner interaction information based on the lesson interactions and collected assessment interactions from the learner. For example, the processing module further updates the representation of the assessment information based on learner input information, and time correlates further learner input information to the further learner output information to produce correlated assessment learner input information. The processing module combines the correlated lesson learner input information and the correlated assessment learner input information to produce the learner interaction information. 
     The method continues at step  358  where the processing module generates learning assessment results information based on the lesson interactions, assessment information, and the assessment interactions, where the learning assessment output information is utilized to enhance a subsequent lesson package. For example, the processing module summarizes correlated lesson learner input information of the collected lesson interactions and summarizes the correlated assessment learner input information. The processing module stores the learning asset results information in a learning assets database to enable subsequent improved learning effectiveness (e.g., modify the pace of a lesson, modify ordering of learning objectives, and modify a default view of the environment for a subsequent lesson experience). 
     The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system  10  of  FIG.  1    or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element, a sixth memory element, etc.) that stores operational instructions can, when executed by one or more processing modules of the one or more computing devices of the computing system  10 , cause the one or more computing devices to perform any or all of the method steps described above. 
       FIGS.  11 D and  11 E  are schematic block diagrams of another embodiment of a computing system illustrating another example of assessing learning experience retention (e.g., assessing learning comprehension). The computing system includes the experience execution module  32  of  FIG.  1    and the human interface module  18  of  FIG.  1   . The experience execution module  32  includes the environment generation module  240  of  FIG.  10 A  and the instance experience module  290  of  FIG.  10 A . 
       FIG.  11 D  illustrates an example of operation of assessing learning comprehension regarding a topic where, the instance experience module  290  modifies a fundamental illustrative model  770  to illustrate a first set of teaching assets of (e.g., of first teaching assets  780 - 1 ) of a first learning object  750 - 1  to produce a first learning illustrative model. The fundamental illustrative model  770  is based on illustrative assets  754  (e.g., representations of a cylinder wall, a piston, valves, etc.) of a lesson that includes the plurality of learning objects (e.g., including the first learning object  750 - 1  and a second learning object  750 - 2 ). 
     The instance experience module  290  outputs the first learning illustrative model to a second computing entity (e.g., the human interface module  18 ). For example, the instance experience module  290  generates the fundamental illustrative model  770  based on the illustrative asset  754 , modifies the fundamental illustrative model  770  to depict multidimensional aspects of the engine example intake stroke and compression stroke to produce the first learning illustrative model, and outputs, via the human interface module  18 , the first learning illustrative model as human output  162  to the learner  28 - 1 . 
       FIG.  11 D  further illustrates steps of a method of the example of operation of assessing learning comprehension regarding a topic where, in a first step, the experience execution module  32  modifies the fundamental illustrative model  770  to illustrate a first set of assessment assets (e.g., of first assessment asset  782 - 1 ) of the first learning object  750 - 1  of the plurality of learning objects to produce a first assessment illustrative model  784 . The producing of the first assessment illustrative model  784  includes one or more sub-steps. A first sub-step includes identifying a first teaching asset of the first learning object  750 - 1  as part of the first set of assessment assets. For instance, the instance experience module  290  identifies aspects of knowledge to demonstrate to the learner  28 - 1  with regards to the intake stroke and the compression stroke. 
     A second sub-step includes identifying a first assessment asset of the first learning object  750 - 1  as part of the first set of assessment assets. For example, the instance experience module  290  identifies comprehension testing items associated with the intake stroke and the compression stroke. A third sub-step includes generating a multi-dimensional representation of the first set of assessment assets. For example, the instance experience module  290  renders a three-dimensional model. 
     A fourth sub-step includes integrating the multi-dimensional representation of the first set of assessment assets and the fundamental illustrative model  770  to produce the first assessment illustrative model  784 . For example, the instance experience module  290  scales the representations together. As another example, the representations are aggregated. As yet another example, the instance experience module  290  selects common dimensionality for the representations. For instance, the instance experience module  290  produces a three-dimensional basic engine rendering of multiple objects that make up the engine (e.g., cylinder walls, piston, valves, spark plug) poised to capture a response from the learner  28 - 1  to test comprehension level. 
     Having produced the first assessment illustrative model  784 , the experience execution module  32  obtains a first assessment response for the first assessment illustrative model  784 . The obtaining of the first assessment response includes a second step of the method of the example of operation of assessing learning comprehension regarding the topic, where the experience execution module  32  outputs the first assessment illustrative model  784  to the second computing entity. For instance, the instance experience module  290  outputs the first assessment illustrative model  784  to the human interface module  18 , where the human interface module  18  sends human output  162  to the learner  28 - 1  based on the first assessment illustrative model  784 . 
     Having output the first assessment illustrative model  784 , in a third step of the method of the example of operation the experience execution module  32  receives the first assessment response from the second computing entity in response to the first assessment illustrative model  784 . For example, the instance experience module  290  interprets learner input information  174  from the human interface module  18  to produce the first assessment response (e.g., performing tasks such as pointing to various aspects of the intake and compression strokes and/or answering questions). 
     Having received the first assessment response, the experience execution module  32  updates the first assessment illustrative model  784  based on the first assessment response. For example, the instance experience module  290  interprets cursor movements from the learner input information  174  and re-renders the first assessment illustrative model  784  based on the cursor movements. 
     Having obtained the first assessment response, the experience execution module  32  indicates that the first assessment response is favorable when detecting one or more conditions. A first condition includes detecting completion of outputting the first assessment illustrative model to the second computing entity. For example, move forward with the lesson when the questions have been asked. 
     A second condition includes detecting an advancement indicator from the second computing entity. For example, move forward when the learner  28 - 1  indicates so. A third condition includes detecting a favorable learner comprehension level based on the first assessment response. For example, the instance experience module  290  evaluates the first assessment response by comparing the first assessment response to correct answers associated with the first assessment assets  782 - 1 . The instance experience module  290  indicates the favorable learner comprehension level when the first assessment response compares favorably to the correct answers (e.g., a minimum threshold number of learner answers are correct). 
     Having received the first assessment response, the experience execution module  32  further generates a first evaluation based on the first assessment response and the first set of assessment assets. For example, the instance experience module  290  generates the first evaluation based on the comparing of the first assessment response to the correct answers associated with the first assessment assets  782 - 1 . The first evaluation includes one or more of a list of correctly answered questions, a percentage of correctly answered questions, a list of incorrectly answered questions, and evaluation score, a pass fail indicator, and a lesson completion indicator. 
     Having generated the first evaluation, the experience execution module  32  outputs the first evaluation to another computing entity (e.g., a computing entity associated with the learner  28 - 1  to provide feedback). Having generated the first evaluation, the experience execution module  32  further updates a database record associated with a learner utilizing the first evaluation. For example, the instance experience module  290  identifies the database record associated with the learner  28 - 1  of the learning assets database  34  of  FIG.  1    and modifies the identified database record to indicate performance of the learner  28 - 1  with regards to the learning comprehension of aspects of the first learning illustrative model (e.g., the intake and compression strokes of the engine). 
       FIG.  11 E  illustrates further steps of the method of the example of operation of assessing learning comprehension regarding a topic where, when the first assessment response is favorable, the experience execution module  32  outputs a second learning illustrative model to the second computing entity. The fundamental illustrative model  770  is modified to illustrate a second set of teaching assets (e.g., of second teaching assets  780 - 2 ) of the second learning object  750 - 2 . For example, the instance experience module  290  modifies the fundamental illustrative model  770  to illustrate aspects of the power and exhaust strokes to produce the second learning illustrative model for output via the human interface module  18  to the learner  28 - 1  as human output  162 . 
     Having output the second learning illustrative model when the first assessment response is favorable, in a fourth step of the method of the example of operation, the instance experience module  290  modifies the fundamental illustrative model  770  to illustrate a second set of assessment assets (e.g., assessment aspects of the power and exhaust strokes of second assessment assets  782 - 2 ) of the second learning object  750 - 2  to produce a second assessment illustrative model  790 . 
     Having produced the second assessment illustrative model  790 , in a fifth step of the method of the example of operation, the instance experience module  290  outputs the second assessment illustrative model  790 , via the human interface module  18  to the learner  28 - 1  as human output  162 . For example, the learner  28 - 1  visualizes renderings of questions associated with the power and exhaust strokes. 
     Having output the second assessment illustrative model  790 , in a sixth step of the method of the example of operation, the instance experience module  290  obtains a second assessment response for the second assessment illustrative model. For example, the instance experience module  290  interprets learner input information  174  from the human interface module  18  based on human input  164  from the learner  28 - 1  to produce the second assessment response (e.g., answers to questions to test comprehension of the power and exhaust strokes). 
     The method described above in conjunction with the experience execution module  32  can alternatively be performed by other modules of the computing system  10  of  FIG.  1    or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element, a sixth memory element, etc.) that stores operational instructions can, when executed by one or more processing modules of the one or more computing devices of the computing system  10 , cause the one or more computing devices to perform any or all of the method steps described above. 
       FIGS.  12 A- 12 B  are schematic block diagrams of another embodiment of an experience execution module  32  of a computing system illustrating another example of executing a learning experience. The experience execution module  32  includes the environment generation module  240 , the instance experience module  290 , and the learning assessment module  330 . 
       FIG.  12 A  illustrates an example of operation where the instance experience module  290  generates an initial representation of a lesson package  206  utilizing a baseline environment and object information  292  to output learner output information  172  to a learner. The environment generation module  240  generates the baseline environment and object information  292  based on the lesson package  206  recovered from the learning assets database  34 . 
     The learning assessment module  330  selects an experience perception approach. The approaches include a manual approach where session control information from the learner takes control, a current adaptive session approach where adaptations to improve effectiveness of current real-time based on interactions of the learner, and a historical session adaptive approach where adaptations are based on historical learning assessment results information  370  (e.g., historical records of learning assessment results information including effectiveness information). 
     For example, the learning assessment module  330  selects manual when a session control information input from the learner indicates manual. As another example, the learning assessment module  330  selects current session adaptive when an effectiveness indicator associated with a set of experience perception parameters are below a minimum effectiveness threshold level. As yet another example, the learning assessment module  330  selects the historical session adaptive approach when an effectiveness indicator associated with a particular experience perception parameters set of historical learning assessment results information  370  is greater than the minimum effectiveness threshold level. 
     When the experience perception approach is manual, the instance experience module  290  modifies a current representation of the lesson package utilizing session control information from the learner interaction information from the learner. For example, the learning assessment module  330  updates a viewpoint of the representation based on repositioning information from the learner. 
       FIG.  12 B  further illustrates the example of operation where, when the experience perception approach is current session adaptive, the instance experience module modifies the current representation of the lesson package based on an effectiveness assessment of the current session. For example, the learning assessment module  330  determines an effectiveness level of the current session based on one or more of instruction information, assessment information  252 , and learner interaction information  332  to provide session control adaptation information  372 . 
     The instance experience module  290  updates the current representation of the lesson package utilizing the session control adaptation information  372  (e.g., update the viewpoint of the representation based on a previously utilized viewpoint that is associated with a favorable effectiveness level). Favorable effectiveness includes obtaining more correct answers, completing a lesson in less time, and skipping more learning objectives when possible. 
     When the experience perception approach is historical session adaptive, the instance experience module  290  modifies the current representation of the lesson package based on the historical learning assessment results information  370 . For example, the learning assessment module  330  identifies a particular experience perception parameters set of the historical learning assessment results information  370  to produce the session control adaptation information  372 . The instance experience module  290  updates the current representation of the lesson package utilizing the session control adaptation information  372 . For example, the instance experience module  290  updates a viewpoint of the representation based on a historically utilized viewpoint that is associated with a favorable effectiveness level. 
       FIG.  12 C  is a logic diagram of another embodiment of a method for executing a learning experience within a computing system (e.g., the computing system  10  of  FIG.  1   ). In particular, a method is presented in conjunction with one or more functions and features described in conjunction with  FIGS.  1 - 7 B , and also  FIGS.  12 A- 12 B . The method includes step  390  where a processing module of one or more processing modules of one or more computing devices within the computing system generates an initial representation of a lesson package to output learner output information to a learner. For example, the processing module utilizes baseline environment and object information to render the initial representation for output as the learner output information. 
     The method continues at step  392  where the processing module selects an experience perception approach. For example, the processing module selects manual when a session control information input from the learner indicates manual. As another example, the processing module selects current session adaptive when an effectiveness indicator associated with a set of experience perception parameters are below a minimum effectiveness threshold level. As yet another example, the processing module selects historical session adaptive when an effectiveness indicator associated with a particular experience perception parameters set of historical learning assessment results information is greater than the minimum effectiveness threshold level. 
     When the experience perception approach is manual, the method continues at step  394  where the processing module modifies a current representation of the lesson package utilizing session control information of learner interaction information from the learner. For example, the processing module updates a viewpoint of the representation based on repositioning information from the learner. 
     When the experience perception approach is current session adaptive, the method continues at step  396  where the processing module modifies the current representation of the lesson package utilizing an effectiveness assessment of a current session. For example, the processing module determines an effectiveness level of the current session based on one or more of instruction information, assessment information, and learner interaction information to produce session control adaptation information. The processing module utilizes the session control adaptation information to update the current representation of the lesson package. 
     When the experience perception approach is historical session adaptive, the method continues at step  398  where the processing module modifies the current representation of the lesson package utilizing historical learning assessment results information. For example, the processing module identifies a particular experience perception parameters set of the historical learning assessment results information to produce the session control adaptation information. The processing module updates the current representation of the lesson package utilizing the session control adaptation information. 
     The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system  10  of  FIG.  1    or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element, a sixth memory element, etc.) that stores operational instructions can, when executed by one or more processing modules of the one or more computing devices of the computing system  10 , cause the one or more computing devices to perform any or all of the method steps described above. 
       FIGS.  12 D- 12 G  are schematic block diagrams of another embodiment of a computing system illustrating an example of improving learning comprehension of a lesson within the computing system. The computing system includes the experience execution module  32  of  FIG.  1    and the human interface module  18  of  FIG.  1   . The experience execution module  32  includes the environment generation module  240  of  FIG.  10 A  and the instance experience module  290  of  FIG.  10 A . 
       FIG.  12 D  illustrates steps of an example of operation of the improving the learning comprehension of the lesson where, in a first step the instance experience module  290  modifies a fundamental illustrative model  770  to illustrate a first set of learning assets  802  of a first learning object  750 - 1  in accordance with an illustration approach  800  to produce a first learning illustrative model  804 . The lesson includes a plurality of learning objects (e.g., first learning object  750 - 1  and a second learning object  750 - 2 ) which includes the first learning object  750 - 1 . The environment generation module  240  generates the fundamental illustrative model  770  based on illustrative assets  754  of the lesson (e.g., representations of a cylinder wall, a piston, valves, etc.) as previously discussed. 
     The modifying of the fundamental illustrative model  770  to illustrate the first set of learning assets  802  of the first learning object  750 - 1  in accordance with the illustration approach  800  to produce the first learning illustrative model  804  includes a variety of one or more sub-steps. A first sub-step includes identifying a first teaching asset  781  of the first learning object  750 - 1  as part of the first set of learning assets  802 . For example, the instance experience module  290  selects a representation of an intake valve and a piston associated with first teaching asset  780 - 1 . 
     A second sub-step includes identifying a first assessment asset  782  of the first learning object  750 - 1  as part of the first set of learning assets  802 . For example, the instance experience module  290  accesses first assessment assets  782 - 1  selects a representation of arrows describing action vectors of an intake stroke along with a question “which stroke”? 
     A third sub-step of producing the first learning illustrative model  804  includes generating a multi-dimensional representation of the first set of learning assets  802 . For example, the instance experience module  290  generates a three-dimensional rendering of a combination of the representations of the intake valve, the piston, the arrows, and the text of the question to produce the first set of learning assets  802 . 
     A fourth sub-step of producing the first learning illustrative model  804  includes integrating the multi-dimensional representation of the first set of learning assets and the fundamental illustrative model  770  in accordance with the illustration approach to produce the first learning illustrative model  804 . For example, the instance experience module  290  combines the first set of learning assets  802  and the fundamental illustrative model  770  (e.g., remaining assets of the example including the cylinder walls, the exhaust valve, and the spark plug) and generates a three-dimensional rendering utilizing the illustration approach  800  to produce the first learning illustrative model  804 . For instance, the instance experience module  290  generates a side view cutout representation of a three-dimensional rendering of the piston and associated parts to illustrate the intake stroke and pose the question. 
     The illustration approach  800  provides guidance with respect to generating the multidimensional representations of assets associated with the lesson. The guidance includes a viewpoint of one or more learners (e.g., perspective such as top view, side view, angle view, etc.), a scale of a view, the scope of the view, a speed of delivery of the lesson (e.g., too fast may be too challenging for the learner and negatively impact comprehension), a number of views (e.g., clicking through a cutout view followed by a top view followed by a bottom view etc.), and applying highlighting to the view (e.g., shading, colors, flashing, etc. to highlight an element). 
     The illustration approach  800  can impact the representation of the lesson to the learner and hence a comprehension outcome. The illustration approach  800  starts with a default value and is updated over time based on learner comprehension outcomes. For example, a particular viewpoint that is correlated with higher levels of comprehension by initial learners is utilized for subsequent lesson delivery sessions for subsequent learners. As another example, a particular speed of delivery that is correlated with lower levels of comprehension by the initial learners is adjusted to a slower pace for the subsequent lesson delivery sessions for the subsequent learners. 
       FIG.  12 E  further illustrates steps of the example of operation of the improving the learning comprehension of the lesson where, having produced the first learning illustrative model  804 , in a second step the instance experience module  290  obtains a first comprehension evaluation  808  for the first learning illustrative model  804 . The first learning illustrative model  804  includes the first assessment asset  782  (e.g., which stroke?) of the first learning object  750 - 1 . 
     The obtaining of the first comprehension evaluation  808  for the first learning illustrative model  804  includes a series of sub-steps. A first sub-step includes outputting the first learning illustrative model  804  to a second computing entity (e.g., the human interface module  18 ). For example, the instance experience module  290  outputs, via the human interface module  18 , the first learning illustrative model  804  as human output  162  to the learner  28 - 1 . For instance, the learner  28 - 1  is able to visualize a side view of the piston travel in the cylinder and the air fuel mixture entering the cylinder through the open intake valve along with the question, which stroke? 
     A second sub-step includes the instance experience module  290  obtaining a first assessment response  806  for the first learning illustrative model  804 . The obtaining includes recovering a historical record for a first assessment response  806  associated with a previous lessons session (e.g., and another learner). The obtaining further includes receiving the first assessment response  806  from the human interface module  18 , where the human interface module  18  interprets human input  164  from the learner  28 - 1  (e.g., answer to the question of which stroke) in response to visualizing the side view of the piston travel in cylinder etc. 
     A third sub-step includes generating the first comprehension evaluation  808  based on the first assessment response  806  and the first assessment asset  782 . For example, the instance experience module  290  produces the first comprehension evaluation  808  to indicate that the learner  28 - 1  answered the question incorrectly when visualizing the first learning illustrative model  804  based on the (current) illustration approach  800 . 
       FIG.  12 F  further illustrates steps of the example of operation of the improving the learning comprehension of the lesson where, having produced the first comprehension evaluation, in a third step the instance experience module  290  modifies the illustration approach  800  based on the first comprehension evaluation  808  for the first learning illustrative model to produce an updated illustration approach  810 . The first comprehension evaluation  808  indicates that a portion of the lesson is too easy when comprehension is greater than an expected high comprehension threshold level. For example, when the learner  28 - 1  answers too many questions correctly as compared to an expected level of correctness. Alternatively, the first comprehension evaluation  808  indicates that the portion of the lesson is too difficult when the comprehension is less than an expected low comprehension threshold level. For example, when the learner  28 - 1  answers too few questions correctly as compared to the expected level of correctness. 
     When the first comprehension evaluation indicates that the portion of the lesson is too easy, the modifying of the illustration approach  800  includes updating the illustration approach  800  producing the updated illustration approach  810  to include one or more of a more challenging viewpoint perspective, a larger scale of a view, a larger scope of the view, a faster speed of delivery, a higher number of views, and more highlighting of the view. For instance, the instance experience module  290  updates the illustration approach  800  to provide a 15% increase in the speed of delivery to produce the updated illustration approach  810  when the first comprehension evaluation  808  indicates that the learner  28 - 1  achieved a comprehension level above the expected high comprehension threshold level for the first learning illustrative model  804 . 
     When the first comprehension evaluation indicates that the portion of the lesson is too difficult, the modifying of the illustration approach  800  includes updating the illustration approach  800  producing the updated illustration approach  810  to include one or more of a less challenging viewpoint perspective, a smaller scale of the view, a smaller scope of the view, a slower speed of the delivery, a lower number of the views, and less highlighting of the view. For instance, the instance experience module  290  updates the illustration approach  800  to provide a 30° clockwise rotation of the view and a 10% zoom in to produce the updated illustration approach  810  when the first comprehension evaluation  808  indicates that the learner  28 - 1  did not fully understand the first learning illustrative model  804 . 
       FIG.  12 G  further illustrates steps of the example of operation of the improving the learning comprehension of the lesson where, having produced the updated illustration approach, in a fourth step the instance experience module  290  modifies the fundamental illustrative model  770  to illustrate a second set of learning assets  816  of the second learning object  750 - 2  in accordance with the updated illustration approach a 10 to produce a second learning illustrative model  818 . The modifying the fundamental illustrative model  770  to illustrate the second set of learning assets  816  in accordance with the updated illustration approach a 10 to produce the second learning illustrative model  818  includes one or more of a variety of sub-steps. 
     A first sub-step includes identifying a second teaching asset  812  of the second learning object  750 - 2  as part of the second set of learning assets  816 . For example, the instance experience module  290  accesses second teaching assets  780 - 2  to select the piston and the exhaust valve as the second teaching asset  812  to teach and/or assess with regards to the exhaust stroke. 
     A second sub-step includes identifying a second assessment asset  814  of the second learning object  750 - 2  as part of the second set of learning assets  816 . For example, the instance experience module  290  accesses second assessment asset  782 - 2  to select an action vector directional arrow for the piston closing in on the exhaust valve, another action vector directional arrow indicating the direction of exhaust flowing out through the exhaust valve, and an assessment question of “which stroke?” 
     A third sub-step of producing the second learning illustrative model  818  includes generating a multi-dimensional representation of the second set of learning assets  816 . For example, the instance experience module  290  generates a three-dimensional rendering of a combination of the representations of the exhaust valve, the piston, the arrows, and the text of the question to produce the second set of learning assets  816 . 
     A fourth sub-step of producing the second learning illustrative model  818  includes integrating the multi-dimensional representation of the second set of learning assets  816  and the fundamental illustrative model  770  in accordance with the updated illustration approach  810  to produce the second learning illustrative model  818 . For example, the instance experience module  290  combines the second set of learning assets  816  and the fundamental illustrative model  770  (e.g., remaining assets of the example including the cylinder walls, the exhaust valve, and the spark plug) and generates a three-dimensional rendering utilizing the updated illustration approach  810  (e.g., with the 10% zoom and 30% clockwise rotation) to produce the second learning illustrative model  818 . For instance, the instance experience module  290  generates a new side view cutout representation of the three-dimensional rendering of the piston and associated parts to illustrate the exhaust stroke with better intended comprehension and pose the question. 
     Alternatively, or in addition to, the instance experience module  290  performs a variety of further steps of the method. For example, the instance experience module  290  outputs the second learning illustrative model  18  to a second computing entity. For instance, via the human interface module  18  to the learner  28 - 1 . The instance experience module  290  obtains a second comprehension evaluation for the second learning illustrative model and modifies the updated illustration approach  810  based on the second comprehension evaluation for the second learning illustrative model to produce a further updated illustration approach. For instance, the instance experience module  290  determines that further comprehension improvements are required when the learner  28 - 1  scores lower than expected on the further comprehension. 
     Having modified the illustration approach  800  to produce the further updated illustration approach, the instance experience module modifies the second learning illustrative model in accordance with the further updated illustration approach to produce an updated second learning illustrative model. For instance, the instance experience module  290  provides a viewpoint that is further rotated and where the speed of delivery is slowed down to support improved comprehension. The instance experience module  290  outputs the updated second learning illustrative model to the second computing entity. For instance, via the human interface module  18  to the learner  28 - 1 . 
     The method described above in conjunction with the experience execution module  32  can alternatively be performed by other modules of the computing system  10  of  FIG.  1    or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element, a sixth memory element, etc.) that stores operational instructions can, when executed by one or more processing modules of the one or more computing devices of the computing system  10 , cause the one or more computing devices to perform any or all of the method steps described above. 
       FIGS.  13 A- 13 B  are schematic block diagrams of another embodiment of an experience execution module  32  of a computing system illustrating an example of sharing an assessment of a learning experience. The experience execution module  32  includes the environment generation module  240 , the instance experience module  290 , the learning assessment module  330 , and a scoring module  410 . 
       FIG.  13 A  illustrates an example of operation where the learning assessment module  330  generates learning assessment results information  334  based on learner interaction information  332  for present and previous learning experiences storing the learning assessment results information  334  in the learning assets database  34 . The instance experience module  290  generates the learner interaction information  332  based on assessment interactions of learner input information  174 . 
     The instance experience module  290  outputs a representation of assessment information  252  of a lesson package  206  to a learner as learner output information  172 . The instance experience module  290  further generates the representation of the assessment information  252  based on baseline environment and object information  292  generated by the environment generation module  240  based on the lesson package  206 . 
     The scoring module  410  interprets the learning assessment results information  334  to produce scoring information  412  for storage in the learning assets database  34 . For example, the scoring module  410  accesses the learning assessment results information  334  for the learner and scores portions of learning experiences, i.e., comparing correct answers of a learning objective to answers provided by the learner to produce a raw scoring element and summarizing a set of raw scoring elements to produce a score. 
     The scoring module  410  updates learning assessment results information stored in the learning assets database  34  by modifying scoring information  412  associated with the learner based on the scoring information from the current session. For example, the scoring module  410  recovers scoring information associated with the learner from the learning assets database (e.g., previous sessions) and identifies a portion of the recovered scoring information  412  to be updated based on the scoring information associated with the present learning experience. For instance, the scoring module  410  adds a score to a running transcript for the learner indicating completion of another learning objective towards an advanced degree. 
     The scoring information  412  includes one or more of a learner identifier, a learning objective identifier, and a lesson identifier for each scored learning objective. The scoring information further includes a number of right and wrong answers for each learning objective and an overall learning objective evaluation score. The scoring information further includes a list of learning objectives successfully passed and failed. The scoring information further includes identifiers of accreditation sought (e.g., a degree in progress, a certificate in progress, a license in progress, etc.) and requirements for the accreditation sought. 
       FIG.  13 B  further illustrates the example of operation where the scoring module  410  facilitates sharing of the scoring information  412  of the learning assets database  34  with other computing entities utilizing a blockchain approach to securely acquire and update a transcript information blockchain  414 . The transcript information blockchain  414  includes a blockchain that wraps scoring information and self authenticates validity of the scoring information between computing entities. The structure of the transcript information blockchain is discussed in greater detail with reference to  FIG.  13 D . 
     In an example of operation the scoring module  410  obtains a previous blockchain associated with the scoring information to be shared and updates content associated with the previous blockchain. The scoring module  410  further generates a subsequent blockchain around the updated content utilizing the previous blockchain and shares the subsequent blockchain with the other computing entity in accordance with a blockchain procedure utilized between authorize computing entities. The procedure is discussed in greater detail with regards to  FIGS.  13 C- 13 D . 
     The scoring module  410  interprets the transcript information blockchain  414  to determine earned credentials. For example, the scoring module  410  obtains the transcript information blockchain  414 , i.e., retrieve from memory, received from another computing entity, and authenticates the transcript information blockchain utilizing the blockchain approach. When authenticated, the scoring module  410  extracts scoring information from a content portion of the blockchain and interprets the scoring information with regards to accreditation requirements. 
       FIG.  13 C  is a schematic block diagram of another embodiment of the computing system  10  illustrating an example of sharing an assessment of a learning experience. The computing system includes a plurality of computing entities  20 - 1  through  20 -N. in an example of operation, the computing entity  20 - 1  outputs the transcript information blockchain  414  to the computing entity  20 - 2 . The computing entity  20 - 2  authenticates the transcript information blockchain  414 . When authenticated, the computing entity  20 - 2  extracts scoring information from the authenticated transcript information blockchain  414 . The computing entity  20 - 2  interprets and/or updates the extracted scoring information. 
     The process continually repeats where the computing entity  20 - 2  generates an updated transcript information blockchain  414  to output to another computing entity etc. As a result, any number of computing entities may provide learning experiences that in aggregate, as component learning objectives are successfully accomplished, enables a learner to complete an accreditation. 
       FIG.  13 D  is a schematic block diagram of an embodiment of a transcript information blockchain of a computing system illustrating an example of sharing an assessment of a learning experience. The example transcript information blockchain  414  includes a plurality of blocks  2 - 4 . Each block includes a header section and a transaction section. The header section includes one or more of a nonce, a hash of a preceding block of the blockchain, where the preceding block was under control of a preceding device (e.g., a computing entity, a computing device, etc.) in a chain of control of the blockchain, and a hash of a current block (e.g., a current transaction section). The current block is under control of a current device in the chain of control of the blockchain. 
     The transaction section includes one or more of a public key of the current device, a signature of the preceding device, authentic transcript request information regarding a transcript request and change of control from the preceding device to the current device, and content information (e.g., scoring information) from the previous block as received by the previous device plus content added by the previous device when transferring the current block to the current device. 
     The example further includes devices  2 - 3  to facilitate illustration of generation of the blockchain. Each device includes a hash function, a signature function, and storage for a public/private key pair generated by the device. 
     An example of operation of the generating of the blockchain, when the device  2  has control of the blockchain and is passing control of the blockchain to the device  3  (e.g., the device  3  is transacting a transfer of content from device  2 ), the device  2  obtains the device  3  public key from device  3 , performs a hash function  2  over the device  3  public key and the transaction  2  to produce a hashing resultant (e.g., preceding transaction to device  2 ) and performs a signature function  2  over the hashing resultant utilizing a device  2  private key to produce a device  2  signature. 
     Having produced the device  2  signature, the device  2  generates the transaction  3  to include the device  3  public key, the device  2  signature, device  3  authentic transcript request to device  2  information, and the previous content plus content from device  2 . The device  3  authentic transcript request to device  2  information includes one or more of a transcript request, a query request, background content, and routing instructions from device  3  to device  2  for access to the content. The previous content plus content from device  2  includes one or more of content from an original source, content from any subsequent source after the original source, an identifier of a source of content, a serial number of the content, an expiration date of the content, content utilization rules, and results of previous blockchain validations. 
     Having produced the transaction  3  section of the block  3  a processing module (e.g., of the device  2 , of the device  3 , of a transaction mining computing entity, of a computing device, generates the header section by performing a hashing function over the transaction section  3  to produce a transaction  3  hash, performing the hashing function over the preceding block (e.g., block  2 ) to produce a block  2  hash. The performing of the hashing function may include generating a nonce such that when performing the hashing function to include the nonce of the header section, a desired characteristic of the resulting hash is achieved (e.g., a desired number of preceding zeros is produced in the resulting hash). 
     Having produced the block  3 , the device  2  sends the block  3  to the device  3 , where the device  3  initiates control of the blockchain. Having received the block  3 , the device  3  validates the received block  3 . The validating includes one or more of verifying the device  2  signature over the preceding transaction section (e.g., transaction  2 ) and the device  3  public key utilizing the device  2  public key (e.g., a re-created signature function result compares favorably to device  2  signature) and verifying that an extracted device  3  public key of the transaction  3  compares favorably to the device  3  public key held by the device  3 . The device  3  considers the received block  3  validated when the verifications are favorable (e.g., the authenticity of the associated content is trusted). For instance, the device considers the transcript records intact, valid, and usable to facilitate determination of status of an accreditation. 
       FIG.  13 E  is a logic diagram of an embodiment of a method for sharing an assessment of a learning experience within a computing system (e.g., the computing system  10  of  FIG.  1   ). In particular, a method is presented in conjunction with one or more functions and features described in conjunction with  FIGS.  1 - 7 B , and also  FIGS.  13 A- 13 D . The method includes step  430  where a processing module of one or more processing modules of one or more computing devices within the computing system generates learning assessment results information based on learner action for learning experiences. For example, the processing module stores learning assessment results information in a learning assets database, where learner interaction information is generated based on assessment interactions of learner input information. The processing module outputs a representation of assessment information of a lesson package to a learner as learner output information. The processing module generates the representation of the assessment information based on a baseline environment and objects and assessment information. 
     The method continues at step  432  where the processing module interprets the learning assessment results information to produce scoring information. For example, the processing module accesses the learning assets results information for the learner and scores portions of the learning experiences, i.e., comparing correct answers of a learning objective two answers provided by the learner to produce a raw-scoring element. 
     The method continues at step  434  where the processing module updates learning asset information by modifying recovered scoring information with the scoring information. For example, the processing module recovers scoring information associated with the learner from the learning assets database and identifies a portion of the recovered scoring information to be updated based on the scoring information associated with the present learning experience. For instance, the processing module adds a set of learning objective scores to a running transcript for the learner indicating completion of another set of learning objectives towards an accreditation. 
     The method continues at step  436  where the processing module facilitates sharing of the scoring information utilizing a blockchain approach by acquiring and updating a transcript information blockchain. For example, the processing module obtains a previous blockchain associated with the scoring information to be shared and updates a content portion associated with the previous blockchain. The processing module generates a subsequent blockchain around the updated content utilizing the previous blockchain in chairs the subsequent blockchain with another computing entity in accordance with a blockchain procedure that ensures authenticity. 
     The method continues at step  438  where the processing module interprets the transcript information blockchain to determine and authenticated earn credential. For example, the processing module obtains the transcript information blockchain and authenticates the transcript information blockchain utilizing the blockchain approach. When authenticated, the processing module extracts going information from the content portion of the blockchain and interprets the scoring information with regards to the earned credential to determine a status associated with accreditation. 
     The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system  10  of  FIG.  1    or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element, a sixth memory element, etc.) that stores operational instructions can, when executed by one or more processing modules of the one or more computing devices of the computing system  10 , cause the one or more computing devices to perform any or all of the method steps described above. 
       FIG.  14 A  is a schematic block diagram of another embodiment of an experience creation module  30  of a computing system illustrating an example of creating a lesson. The experience creation module  30  includes the learning assessment module  330  and a lesson generation module  450 . In an example of operation, the learning assessment module  330  obtains learning objectives to be associated with an enhanced lesson package. For example, the learning assessment module  330  extracts the learning objectives from a lesson package  206  from the learning assets database  34 . 
     The learning assessment module  330  recovers historical learning assessment results information  370  associated with the learning objectives. For example, the learning assessment module  330  identifies the learning objectives, accesses the learning assets database  34  utilizing identities of the learning objectives to retrieve the associated historical learning assessment results information  370 . 
     The learning assessment module  330  identifies a set of optimizations based on previous utilization only some of the learning objectives based on the recovered historical learning assessment results information  370 . For example, the learning assessment module  330  tests for various potential optimizations such as ordering, baseline speed, instructor selection, learner categorization, etc. and aggregates hypothesis potential optimizations to form lesson effectiveness information  452 . 
     When the set of optimizations has an expected effectiveness level less than a minimum desired effectiveness level, the lesson generation module  450  facilitates performing one or more learning experiments to identify favorable optimizations. For example, the lesson generation module  450  modifies ordering of learning objectives for a lesson to be subsequently delivered and assessed and gathers assessments for the subsequent delivered lesson to identify a favorable set of optimizations. The lesson effectiveness information  452  includes, for each set of learning objectives that form one or more lessons, performance metrics including time, a comprehension level, a learner rating, and instructor rating, and any other metric associated with performance 
     The lesson generation module  450  generates an enhanced lesson package  454  utilizing the obtained learning objectives in accordance with the set of optimizations. For example, the lesson generation module  450  builds the enhanced lesson package  454  in accordance with parameters of the set of optimizations. For example, in a particular order with a particular speed and utilizing a particular instructor when a choice of instructors is available. The enhanced lesson package includes a set of learning objectives and/or lessons, a particular ordering of the learning objectives, where the ordering is optimize based on previous results (e.g., for comprehension, for time of execution of learning). The enhanced lesson package  454  may represent updating of previously stored lesson package  206  as well as generation of an entirely new lesson package. 
       FIG.  14 B  is a schematic block diagram of another embodiment of a representation of a learning experience where a set of four learning objectives are executed amongst a set of learners, where assessment information includes speed of execution and comprehension level obtained by the set of learners. The learning objectives are executed in accordance with experimentation to identify optimizations. 
     In the example, the second experience re-ordered learning objective  3  is ahead of learning objective  2  as compared to experience  1 . A comparison of assessment information indicates that the speed of execution and comprehension level of the learning objective  2  is more favorable when sequenced after the learning objective  3 . 
     When the optimization has been identified, a new lesson is generated where the learning objective  2  is ordered after the execution of the learning objective  3 . With the reordering, a comprehension and speed of execution optimization is expected. 
       FIG.  14 C  is a logic diagram of an embodiment of a method for creating a lesson within a computing system (e.g., the computing system  10  of  FIG.  1   ). In particular, a method is presented in conjunction with one or more functions and features described in conjunction with  FIGS.  1 - 7 B , and also  FIGS.  14 A- 14 B . The method includes step  470  where a processing module of one or more processing modules of one or more computing devices within the computing system obtains learning objectives to be associated with an enhanced lesson package. The obtaining includes at least one of receiving, extracting from a lesson package retrieved from a learning assets database, and generating based on new learnings. 
     The method continues at step  472  where the processing module recovers historical learning assessment results information associated with the learning objectives. For example, the processing module identifies the learning objectives and accesses the learning assets database utilizing the identities of the learning objectives to retrieve the associated historical learning assessment results information. 
     The method continues at step  474  where the processing module identifies a set of optimizations based on previous utilization of at least some of the learning objectives based on the recovered historical learning assessment results information. For example, the processing module tests for various potential optimizations and aggregates hypothesis potential optimizations to form the set of optimizations. 
     When the set of optimizations has an unfavorable expected effectiveness level, the method continues at step  476  where the processing module facilitates performing one or more learning experiments to identify favorable optimizations for the set of optimizations. For example, the processing module modifies ordering of learning objectives for a lesson to be subsequently delivered and assessed. The processing module further gathers assessments for the subsequent delivered lesson and identifies a favorable set of optimizations. 
     The method continues at step  478  where the processing module generates the enhanced lesson package utilizing the obtained learning objectives in accordance with the set of optimizations. For example, the processing module builds the enhanced lesson package in accordance with parameters of the set of optimizations. For example, the processing module selects a particular order with a particular speed and with a particular selection of an instructor associated with favorable results. 
     The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system  10  of  FIG.  1    or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element, a sixth memory element, etc.) that stores operational instructions can, when executed by one or more processing modules of the one or more computing devices of the computing system  10 , cause the one or more computing devices to perform any or all of the method steps described above. 
       FIGS.  15 A- 15 B  are schematic block diagrams of another embodiment of an experience creation module  30  of a computing system illustrating an example of creating a lesson. The experience creation module  30  includes the concept generation module  490 , the asset module  182 , the instruction module  184 , a learning objective generation module  492 , and the lesson generation module  450 . 
       FIG.  15 A  illustrates an example of operation where the concept generation module  490  determines whether learning objective concept information  500  is in compliance with learning objective concept guidance. The learning objective concept information includes a concept description that includes an actor, and action, i.e. performed by the actor, and a victim, i.e., receiving the action. For example, the fuel enters the cylinder when the intake valve opens. The determining includes indicating compliance when the learning objective concept information includes a concept description involving the actor, the action, and the victim of the action. 
     When the learning objective concept information is not in compliance, the concept generation module  490  facilitates a resolution. The facilitating includes one or more of requesting updated learning objective concept information, modifying the learning objective concept information to bring into compliance, and recovering a similar complaint concept description from learning path information  194  retrieved from the learning assets database  34 . 
     When the learning objective concept information is in compliance, the concept generation module  490  outputs concept information  502  that includes the concept description. For example, the concept generation module  490  generates and outputs the concept information  502  to include the complaint concept description. 
       FIG.  15 B  further illustrates the example of operation where the learning objective generation module  492  generates learning objective information  504  to include a learning objective utilizing the concept information, lesson asset information  202  based on supporting asset information  198 , and instruction information  204  based on instructor input information  166  in response to instructor output information  160 . For example, the learning objective generation module  492  maps the concept description to learning objective tasks utilizing assets of the lesson asset information  202  as manipulated in accordance with the instruction information  204  to impart knowledge associated with the concept description of the concept information  502 . 
     The lesson generation module  450  generates a lesson package  206  to include the learning objective information  504  for storage in the learning assets database  34 . For example, the lesson generation module  450  portrays a multidimensional representation of the learning objective within an environment utilizing objects of the environment to produce the lesson package  206 . 
       FIG.  15 C  is a logic diagram of another embodiment of a method for creating a lesson within a computing system (e.g., the computing system  10  of  FIG.  1   ). In particular, a method is presented in conjunction with one or more functions and features described in conjunction with  FIGS.  1 - 7 B , and also  FIGS.  15 A- 15 B . The method includes step  520  where a processing module of one or more processing modules of one or more computing devices within the computing system determines whether learning objective concept information is in compliance with learning objective concept guidance. For example, the processing module indicates compliance when the learning objective concept information includes a concept description involving an actor, and action, and a victim of the action. 
     When the learning objective concept information is not in compliance, the method continues at step  522  where the processing module facilitates a resolution. For example, the processing module performs one or more of requesting updated learning objective concept information, modifying the learning objective concept information to bring to compliance, and recovering a similar and compliant concept description from a learning path retrieved from a learning assets database. 
     When resolved, the method continues at step  524  where the processing module issues concept information that includes a concept description. For example, the processing module generates and sends the concept information to include the complaint concept description. 
     The method continues at step  526  where the processing module generates learning objective information to include a learning objective utilizing the concept information, lesson asset information based on supporting asset information, and instruction information based on instructor input information in response to instructor output information. For example, the processing module maps the concept description to learning objective tasks utilizing assets of the lesson asset information as manipulated in accordance with the instruction information to impart knowledge associated with the concept description. 
     The method continues at step  528  where the processing module generates a lesson package to include the learning objective for storage in a learning asset database. For example, the processing module renders a multidimensional representation of the learning objective within an environment utilizing objects of the environment in accordance with the learning objective concept information. 
     The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system  10  of  FIG.  1    or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element, a sixth memory element, etc.) that stores operational instructions can, when executed by one or more processing modules of the one or more computing devices of the computing system  10 , cause the one or more computing devices to perform any or all of the method steps described above. 
       FIGS.  16 A- 16 B  are schematic block diagrams of another embodiment of an experience creation module  30  of a computing system illustrating an example of creating a lesson.  FIG.  16 A  illustrates an example of operation where the concept generation module  490  generates concept information  502  based on lesson concept information  540 . The lesson concept information  540  includes input from an instructor on a lesson that includes a set of learning objectives, where each learning objective is associated with a concept description that includes a set of actors, actions, and victims. For example, the fuel enters the cylinder when the intake valve opens, the piston compresses the air/fuel mix, and the spark plug ignites the air/fuel mix. 
     The instruction module  184  accesses lesson package  206  from the learning assets database  34  based on the concept information  502  to identify candidate instruction information  204  that can potentially portray step-by-step instructions to coincide with a sequence of the lesson. For example, the processing module identifies the set of actors, actions, and victims from the concept information  502  and searches the learning assets database  34  to locate examples of other learning objectives that include similar items. 
     The instruction module  184  selects elements of the candidate instruction information that favorably portrays desired step-by-step instructions of the lesson to produce instruction information  204 . For example, the instruction module  184  compares permutations of the candidate instruction information to the desired step-by-step instructions and outputs instruction information the compares favorably. 
       FIG.  16 B  further illustrates the example of operation where the learning objective generation module  492  generates learning objective information  504  to include a learning objective utilizing lesson asset information  202  based on supporting asset information  198 , the instruction information  204 , and in accordance with the step-by-step instructions of the concept information  502 . For example, the learning objective generation module  492  generates an environment associated with instruction information  204 , adds assets of the lesson asset information  202  to the environment, selects instruction object annotations of the instruction information  204  in accordance with the step by step instructions of the concept information  502  to produce the learning objective information  504 . 
     The lesson generation module  450  generates a lesson package to include the set of learning objectives for storage in the learning assets database as an auto-generated lesson package  542 . For example, the lesson generation module  450  portrays a multidimensional representation of the set of learning objectives within the environment utilizing objects of the environment and in accordance with the steps of the instruction information  204  to generate the auto-generated lesson package  542  for storage in the learning assets database  34 . 
       FIG.  16 C  is a logic diagram of another embodiment of a method for creating a lesson within a computing system (e.g., the computing system  10  of  FIG.  1   ). In particular, a method is presented in conjunction with one or more functions and features described in conjunction with  FIGS.  1 - 7 B , and also  FIGS.  16 A- 16 B . The method includes step  560  where a processing module of one or more processing modules of one or more computing devices within the computing system generates concept information based on lesson concept information. For example, the processing module identifies sets of actors, actions, and victims to identify a sequence of objects within an environment portrays series of concepts associated with the lesson. 
     The method continues at step  562  where the processing module identifies candidate instruction information based on the concept information. For example, the processing module identifies the set of actors, actions, and victims from the concept information, and searches a learning assets database to locate examples of other learning objectives that include similar items. 
     The method continues at step  564  where the processing module selects elements of the candidate instruction information that favorably portrays step-by-step instructions of the concept information to produce instruction information. For example, the processing module compares permutations of the candidate instruction information to the desired step-by-step instructions and outputs instruction information the compares favorably. 
     The method continues at step  566  where the processing module generates a set of learning objectives based on the instruction information intensive information. For example, the processing module generates an environment associated with instruction information and adds assets of the lesson assets to the environment. The processing module further selects instruction object annotations of the instruction information in accordance with step-by-step instructions of the concept information to produce the set of learning objectives of the lesson. 
     The method continues at step  568  where the processing module generates a lesson package to include the set of learning objectives in accordance with the concept information. For example, the processing module betrays a multidimensional representation of the set of learning objectives within the environment utilizing objects of the environment. 
     The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system  10  of  FIG.  1    or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element, a sixth memory element, etc.) that stores operational instructions can, when executed by one or more processing modules of the one or more computing devices of the computing system  10 , cause the one or more computing devices to perform any or all of the method steps described above. 
       FIGS.  17 A- 17 B  are schematic block diagrams of another embodiment of an experience execution module  32  of a computing system illustrating another example of executing a learning experience. The experience execution module  32  includes the environment generation module  240  a set of instance experience modules  290 - 1  through  290 -N, and a common experience module  580 . 
       FIG.  17 A  illustrates an example of operation where, for each learner of N learners experiencing an instruction portion of a common lesson, a corresponding instance experience module  290  establishes a representation of the lesson utilizing baseline environment and object information  292  in accordance with instruction information  204  to output corresponding learner output information  172  to a corresponding learner. The environment generation module  240  generates the baseline environment and object information  292  based on a lesson package  206  recovered from the learning assets database  34 . The establishing of the representation includes generating the representation of the environment with the objects, i.e., including the representation of substantially all of the learners, in accordance with the instruction information  204  (e.g., a starting configuration), and outputting the representation to the corresponding learner where the common lesson has initiated playing. 
     For each learner, the corresponding instance experience module  290  selects a reference time frame of playing of the common lesson. For example, the set of learners is divided into two groups were a first group of learners is associated with a first time reference and speeds ahead experiencing the instruction of the lesson within the environment that includes a representation of just the first group of learners. A second group of learners is associated with a second time reference and runs the common lesson at a slower pace but visualizes the interaction of the first group within the environment in addition to the second group of learners. 
     The selecting of the time reference is based on one or more of an estimated learner capability level, i.e., advanced versus mainstream, a manual request, and prerequisite completed learning objectives by a particular. For example, a more capable learner is affiliated with the first group of learners that is associated with the first time a reference that speeds ahead. 
     When an instruction portion of the common lesson is playing, each instance experience module  290  outputs the unique representation of the lesson in accordance with a corresponding reference time frame as learner output information, i.e., same environment/objects as others within the common group but with a potentially different viewpoint. The instance experience module  290  interprets corresponding learner input information  174  in response to the learner output information  172  produce corresponding learner interaction information  332 - 1  through  332 -N. The interpreting includes extracting session control information to adjust to viewpoint of the corresponding learner output information (e.g., view directions, zoom level, etc.), and outputting the corresponding session control information to the common experience module  580  to facilitate group control and experience processing. 
       FIG.  17 B  further illustrates the example of operation where, when the instruction portion of the common lesson is playing, the common experience module  580  interprets learner interaction information  332  from each learner to produce session control adaptation information  372 - 1  through  372 -N to control and portray all instances of the common lesson (e.g., all stop at once, all start again at the same time, one group stops while the other continues, etc.) to facilitate a common experience. The interpreting includes selecting one or more common time frames that learners are synchronized with (e.g., everyone watches one common time reference of playing the session, the group is divided into four different speeds of playing the session), and identifying the representations of motions of each learner to include in the session control adaptation information. 
     The common experience module  580  further identifies session control information of each learner and aggregates the representations of the motions of the learners, i.e., all can see each other unless invisibility desired and determines the common session control adaptation information in accordance with a control approach, (e.g., anyone can start stop the session, anyone can speed up/slow down the playing, playing at a speed faster than a slowest request, etc.). The common experience module  580  further selects learners that may speed have the rest and their motions are portrayed in parallel with the instructor to facilitate improved instruction to learners running at a slower pace. 
     The common experience module  580  facilitates execution of an assessment portion of the common lesson, where the facilitation is in accordance with an assessment of approach of the assessment information  252 . The assessment approaches includes private assessments, open group assessments where other learners experience testing of other learners, etc. The facilitating includes selecting the assessment approach, i.e., manual input, in accordance with a configuration aspect of the common lesson, based on historical results, etc. The facilitating further includes outputting of the assessment utilizing the assessment information  252 , interpreting learner interaction information  332  to produce an assessment, and pretraining the learner interaction information as updated session control adaptation information  372  to update the representation of the assessment portion as modified learner output information  172 - 1  through  172 -N. 
       FIG.  17 C  is a logic diagram of another embodiment of a method for executing a learning experience within a computing system (e.g., the computing system  10  of  FIG.  1   ). In particular, a method is presented in conjunction with one or more functions and features described in conjunction with  FIGS.  1 - 7 B , and also  FIGS.  17 A- 17 B . The method includes step  600  where a processing module of one or more processing modules of one or more computing devices within the computing system establishes a set of representations of an instruction portion of a lesson for a set of learners. For example, the processing module generates a representation, for each learner, of an environment with objects (e.g., including the representation of at least some of the learners) in accordance with instruction information for a starting configuration. The processing module outputs the representations to the learner while initiating playing of the lesson. 
     For each learner, the method continues at step  602  where the processing module selects a reference time frame for playing the instruction portion. For example, the processing modules selects the reference time frame based on one or more of an estimated learner capability level, a manual request, and prerequisite completed learning objectives by the learner. 
     While executing the instruction portion, the method continues at step  604  where the processing module interprets learner input information in response to updated representations of the instruction portion to produce learner interaction information. For example, the processing module extracts session control information to adjust a viewpoint of the corresponding learner output information and generates corresponding session control information to facilitate group control and experience processing. 
     The method continues at step  606  where the processing module interprets the learner interaction information from each learner to produce session control adaptation information to further update the representations of the instruction portion. For example, the processing module, for each reference time frame, identifies representations of motions for each learner to include in the session control adaptation information, and identifies session control information of each learner. The processing module further aggregates the representations of the motions of the learners to update the representations of the instruction portion. 
     The method continues at step  608  where the processing module facilitates execution of an assessment portion of the lesson. For example, the processing module selects an assessment approach, facilitates output of the assessment utilizing assessment information, and interprets learner interaction information to produce an assessment. The processing module further portrays the learner interaction information as updated session control adaptation information to update the representations of the assessment portion as modified learner output information. 
     The method described above in conjunction with the processing module can alternatively be performed by other modules of the computing system  10  of  FIG.  1    or by other devices. In addition, at least one memory section (e.g., a computer readable memory, a non-transitory computer readable storage medium, a non-transitory computer readable memory organized into a first memory element, a second memory element, a third memory element, a fourth element section, a fifth memory element, a sixth memory element, etc.) that stores operational instructions can, when executed by one or more processing modules of the one or more computing devices of the computing system  10 , cause the one or more computing devices to perform any or all of the method steps described above. 
     It is noted that terminologies as may be used herein such as bit stream, stream, signal sequence, etc. (or their equivalents) have been used interchangeably to describe digital information whose content corresponds to any of a number of desired types (e.g., data, video, speech, audio, etc. any of which may generally be referred to as ‘data’). 
     As may be used herein, the terms “substantially” and “approximately” provides an industry-accepted tolerance for its corresponding term and/or relativity between items. Such an industry-accepted tolerance ranges from less than one percent to fifty percent and corresponds to, but is not limited to, component values, integrated circuit process variations, temperature variations, rise and fall times, and/or thermal noise. Such relativity between items ranges from a difference of a few percent to magnitude differences. As may also be used herein, the term(s) “configured to”, “operably coupled to”, “coupled to”, and/or “coupling” includes direct coupling between items and/or indirect coupling between items via an intervening item (e.g., an item includes, but is not limited to, a component, an element, a circuit, and/or a module) where, for an example of indirect coupling, the intervening item does not modify the information of a signal but may adjust its current level, voltage level, and/or power level. As may further be used herein, inferred coupling (i.e., where one element is coupled to another element by inference) includes direct and indirect coupling between two items in the same manner as “coupled to”. As may even further be used herein, the term “configured to”, “operable to”, “coupled to”, or “operably coupled to” indicates that an item includes one or more of power connections, input(s), output(s), etc., to perform, when activated, one or more its corresponding functions and may further include inferred coupling to one or more other items. As may still further be used herein, the term “associated with”, includes direct and/or indirect coupling of separate items and/or one item being embedded within another item. 
     As may be used herein, the term “compares favorably”, indicates that a comparison between two or more items, signals, etc., provides a desired relationship. For example, when the desired relationship is that signal  1  has a greater magnitude than signal  2 , a favorable comparison may be achieved when the magnitude of signal  1  is greater than that of signal  2  or when the magnitude of signal  2  is less than that of signal  1 . As may be used herein, the term “compares unfavorably”, indicates that a comparison between two or more items, signals, etc., fails to provide the desired relationship. 
     As may be used herein, one or more claims may include, in a specific form of this generic form, the phrase “at least one of a, b, and c” or of this generic form “at least one of a, b, or c”, with more or less elements than “a”, “b”, and “c”. In either phrasing, the phrases are to be interpreted identically. In particular, “at least one of a, b, and c” is equivalent to “at least one of a, b, or c” and shall mean a, b, and/or c. As an example, it means: “a” only, “b” only, “c” only, “a” and “b”, “a” and “c”, “b” and “c”, and/or “a”, “b”, and “c”. 
     As may also be used herein, the terms “processing module”, “processing circuit”, “processor”, and/or “processing unit” may be a single processing device or a plurality of processing devices. Such a processing device may be a microprocessor, micro-controller, digital signal processor, microcomputer, central processing unit, field programmable gate array, programmable logic device, state machine, logic circuitry, analog circuitry, digital circuitry, and/or any device that manipulates signals (analog and/or digital) based on hard coding of the circuitry and/or operational instructions. The processing module, module, processing circuit, and/or processing unit may be, or further include, memory and/or an integrated memory element, which may be a single memory device, a plurality of memory devices, and/or embedded circuitry of another processing module, module, processing circuit, and/or processing unit. Such a memory device may be a read-only memory, random access memory, volatile memory, non-volatile memory, static memory, dynamic memory, flash memory, cache memory, and/or any device that stores digital information. Note that if the processing module, module, processing circuit, and/or processing unit includes more than one processing device, the processing devices may be centrally located (e.g., directly coupled together via a wired and/or wireless bus structure) or may be distributedly located (e.g., cloud computing via indirect coupling via a local area network and/or a wide area network). Further note that if the processing module, module, processing circuit, and/or processing unit implements one or more of its functions via a state machine, analog circuitry, digital circuitry, and/or logic circuitry, the memory and/or memory element storing the corresponding operational instructions may be embedded within, or external to, the circuitry comprising the state machine, analog circuitry, digital circuitry, and/or logic circuitry. Still further note that, the memory element may store, and the processing module, module, processing circuit, and/or processing unit executes, hard coded and/or operational instructions corresponding to at least some of the steps and/or functions illustrated in one or more of the Figures. Such a memory device or memory element can be included in an article of manufacture. 
     One or more embodiments have been described above with the aid of method steps illustrating the performance of specified functions and relationships thereof. The boundaries and sequence of these functional building blocks and method steps have been arbitrarily defined herein for convenience of description. Alternate boundaries and sequences can be defined so long as the specified functions and relationships are appropriately performed. Any such alternate boundaries or sequences are thus within the scope and spirit of the claims. Further, the boundaries of these functional building blocks have been arbitrarily defined for convenience of description. Alternate boundaries could be defined as long as the certain significant functions are appropriately performed. Similarly, flow diagram blocks may also have been arbitrarily defined herein to illustrate certain significant functionality. 
     To the extent used, the flow diagram block boundaries and sequence could have been defined otherwise and still perform the certain significant functionality. Such alternate definitions of both functional building blocks and flow diagram blocks and sequences are thus within the scope and spirit of the claims. One of average skill in the art will also recognize that the functional building blocks, and other illustrative blocks, modules and components herein, can be implemented as illustrated or by discrete components, application specific integrated circuits, processors executing appropriate software and the like or any combination thereof. 
     In addition, a flow diagram may include a “start” and/or “continue” indication. The “start” and “continue” indications reflect that the steps presented can optionally be incorporated in or otherwise used in conjunction with other routines. In this context, “start” indicates the beginning of the first step presented and may be preceded by other activities not specifically shown. Further, the “continue” indication reflects that the steps presented may be performed multiple times and/or may be succeeded by other activities not specifically shown. Further, while a flow diagram indicates a particular ordering of steps, other orderings are likewise possible provided that the principles of causality are maintained. 
     The one or more embodiments are used herein to illustrate one or more aspects, one or more features, one or more concepts, and/or one or more examples. A physical embodiment of an apparatus, an article of manufacture, a machine, and/or of a process may include one or more of the aspects, features, concepts, examples, etc. described with reference to one or more of the embodiments discussed herein. Further, from figure to figure, the embodiments may incorporate the same or similarly named functions, steps, modules, etc. that may use the same or different reference numbers and, as such, the functions, steps, modules, etc. may be the same or similar functions, steps, modules, etc. or different ones. 
     Unless specifically stated to the contra, signals to, from, and/or between elements in a figure of any of the figures presented herein may be analog or digital, continuous time or discrete time, and single-ended or differential. For instance, if a signal path is shown as a single-ended path, it also represents a differential signal path. Similarly, if a signal path is shown as a differential path, it also represents a single-ended signal path. While one or more particular architectures are described herein, other architectures can likewise be implemented that use one or more data buses not expressly shown, direct connectivity between elements, and/or indirect coupling between other elements as recognized by one of average skill in the art. 
     The term “module” is used in the description of one or more of the embodiments. A module implements one or more functions via a device such as a processor or other processing device or other hardware that may include or operate in association with a memory that stores operational instructions. A module may operate independently and/or in conjunction with software and/or firmware. As also used herein, a module may contain one or more sub-modules, each of which may be one or more modules. 
     As may further be used herein, a computer readable memory includes one or more memory elements. A memory element may be a separate memory device, multiple memory devices, or a set of memory locations within a memory device. Such a memory device may be a read-only memory, random access memory, volatile memory, non-volatile memory, static memory, dynamic memory, flash memory, cache memory, and/or any device that stores digital information. The memory device may be in a form a solid-state memory, a hard drive memory, cloud memory, thumb drive, server memory, computing device memory, and/or other physical medium for storing digital information. 
     While particular combinations of various functions and features of the one or more embodiments have been expressly described herein, other combinations of these features and functions are likewise possible. The present disclosure is not limited by the particular examples disclosed herein and expressly incorporates these other combinations.