Patent Publication Number: US-10311742-B2

Title: Adaptive training system, method, and apparatus

Description:
PRIOR APPLICATIONS 
     This application claims the benefit of U.S. provisional application Ser. No. 61/530,348 filed Sep. 1, 2011, which is herein incorporated by reference in its entirety. 
    
    
     FIELD OF THE INVENTION 
     This invention relates to computerized training systems, and more particularly computerized training systems where the computer administers the training. The preferable environment is a computerized system with associated devices that immerse students in emotionally engaging and functional operational environments throughout the learning experience, such as those relying on simulation for the training, e.g., for flight simulation or other vehicle simulator. 
     BACKGROUND OF THE INVENTION 
     Computerized training systems of many types exist. In the area of training in vehicle operation, these frequently employ a simulator station that emulates the actual vehicle, often accomplished using a dummy vehicle control panel with a simulated out-the-window scene visible to the trainee. The training takes place in a virtual environment created by a pre-programmed computer system. 
     Simulator systems are generally expensive and it is very desirable to make maximum use of each piece of hardware, to reduce the overall costs of the equipment for the training results conferred. 
     Known training systems provide the trainee with classroom lessons and computer based training (CBT) delivered by computer or by a human instructor, followed by an after-action review that is given to the trainee from which the effectiveness of the training on the trainee can be determined. If the assessment is not positive for the trainee having been effectively trained by the course of instruction, the computer system either repeats the instruction process for the trainee, or initiates a remedial process to bring the trainee up to an effective level. This rigid sequential process is repeated for all trainees who follow the identical sequence of instruction until the assessment indicates adequate effectiveness of the training. 
     This process can result in wasteful or inefficient and costly use of the training resources, e.g., the simulator, because the varying skill levels of the trainees, and varying effectiveness of the course of instruction on each trainee. The most advanced student or trainee may be exposed to steps of training for less difficult aspects of the training, making that trainee bored, and also wasting the training time by trying to teach things that the trainee already knows. On the other hand, a less expert, moderately-skilled individual may be given additional instruction that is not necessary while at the same time being given less instruction in certain areas where he requires additional instruction and training, resulting in more repeat work. Finally, there is the very low-skilled trainee that needs to learn virtually everything, and has difficulties with addressing some of the more difficult aspects of the training, possibly missing basics, and therefore being unable to benefit from the remainder of the more advanced segment of the instruction set. 
     Similarly, different courses of training may have differing effectiveness depending on the nature of the trainees. As a result, training methods that are not effective for a given trainee may be administered, and their lack of effectiveness can only be determined after the training system has been occupied for a full instruction session. 
     For the foregoing reasons, current learning systems are not making efficient use of the available hardware and computer support systems and personnel. 
     SUMMARY OF THE INVENTION 
     It is accordingly an object of the present invention to provide a computerized learning system, especially a computerized simulation system, in which a trainee is efficiently provided with instructions that are appropriate to his skill level and his personal leaning parameters as they are determined by the assessment of the ongoing instruction or by prior identified leaning preferences of the trainee. Preferably, the system supports self-paced learner-driven discovery while continuously targeting the learner&#39;s KSA (Knowledge, Skill, Ability) gap. The system may rely on full simulation, which may be real (i.e., using a real device in the training for its use), simulated (as with touch screen I/O devices that emulate the device being trained for) or based on a model (or dummy copy) of the device or devices, the use of which is being trained. 
     According to an aspect of the invention, a system for training a student comprises a simulation station configured to interact with the student and a computer system. The simulation system displays output to the student via at least one output device and receives input via at least one input device. The computer system has a rules engine operative on it and computer accessible data storage operatively associated with it and storing (i) learning object data including a plurality of learning objects each configured to provide interaction with the student at the simulation system, and (ii) rule data defining a plurality of rules accessed by the rules engine. The rules data includes, for each rule, respective (a) if-portion data defining a condition of data and (b) then-portion data defining an action to be performed at the simulation station. For at least some of the rules, the respective action comprises output of a respective one of the learning objects so as to interact with the student. The rules engine causes the computer system to perform the action when the condition of data is present in the data storage. 
     According to another aspect of the invention, a method for providing computerized training to a student comprises providing a simulation station connected with a computer system with computer-accessible data storage supporting a rules engine thereon. Lesson data is stored in the data storage so as to be accessed by the rules engine. This lesson data comprises
         learning object data defining a number of learning objects that each, when activated by the rules engine, cause the simulation station to output visual imagery, audio or other output and   rules data defining a plurality of rules on which the rules engine operates so as to administer the computerized training.
 
The rules each have a data condition part and an action part. The data condition part defines a state of data in the data storage that, when present, causes the rules engine to direct the computerized system to take a predetermined action. At least some of the actions comprise activating at least some of the learning objects to interact with the student at the simulation station.
       

     Student state data is also stored in the data storage. The student state data includes data defining an assessment measure of training of the student. 
     The computerized training is provided to the student at the simulation station with the rules engine administering the training according to the rules stored in the data storage. The assessment measure for the student is determined repeatedly or continually based on input received from the student at the simulation station, and the determined assessment measure is stored in the student state data. The rules data defines at least one rule that initiates the action thereof when a data condition that the student state data in the data storage defines an assessment measure below a predetermined value is present, and the action includes initiating operation on the simulation station of one of the stored learning objects. 
     According to another aspect of the invention, objects of the invention are accomplished using a computerized training interface system having input and output capabilities, and a computerized system connected with it that preferably operates using an inference engine or a rules engine. The rules engine is programmed with a set of rules as will be described herein that allow it or enable it to administer flexibly the training of a trainee in an immersive training station. 
     An Intelligent Decision Making Engine (IDME) is a data-driven computer system, preferably a rule based inference engine implemented using a CLIPS software package, which is available as open-source public domain software, that implements actions or procedures responsive to specified qualities of data being stored. The rules are continuously active once loaded, and are configured to allow for continuous adaptive modification of any instruction and other interactions with the trainee of the training station in real time, an interactive adaptive learning system, as will be described herein. The CLIPS software and its operation are described inter alia in the Third Conference on CLIPS Proceedings (Electronic Version) available publicly at http://clipsrules.sourceforge.net/documentation/other/3CCP/pdf, NASA Conference pub. 10162 Vol. 1 (1994), which is herein incorporated by reference in its entirety. 
     Because the use of a rules engine makes the reaction to changes in the data immediate, the adaptive process of the invention is especially efficient at delivering training. It may be said that the rules engine system provides for a higher-resolution or finer-grain adaptive learning than is available in the prior art due to the immediacy of the reaction of the rules-based system. 
     The organization of rules is prepared by the training staff and generally provides for at least one of
         (1) remedial instruction action when there is an indication of failure or ineffectiveness of the training,   (2) increased training difficulty when the assessment indicates that the trainee has too high a level of ability for the immediate level or subject matter of the training, and   (3) an adjustment of type of training to better address the training requirements of the individual trainee.       

     These assessments and changes are executed continuously as the instruction progresses, and as soon as any indication of inefficiency of use of the resources is present in the data base of the rules engine. The continuous performance assessment targets the individual learner lesson adaption to the state of the learner. The complexity and pace of the lesson are adapted to regulate learner engagement and maximize learning and retention. 
     Other advantages and objects will become obvious from the present specification. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a schematic view of the overall simulation system according to the invention. 
         FIG. 2  is a schematic view of the internal operation of a training system according to the invention. 
         FIG. 3  is a more detailed schematic view of the operation of the computerized simulation system of the invention. 
         FIG. 4  shows an example of an immersive platform station for use as the training station for the invention, together with a schematic illustration of the peripheral devices attached thereto and the associated software support from the computer controlling system. 
         FIG. 5  is a perspective view of an exemplary simulation system using the present invention. 
         FIG. 6  is an exemplary display showing an avatar, and some training field of view and equipment presented to a trainee as an example. 
         FIG. 7  is an illustrated diagram of the operation of a rules engine to administer a training program for the HUD (Head-Up Display) and CCU (Cockpit Control Unit) of a vehicle. 
         FIG. 8  shows a diagram of a timeline of training of an ideal student using the training system of the present invention. 
         FIG. 9  is a timeline diagram of a student requiring corrective or remedial actions being trained in this same material as in  FIG. 8 . 
         FIG. 10  is an illustration of the development of the learning object database used for the present training method. 
         FIG. 11  is a diagram of a data model by which the data is stored in a computer accessible memory device. 
         FIG. 12  is a diagram illustrating the relative efficiencies of training for a number of different students at different skill levels. 
         FIG. 13  is a diagram of an example showing lesson flow for an exemplary rules implementation. 
         FIG. 14  is a diagram illustrating trainee insertion in the adaptive learning system of the present invention. 
         FIG. 15  is a diagram illustrating trainee re-evaluation in the adaptive learning system of the present invention. 
         FIG. 16  shows a story board style illustration of the training process for a UH60 attack helicopter simulation, illustrating various rules-implemented processes possible according to the present invention. 
         FIG. 17  is a diagram illustrating the structure of a multi-processor embodiment of the invention. 
     
    
    
     DETAILED DISCLOSURE 
       FIG. 1  shows a diagram of an embodiment of the system architecture of the computer system controlling the operation of a LinkPod™ training system, which may be used for a variety of training purposes, especially for training in operation of vehicles, such as a flight simulator. 
     The system is implemented in a computer system, which may comprise one computer or a plurality of computers linked by a network or local connection over which system operation is distributed. The computer system or systems may run operating systems such as LINUX or Windows, and their operations are controlled by software in the form of computer-executable instructions stored in computer-accessible data memory or data storage devices, e.g., disk drives. The computers also include typical computer hardware, i.e., a central processor, co-processor or multi-processor, memory connected to the processor(s), and connected devices for input and output, including data storage devices that can be accessed by the associated computer to obtain stored data thereon, as well as the usual human operator interfaces, i.e., a user-viewable display monitor, a keyboard, a mouse, etc. 
     The databases described herein are stored in the computer-accessible data storage devices or memory on which data may be stored, or from which data may be retrieved. The databases described herein may all be combined in a single database stored on a single device accessible by all modules of the system, or the database may be in several parts and stored in a distributed manner as discrete databases stored separate from one another, where each separate database is accessible to those components or modules of the system that require access to operate according to the method or system described herein. 
     Referring to  FIG. 2 , the overall LinkPod™ system comprises an immersive station  3 , which is an adaptable training station with a number of input and/or output devices accessible by user. Referring to  FIG. 5 , the immersive station  3  in the preferred embodiment comprises a seat  4  for a user and displays, including a larger 3D HDTV resolution display  6  and two or more touch sensitive I/O screens  8  supported for adjusting movement. The touch screens can be used to display a cockpit of any vehicle or the specific device the training is for, so the station  3  can be used for a variety of possible training courses for a variety of different vehicles or aircraft. The immersive station  3  also has an eye tracker that detects the direction that the trainee is looking in and generates a data signal carrying that information. All the displays  6  and  8  are connected with and controlled by a local computer system that supports the immersive station  3  as a platform. The base of the station  3  is a frame supported on casters, which allow for easy movement of the station  3  as desired. 
     As illustrated in  FIG. 4 , the immersive platform station computer system  10  runs an immersive platform manager software module  14 , which operates a selected configuration of the trainee station  3 . The platform support includes support of the main display  6 , the interactive displays  8 , the eye tracker or gaze detector, a haptic system, a brain sensor system, which can detect certain neurological parameters of the trainee relevant to the training, sensors that can detect the trainee&#39;s posture, and also a 3D sound system, and a microphone, and any other hardware that is desired for trainee station. The various components of the system return electrical signal data that is processed by platform manager  10  and transmitted to other modules of the system. 
     It will be understood that a plurality of immersive stations  3  can be supported in parallel by a system according to the invention. 
     The immersive station  3  is electronically connected by a network data link or local connection with a computerized learning management system (LMS)  5 . Generally, the LMS  5  is supported on a separate computer system via a network, and it may be connected to a number of training stations  3  locally or remote from its location. The LMS stores all the data, including videos and other information and media files used in the lessons, as well as data defining the students that use the system and data relating to administration of training with the various training stations  3  connected therewith via one or more networks or the Internet. The LMS is similar to training management systems known to those of skill in the art, in that it communicates with the immersive station  3  so as to display a prompt and it receives student log-in identification data, typically comprising an ID and a password, from the immersive station  3  entered by the trainee through an interactive screen  8  at the immersive station  3 . The LMS then lists the possible courses of instruction available to the trainee, and receives a responsive communication through the interactive device  8  that selects a course. The LMS then loads the respective training station  3  with the necessary training data resources, media, software that supports hardware needed for the specific training selected, and other data as will be described herein, and also and initiates the system of the training station to present the course to the trainee. 
     Referring to  FIG. 2 , LMS  5  is connected with and accesses stored curriculum and records database  6 . This database contains the data needed to administer training in the system, including history of the student or students. Selection of a course of training responsive to trainee log-in and other selection input, causes the LMS to load the requisite lessons, rules, and other data into the appropriate data storage or memory so as to be accessible by the components of the system that are involved in delivery of training at the immersive station  3 . 
     The system further includes an intelligent decision making engine (IDME) indicated at  7 . Learning management system  5  communicates internally with IDME  7 , which in the preferred embodiment is a rules-based inference engine supported on a computer system in the training station  3 . The IDME rules run via an API of CLIPS rules-engine software running on the host computer. The IDME  7  has computer accessible memory that is loaded by the LMS  5  with the rules needed for the specific selected training operation. Preferably, the IDME has access to a database shared with other components of the system that contains training data, as will be described herein. 
     The IDME rules engine operates according to a set of rules that are loaded into the associated storage or memory so as to be accessible by the IDME. Each rule specifies a condition of data in the associated database, if the data value of a current measure of effectiveness for the current trainee is below a predetermined threshold value, etc. The rule also specifies an action that is to be taken whenever that condition of data is satisfied, such as, e.g., to display a question to the trainee and wait for a response. The rules engine is a data-driven system, in that the state of the data in the associated database immediately triggers prescribed actions when it satisfies the condition of the rule. As such, the set of rules loaded in the IDME all operate continuously and simultaneously based on the state of data accessible to the IDME, and the rules trigger actions that will be taken in the training process at the immersive station  3  at whatever point in time the associated data condition of the rule is met. 
     When the rules dictate, the IDME  7  passes, sends or otherwise transfers data to a content adaption module  9  that corresponds to actions, i.e., commands to perform integrated lesson actions. 
     Content adaption module  9  is also implemented using a software system running on a computer, and the IDME and the content adaption module  9  may be both supported on the same computer. Content adaption module  9  also has access to a data storage device  11  storing data containing training content, e.g., materials, recorded instruction and various other software and data that is used in providing simulation or training to the user at station  3 , and it controls the operation of the instruction and/or simulation conducted at immersive station  3 . In particular, the content adaption module  9  causes the immersive station displays and sound system to display avatars delivering audible content, voice instruction, and other actions. Those other actions include interfacing with an external simulation or live device running a computerized simulation of the vehicle of the training by displaying the correct controls on the interactive screens and with an appropriate out-the-window display on the main display  6  created by a computerized image generator, not shown, that renders real-time video based on virtual scene data, as is well known in the art of flight or other vehicle simulation. 
     Content adaption module  9  uses training content  11  to provide to immersive station  3  the necessary training events. As the training proceeds, the various trainee sensors and input devices generally indicated at  13 , e.g., eye-tracking, gaze or blink detection, neural detectors, touchscreens or other touch-based simulated control panel or cockpit input/output devices, a microphone listening for speech, and optionally detectors from which body position or posture may be detected, detect actions or conditions of the trainee and transmit data therefrom to continuous assessment module  15 . 
     The continuous assessment module  15  is also implemented using a software system running on a computer. Preferably, the IDME and the continuous assessment module  15  are both supported on the same computer located geographically at the simulation station  3 . The assessment module  15  may be independent of the IDME, or more preferably, the assessment module  15  may be constitute as set of Assessment Rules (see  FIG. 10 ) incorporated into the rules data as a subset of the total rules data on which the IDME operates. As rules data, the assessment activities may be seamlessly interwoven with the activation of learning objects transmitting output that triggers input of the trainee that may be used to assess a measure of performance (MOP) of the student, or a measure of effectiveness (MOE) of the training as it is given. 
     Continuous assessment module  15  provides continuous assessment of the trainee such as by analysis of responses or activities of the trainee at the immersive station  3 . The continuous assessment module  15  generally produces data that is an assessment of the knowledge, skill and ability (KSA) of the trainee. Knowledge is the retention by the trainee of certain information necessary to operate the vehicle, e.g. the location of the switch for the landing gear on an aircraft. Skill is the use of knowledge to take some action, e.g., to operate the landing gear properly in simulation. Ability is the application of knowledge and/or skill to operate properly in a more complex mission scenario, such as in a simulation using the knowledge and skill. 
     A variety of techniques may be employed to determine KSA values for the trainee. For instance, the assessment module  15  can assess the trainee based on frequency of errors and correct actions in a simulation exercise, with corresponding weighting from severe errors at −5 to perfect operation at +5. Assessment can also be based on the trainee&#39;s visual scan pattern using techniques such as Hidden Markov Model (HMM) to assess the trainee&#39;s skill level while executing tasks. Interactive quizzes or pop-up questions may also be employed, where the response is either a verbal response picked up by a microphone or selection of a multiple choice question response through some other input device such as a touchscreen. Some biometrics may be used as well. 
     The KSA assessments made by the continuous assessment module  15  are stored as data in a student state data area in a database accessible to both the continuous assessment module  9  and the IDME  7 . It will be understood that the student state data may be numerical values linked to identify the associated area of knowledge, skill or ability, and may be a flag of 1 or 0 indicative of the presence or absence in the student of the knowledge, skill or ability, or a numerical variable in a range that is indicative of the degree of presence of the KSA quality, e.g., a score from a test on a scale of 0 to 100, or may be a string of characters that is indicative of some level of KSA or expertise of the student, e.g., with respect to successful completion of some aspect of training, a “YES” or “NO”, or a detailed definition of a familiarity with an instructional area, any character string, e.g., “BEGINNER”, “EXPERT”, or “BASIC”, etc. 
     Also stored in the shared database area is platform state data that defines the current state of the platform, and is indicative of what training is being displayed or the status of the delivery of training to the trainee on the immersive station  3 . This data may also be numerical or character strings. 
     Generally, the rules of the IDME define conditions for action that are based on the student state data or the platform data. The rules cause the system to react to the data produced by the continuous assessment so that the immediate decision making of the system improves the efficacy and efficiency of the use of the simulation device or immersive station  3 . 
     Referring to  FIG. 3 , a more detailed illustration of the operation of the system is shown. As described above, immersive station  3  is occupied by a student that interacts with the immersive station  3 . Student actions at the immersive station  3  are processed by continuously-running assessment program  9 . The assessment program continuously or continually develops an assessment of the knowledge, skill and ability (KSA) of the student from the student actions, and also from the stored LMS model of the student, which has already been obtained or supplied to the system or developed over time to derive, and defines certain training attributes of the trainee, such as whether the trainee is better trained by visual or auditory instruction. 
     From all of these inputs or student actions, the continuous assessment determines the student KSA  17 . The student KSA is compared to a desired or required level of KSA appropriate to the level of instruction or simulation that the student is receiving. The difference between the desired KSA value and the actual student KSA may be referred to as a KSA gap  19 , this being either a quantified value or a value that can be derived from the determined student KSA and compared with the specific expectations of the student as pre-determined by data in the system. 
     The student KSA is part of the student state data that is available to the IDME  7 , and as such the rules are preferably written so as to take instructional actions targeting the current KSA gap of the trainee. As has been stated above, the IDME rules operate continuously, and they take instructional actions immediately based on the data in reaction to the KSA gap or KSA values, providing optimal training directed at the areas where the trainee requires instruction. 
     The instructional actions are sent from the IDME  7  to the learning content adaptation module  5 . The learning content adaptation module  5  accesses training content data stored on a computer accessible data storage device  21  and this material is transmitted to the immersive station  3 , adjusting the training of the trainee. 
     A rule is composed of an if portion and a then portion. The if portion of a rule is a series of patterns which specify the data that cause the rule to be applicable. Commonly, as is known in the art, the pattern that is satisfied is a Boolean or mathematical condition, e.g., if x=0, or if x=1, y=1 and z&lt;50, or student_level=EXPERT, that is either present in the data or absent. The then portion of a rule is the set of actions to be executed when the rule is applicable, i.e., when the if portion of the rule is present in the database. 
     The inference engine or IDME  7  automatically matches data against predetermined patterns and determines which rules are applicable. The if portion of a rule is actually a whenever portion of a rule, because pattern matching occurs whenever changes are made to the data associated with the IDME. The inference engine selects a rule, and if the data conditions of the if portion are present in the data, then the actions of the then portion of the selected rule are executed. The inference engine then selects another rule and executes its actions. This process continues until no applicable rules remain. 
     The if portion, or the contingent data precondition portion, of each of the rules may be any aspect of the data student state or the platform state. The then portion of the rule may include action to be taken in response to the satisfaction of the conditional requirement for the student or platform data may be any action that can be done by the immersive station  3 . 
     For example, the IDME may be programmed with a rule that if the student KSA determined during a simulated aircraft training exercise indicates a poor understanding (either by a flag or a scale of effectiveness that is below a predetermined threshold) of an aspect of the operation of an instrument panel, e.g., an altimeter, then a special avatar is to be displayed and a an instructional statement made via the sound system of the immersive system  3 . In case the current KSA data corresponds to such a flag or falls below the threshold, indicating a shortfall of the trainee&#39;s KSA, the instruction is transmitted to the learning content adaption  5  directing display of the avatar and playing of the audio. The required video and audio is located in the training content database  21 , and the LCA  5  transmits it to the immersive station platform, where it is displayed or played to the trainee.  FIG. 6  shows a main display screen view, wherein a human-appearing avatar is giving audio instruction regarding an aspect of flight training. 
     The avatar may be displayed as part of the rendered imagery shown to the trainee, e.g., as a person standing in the environment displayed and speaking to the trainee. Moreover, the rules-based system can make the avatar interactive with the trainee, responding to the trainee&#39;s reactions to the avatar&#39;s statements or commands. 
     For another example, the IDME may have a rule that if the eye tracker data indicates that the trainee has not blinked for thirty seconds, then the LCA is to schedule a break or discontinue the process and request assistance from the human trainer. 
     The then portion or action specified by the rules to a KSA deficiency relative to an acceptable KSA level may be as simple as repeating a previous course of instruction when a trainee shows a lack of proficiency in one particular area. On the other hand, the action may involve an immediate modification of the training presently being given to the trainee so as to enhance certain aspects of the training so as to offset a shortfall in training that is detected. 
     Another possible rule is one wherein the if portion of the rule is that the data indicates that the trainee is doing extremely well, has very high performance assessment and a low or zero KSA gap, possibly coupled with a biometric data having an indication of physiological effects of low stress or disinterest, such as blinking longer than usual, then additional complexity or difficulty is introduced into the ongoing training. 
     The internal software-based computer architecture of an embodiment of the system is illustrated in the diagram of  FIG. 1 . The host computer system generally indicated at  23  supports the operation of the training station  3 , and preferably is connected via a network, e.g., the Internet, with the computer system that supports the LMS  5 , allowing for the individual trainee to sign in, be recognized by the system, and to have his personal data, if on file, restored to the local system(s) of the training station  3  to assist in his training. 
     The host interface  25  also provides interface of the training station  3  to external simulation state data, and allows training station  3  interactions to be applied to an external simulation, i.e., a simulation program running on a connected computer system. For example, when a student turns on power to a virtual HUD by touching one of the touch screens of training station  3 , this action generates an input signal that is communicated to the connected simulation. Responsive to the input, the simulation changes the switch position in the HUD, and the data defining the switch state in the simulation data base, and the power lamp changes color. The new state is communicated through host interface  25  to the virtual learning object (VLO), meaning the display in the training station  3 , e.g., one of the touch displays, that is configured by the lesson data to look like a HUD control. The VLO changes the displayed appearance of the virtual device, e.g., the HUD, to match the host state data for the simulation of the device. 
     One or more processors in the training station administer the operation of the training platform, which is initiated with all programs and data needed for the selected course of instruction. The platform state data  33  is initialized and made available to the IDME  7 , which accesses both the platform state data and the student state model data. The platform state  29  indicates the state of operation of the simulator attached to the system, and the student state model  35  reflects just data that has been stored based on the student&#39;s conduct and prior history as a trainee. Together these two groups of data are treated as “facts”, the data to which the rules of the CLIPS inference engine  31  are applied. 
     The output of the IDME  7  (if any is indicated by the rules) is actions  39  that are transmitted to the LCA, the learning content adaptation service. These actions  39  are usually data that is transmitted to the learning content adaptation system  9 , which in turn accesses the lesson database  41  accessible to the LinkPod™ core computer so that it can automatically obtain data stored therein. The LCA  9  transmits to the immersive platform service tasks that are to be executed by the simulator platform system, including avatar tasks, and other platform tasks for display or interaction with the trainee. This includes directing rendering of 3D imagery by an image generator computer system based on a database of virtual environment data, including models of vehicles and other objects, textures, and other aspects of display or presentation such as fonts and VOF. Data is returned from the simulation platform in a raw form, and that data is then processed to be converted into student state data or platform state data and stored in the relevant data areas for access by the IDME  7 . 
       FIG. 11  shows a diagram of the data model according to which data for the learning management system is preferably stored and utilized within the system of the invention. All of the elements and objects shown herein constitute data stored electronically on data storage devices that are accessible by a computer. The data model illustrates the organization of the stored data in the database, and is reflected in the database by stored database organizational data, e.g., pointers pointing to the location of data corresponding to records, which is used by software accessing the database to retrieve or store data therein on the data storage device or devices containing the database, as is well known in the art. 
     The LMS  5  identifies each course of instruction as a lesson record. The lesson record contains pointers or lists that include
         a set of objectives of the lesson,   a set of learning objects of the lesson;   a set of virtual objects of the lesson; a set of mappings for the lesson;   a set of resources for the lesson;   an identification of a simulation environment for the lesson; and   the lesson rules to be loaded into the IDME for the lesson.       

     The objectives are each stored as a record  53  with a list of steps to be performed by the trainee in the process of the lesson. These are each a discrete action, such as “identify landing gear control”, and they can be satisfied by a test question given to the trainee. In addition to the identification of the steps, there are a set of measurements of effectiveness of completion of the steps by the trainee, either a flag set to 1 (completed) or 0 (not completed), or a range of effectiveness of the step completion. 
     The learning objects are each stored as a record  55  that defines a series of actions to be taken, i.e., displays of imagery or avatars or administration of tests, generally all outputs to the trainee through the immersive system. 
     The virtual objects are records  57  that define virtual controls, such as cockpit controls that are displayed in interactive viewing displays  8  so as to appear similar to the controls of the real vehicle that is being simulated. 
     The resources are identified as a data record  59  that lists the hardware of the immersive station that is to be used in the lesson, e.g., whether the microphone and voice recognition is to be employed, whether the eye tracking system is to be employed, etc. 
     The simulation environment record  61  identifies a specific database of scene data defining a virtual world that is used for the given lesson. There may be a large number of virtual environments defined in the system, such as mountains, desert, oceans, each of which may be selected by a lesson for use as the training mission environment. 
     The rules record  63  contains the set of rules for the lesson  51 , written in CLIPS language. These rules are loaded into the IDME when the lesson is started. Individual learning object records may also reference rules records  55  as well, which are loaded when the learning object is loaded, and deleted from the IDME when the learning object is completed. 
       FIG. 7  illustrates a simple rule based process of training in which a lesson involving training in learning objects  71  having to do with operation of the CCU of an aircraft and learning objects having to do with HUD operation of the aircraft are combined. 
     Learning objects for the training are selected, step  75 , based on student state data at startup, i.e., the level of training or skill of the student according to the LMS records. The general rules are loaded, and the set of learning objects are loaded. The rules control the presentation of the learning objects to the student so that a student will not be given a more advanced lesson until the student has completed the necessary prerequisites. The order of completing those prerequisites may vary from student to student, but the rule will not permit the display of the advanced learning object until the student state data indicates that the prerequisite learning objects have been completed. 
     As seen in  FIG. 7 , an agenda of learning objects is selected for the student, and the rules cause them to be presented to the student (step  77 ), and once the material has been presented to the student, the student state model data is updated to reflect the fact (step  78 ). Based on the initial run and an assessment of the student knowledge level, a rule  79  is applied to the extant student state data: “IF (1) student has proven knowledge of X, and (2) student has proven knowledge of Y, and (3) student has not yet been presented module Z (another learning object), THEN present module Z” as reflected by values stored in the student state data. This rule is active, but its IF-part is not satisfied until the student state data indicates that the student has knowledge of X and Y. When the student state data indicates that the student has knowledge of X and knowledge of Y, then at that point in time, the rule causes Z to be presented. Once presented, the student model or student state data is updated to reflect that Z has been presented, as by, e.g., setting data as a flag corresponding to completion of the Z module. After this, the student model or data indicates that Z has been presented, and the IF-part of the rule, which includes the determination “(3) student has not yet been presented module Z” is not satisfied, and the rule does not cause any action from then on. 
       FIG. 8  shows a timeline flow for a lesson as applied to a student that is an ideal student, meaning that the student completes the objectives of each learning object without creating conditions in the student state data that cause the IDME rules to trigger remedial actions. 
     At the beginning  101  of the timeline, the lesson is loaded, and this includes loading of the lesson rules. The lesson starts, and the first rule to activate is the Intro Rules  102 , which trigger the action of Intro Content Playback  103 . When the intro is completed, this rule is not satisfied by the data because a flag or content complete for the intro learning object (“LO”) is set at  105 . The HUD LO Description Rules  108  then are satisfied and become active, the action being to load the HUD content and play the HUD playback  109 . When that is completed, the HUD rules direct an adjustment task for the student to perform at  111 . This task is successfully completed and the HUD rules then direct playback of a “good job” message ( 113 ) to the student. When all of these actions are completed, flags so indicating are set in the student model data, and the HUD description rules are no longer satisfied and become inactive. At that point  115 , sample flight LO rules become active, and the rules are satisfied and run through to successful completion at  117 . 
       FIG. 9  shows a different outcome based on the same rules, all of which are loaded at point  201 . The rules include eye-tracker data based rules that react to data indicative of the student not watching display, and of microphone pickup of chatter indicative of distraction. 
     The Intro LO is loaded, and the intro content playback proceeds. In addition to the intro rule, the distraction detection rule is running as well. When the student data indicates that the student is not watching the display ( 203 ) and there is chatter from the microphone ( 205 ), the distraction rule triggers a break-offer action  207 . The break is conducted according to Break Rules  209 , which involve playback  211  offering a break, listening ( 213 ) for an acceptance, and then resuming on return of the student ( 215 ). The intro completion flag is then set at point  216 . 
     The HUD LO then starts according to the HUD description rules  217 . There is the HUD content playback  219 , followed by a test of HUD brightness adjustment  221 . The student here does not properly change the HUD brightness ( 223 ), and the rules cause playback of the system itself doing the brightness adjustment ( 225 ). A negative effectiveness data value is then stored in the student state data ( 227 ). 
     The HUD rules actions are completed at  229 , and the HUD rules become inactive. The rules then load the Flight LO at point  231  with the Flight LO rules  233 . The flight content is then run, but there is an active rule that has its if portion satisfied—(1) the student has a negative HUD score, and (2) if the student data indicates distraction during the intro playback, THEN an action is directed that a HUD brightness training event insertion ( 235 ) is made in the flight LO content  237 . Once that is completed, the lesson continues as before. 
     The remedial action taken in this way using the rules engine avoids failure of the entire lesson effectiveness, because corrective action is taken during the lesson to correct for the distraction and knowledge deficiency detected in the student. The result is more efficient use of the simulation system. 
     Efficiency of the rules-based approach is also illustrated in the comparative timelines of  FIG. 12 . A proficient student timeline is seen at  301 . The proficient student completes four lessons, and his proficiency is detected by rules-based assessment. He then completes two missions  1  and  4  appropriate to his KSA level, completes a test flight and then graduates, freeing the system for the next trainee. 
     The timeline  303  for student  2 , of medium ability shows the same four lessons, with additional training content inserted throughout, resulting in a test fight and graduation in slightly longer time than required for the proficient student, but not equivalent to repetition of the course. 
     The timeline  305  for an expert student is greatly accelerated, because the training is intensified as the rules detect a high level of KSA, resulting in a mission and a test flight after only one lesson, and immediate graduation. This fees the system for an appreciable amount of time, and does not waste the trainee&#39;s time in unnecessary training either. 
       FIG. 13  also illustrates flow of a lesson. The trainee in this scenario gives the wrong answer at assessment point  401 . The student data is modified to have a flag indicative of the wrong answer. The question is re-asked at point  403 , and the right answer is given. A running rule tests this question again at point  405 , and when the wrong answer is given, new content  407  is inserted and displayed to the trainee. The right answer is then given at  409 . 
     This adaptive learning approach is described in  FIGS. 14 and 15 . The adaptive learning allows for both insertion and reevaluation. As listed in  FIG. 14 , various missions are run to evaluate the grasp of the content by the student. Failed content is inserted into the missions to augment memorization of the content by the student. Where questions are used to determine the retention of the information, the questions will be repeated to enhance memorization. The system preserves in the student state data the number of times the information has been presented to the student before the student answers the question correctly. 
     As described in  FIG. 15 , the failure to answer a question correctly can trigger a rule that an ad hoc evaluation of the content may be presented during a mission. 
     The rules engine architecture allows for this type of flexible training method. To obtain maximum efficiency, the rules must be developed and written in a way that identifies KSA parameters that are to be satisfied, and breaks the lessons up into workably discrete components that can be addressed independently to determine when the student has developed the requisite level of training KSA, and when he has not, to take remedial action immediately so as not to allow a partial deficiency to delay the entire training process. 
       FIG. 10  illustrates the process of creation of the rules for a lesson. An existing linear curriculum  81  is broken down by cognitive analysis (step  82 ) into instructional storyboards ( 83 ). The cognitive task analysis  82  and the instructional storyboards  83  are used to develop the expert knowledge rules, and also object modeling for the development of the learning object database for presenting the lesson to a trainee in a rules-based system. The rules of the learning object include lesson rules, which govern the content presented and its order of presentation. Assessment rules identify specific ways of determining KSA of the student, as well as other aspects of the student&#39;s state, such as distraction or boredom. The resulting rules are loaded into the IDME when the training is conducted. 
     A KSA storyboard example is shown in  FIG. 16 . The trainee logs in and starts the training via the LMS (step  501 ). The learning content manager (which includes the IDME, not shown) constantly assesses the student skill levels. The student is first given the knowledge of the lesson, in this case a HUD training exercise, by a virtual coach that performs the HUD usage and then directs the trainee through a declutter operation (stage  502 ). Once completed, skill is developed by reducing coaching in stage  503 . If too slow or too prone to errors, the trainee is sent back to stage  501  for more knowledge training (step  504 ). If not, the trainee moves to stage  505  for ability and retention training. In this stage  505 , a more complex mission using the knowledge and skill is presented to the trainee. If the trainee is not able to perform, the trainee is returned to stage  503  for further skill development. If the trainee is able to perform, further training on points of detected weakness can be given in stage  507 . 
     The operation of the training method of  FIG. 16  is based on rules that are continuously active. In particular, a rule is constantly in effect that is the determined level of skill falls below a predetermined threshold, the training action is then changed to a knowledge-type coach training as in stage  502 . Similarly, a rule responsive to an assessment of ability falling below a predetermined threshold causes the training action of changing to a skill level training. The changes of training to different stages are immediate due to the constant applicability of the rules via the IDME. The result is efficient development of knowledge, skill and ability for the trainee. 
       FIG. 17  illustrates the architecture of a preferred embodiment of a multiprocessor system supporting a LinkPod immersive training station  3 . The station  3  includes the set  131  of I/O devices that interact with the trainee. These include a 3D immersive main display  133 , cf. display  6  of  FIG. 5 , with associated 3D glasses  135  to be worn by the trainee. The I/O devices include also flight controls  137 , which may be a joystick or a more elaborate cockpit control system that can emulate real vehicle controls, and left and right touch screens  8  that allow trainee input to the system and display appropriate media or VLOs to the trainee. The I/O devices also include an eye tracker  139  of the sort well known in the art of simulation and military aircraft, a microphone  141  that receives audio input from the trainee, and an audio system  143  that generates sound as required by the training process. 
     A computer lesson processor #1 ( 145 ) with access to a local data storage device and also access to a network  147 , is connected directly with and transmits data and/or media to one touch display  8  and the audio system  143 . It is also connected with video switch  149 , which switches between video supplied from two possible sources, as will be described below. Lesson processor #1 supports execution of the software that supports the IDME and the LCA functions of the station  3 . It also administers a number of services, i.e., the touch screen service, a service that displays an avatar instructor for the trainee to view, spatial audio service that can output specific sounds via audio system  143  as part of the training, playback of video or audio when appropriate, support for a keyboard of the system, and resource management and training plan services that operate as described above with respect to the IDME/LCA operation, obtaining, locally or via network  147  from the LMS, and implementing the various media or data needed for the training selected. 
     The operation of lesson processor #1 is initiated by lesson host processor  151 , which is connected therewith by the network. Lesson host processor  151  supports the eye tracker  139 , but also administers the immersive platform and maintains the data of the platform state, which is accessible to the IDME of lesson processor #1 locally or via the network. This host processor  151  assists the trainee in initially logging in and accesses over the network  147  the LCS system, identifying the trainee and selecting the lessons that are to be implemented. The rules, media, and other data needed for the identified training are then transmitted from the LCS system over network  147  and loaded into a data storage device accessible by lesson processor #1. 
     Lesson processor #1 communicates via network  147  with lesson processor #2 ( 153 ), which receives from processor #1 data directing what it should display on the associated touch display  8 . Lesson processor #2 also receives data from speech recognition of input via microphone  141 , which is incorporated into the platform state data accessible to the IDME. 
     An additional processor, simulation host processor  155  provides for vehicle simulation, i.e., it determines using a computer model and scene data as well as data of the platform state or student state how the vehicle is moving or operating. Data including the trainee ownership location in a virtual environment and other simulation data is output over the network to synthetic environment processors  157 . 
     The synthetic environment processors  157  are essentially a multiprocessor image generator that renders an out-the-window view to be displayed to the trainee. This view includes distinct 3D imagery for the left and right eyes of the trainee, which is sent through a video combiner  159  and displayed in 3D to the trainee on immersive display  133 . 
     Lesson processor 1 accesses video switch  149  and selectively displays either the OTW imagery being rendered in real time by processors  157 , or it transmits recorded video that is transmitted from lesson processor #3 ( 161 ). Lesson processor #3 outputs recorded video the training session does not provide for trainee changes in the video portion displayed of, e.g., a flight taking place where the trainee is a passenger or supportive technician in the simulation, working on different aspects of the vehicle operation. Time-stamped recorded video or live video may also be supplied and displayed in this way as well via lesson processor #3. 
     The network  147  links all the processors so that the IDME can implement its actions through those processors, and the entire environment acts as a stand-alone training module. Additional training materials and data may be accessed at the LMS system via the network at all times. 
     In addition, the IDME shown is supported on lesson processor 1. It has access to virtually all the data of the training station  3 , including the data stored at the other processors, and rules implemented by the IDME may be based on the state of any of this data. Also, because the rules are in continuous effect, the IDME engine may be divided into distinct sets of rules each supported on a respective processor acting as a decision engine that has access to the platform and student data. 
     The training station may also be readily adapted to the training of two or more trainees at once. The rules of the IDME simply need to be configured to support this functionality. Separate assessments of KSA for each student based on the different inputs from e.g., different touch screens can also be made and rules-based actions taken in response to those KSA values. 
     The terms used herein should be viewed as terms of description rather than of limitation, as those who have skill in the art, with the specification before them, will be able to make modifications and variations thereto without departing from the spirit of the invention.