Abstract:
An adaptive training system for use with an adaptive testing tool includes a user interface receptive of a field of application selection from a user. A plurality of task items are stored in a data store. The plurality of task items are organized by field of application and ranked by difficulty level. An adaptive item selector selects unfamiliar task items as training items based on the selected field of application and a user ability. These training items range in order of difficulty from items slightly below a user&#39;s ability to items sufficiently far above that level to allow for learning effects. The user ability is a function of previous user performance on an adaptive test in the selected field of application. The main purpose of the adaptive item selector is to ensure that training items are neither too easy nor too difficult for a person.

Description:
FIELD OF THE INVENTION  
         [0001]    The present invention generally relates to adaptive testing systems and particularly relates to automated training systems for use with a computerized adaptive testing (CAT) tool.  
         BACKGROUND OF THE INVENTION  
         [0002]    The challenges of evaluating and training individuals has led to many developments over the years, and computer automated testing and training has become the norm in many fields. The field of academics has proven to be one fertile ground for development of computer automated testing and training, and training for standardized examinations with practice tests and questions is only one example application area. Professional training has proven to be another fertile ground for development of computer automated training, and such training proves particularly useful in professions that primarily require computer interfacing skills.  
           [0003]    Skills evaluation is related to training because assessing an individual&#39;s skills before training the individual assists a training tool in focusing on an individual&#39;s training needs. In fact, one past solution to the challenge of training individuals has involved testing an individual&#39;s ability to perform tasks with a wide range of difficulty level, and then training them on the tasks they failed to perform correctly. The inefficiencies inherent in this approach were recognized nearly half a century ago. However, it was not until the advent of high-speed computers in the nineteen-seventies that Frederic Lord and others could construct a computer adaptive testing (CAT) alternative to mass, full-item testing. For more on this point, refer to Howard Wainer,  Computerized Adaptive Testing: A Primer  (Hillsdale, N.J. 1990), pp. 8-11.  
           [0004]    With CAT, task items are ranked according to difficulty level and a computer selects task items for a user during an exam based on the user&#39;s performance during the exam. For example, if a user fails to perform a task of a certain difficulty level, then the computer selects a new task for the user with somewhat lower level of difficulty. Also, if the user succeeds in performing a task of a certain difficulty level, then the computer selects a new task for the user of a somewhat higher level of difficulty. This function is performed recursively until the computer determines a user&#39;s ability based on overall performance during the test, and the CAT system has advantages over previous skills assessment techniques.  
           [0005]    A distinct advantage of the CAT system is the ability to evaluate a user&#39;s skill level with a fewer number of tasks, thereby saving significant amounts of time and effort. For example, performance of fifteen task items is sufficient with a CAT system compared with a requirement for performance of seventy-five task items with previous evaluation systems. Thus, CAT systems benefit from the ability to minimize the number of items required to measure accurately a person&#39;s ability. This advantage, however, has a tradeoff when combined with previous training techniques, because training users only on missed items following a CAT exam fails to provide adequate training.  
           [0006]    Unsuitability of combining previous training procedures with a CAT system relates to the advantages of the CAT system. For example, this unsuitability relates to the ability of the CAT system to assess an individual&#39;s skill level with a fewer number of tasks. Unlike previous testing procedures, the CAT system generally results in a smaller number of missed items during an exam regardless of a user&#39;s skill level. Also, the unsuitability relates to the ability of the CAT system to rapidly converge on a person&#39;s ability level and thereby eliminate the need to test a user on tasks far below, or far above, their ability. As a result of the testing technique granting this ability, difficulty levels of missed items usually congregate at or near a particular level. Thus, training users only on missed items generally results in a small amount of training at or near a single level of difficulty.  
           [0007]    Given the development and availability of the CAT skills assessment technique, the need arises for an adaptive training system and method for use with the CAT tool. Existing training methods, such as training users only on tasks they failed to perform correctly during the CAT test, fails to take into account the following: (1) The CAT test is not converging simply on items previously missed, but rather on items close to a trained person&#39;s higher measured ability level; (2) if training after one CAT test is effective, it will have a “Heisenberg” effect on the path taken by the next CAT, such that with his/her measured ability improved, the trained person will now experience a more difficult CAT test; and (3) it is only when the training has been ineffective that the second CAT will more or less duplicate the path of the first CAT. Thus, training users only on tasks they failed to perform correctly on a CAT test merely trains user&#39;s on a handful of tasks they are unlikely to encounter on a subsequent CAT exam. Such a training method also fails to focus on the goal of raising an individual&#39;s overall skill level. Therefore, the need remains for a solution to the challenge of training individuals based on an adaptive skills evaluation technique that focuses on raising overall skill level in a desired skill set. The present invention provides such a solution.  
         SUMMARY OF THE INVENTION  
         [0008]    In accordance with the present invention, an adaptive training system for use with an adaptive testing tool includes a user interface receptive of a field of application selection from a user. A plurality of task items are stored in a data store. The plurality of task items are organized by field of application and ranked by difficulty level. An item selector selects an unfamiliar task item as a training task item based on the selected field of application and a user ability. The user ability is a function of previous user performance relating to task items of the selected field of application.  
           [0009]    The training tool of the present invention is advantageous over previous training tools because it focuses on raising a person&#39;s overall ability level relating to a field of application. The present invention further benefits by using training task items that correspond to testing task items, wherein both testing task items and training task items permit a user to explore various alternative paths to performing the task. The primary difference between the training and testing task items is the addition of a “Show Me” button to the training task items that a user can elect to use at points of difficulty, such that the user is assisted in accomplishing the task only as needed. Task items can double as both training task items and testing task items, wherein the “Show Me” button is only active (displayed and/or selectable) during a training mode. Further areas of applicability of the present invention will become apparent from the detailed description provided hereinafter. It should be understood that the detailed description and specific examples, while indicating the preferred embodiment of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention. 
       
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0010]    [0010]FIG. 1 is a partial-perspective, block diagram of an adaptive testing and training tool according to the present invention.  
         [0011]    [0011]FIG. 2 is a block diagram of an adaptive training system according to the present invention.  
         [0012]    [0012]FIG. 3 is a flow chart diagram of an adaptive training method according to the present invention.  
         [0013]    [0013]FIG. 4 is a detailed logic flow diagram for training item selection according to the present invention.  
     
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS  
       [0014]    The following description of the preferred embodiment(s) is merely exemplary in nature and is in no way intended to limit the invention, its application, or uses.  
         [0015]    An adaptive testing and training tool  100  according to the present invention is illustrated in FIG. 1. Accordingly, a user  102  interacts with the tool  100  via a user interface including a user input  104 , such as a computer keyboard, that is receptive of a field of application selection from the user  102 . In a preferred embodiment, the user  102  can select a field of application by specifying one or more job titles, and a plurality of corresponding training categories appear for user selection. The user interface further includes a user output  106  such as an active display. Moreover, the tool  100  has computer hardware in tower box  108  providing a computer memory and running an operating system with software accomplishing the present invention. Essentially, the tool  100  has a plurality of task items  110  organized by field of application, ranked by difficulty level, and stored in memory operable with a data processing system. An adaptive training module  112  is operable to select unfamiliar task items as training task items  114  based on a selected field of application and a user ability  116 . The adaptive training module  112  determines the user ability  116  as a function of previous user performance relating to task items  110  of the selected field of application.  
         [0016]    There are alternative ways by which information relating to a previous user performance can be obtained by adaptive training module  112 . For example, if the tool  100  has an adaptive testing module  120 , then the testing module  120  can administer a CAT exam to the user with some of the task items  110 . According to the CAT functionality, the testing module selects testing task items  122  for the user  102  based on user performance  124  during the exam. The test results  126  can then be automatically stored in computer memory for access by adaptive training module  112 . A further alternative involves a user inputting printed results of a CAT exam.  
         [0017]    A user can input results of a paper/pencil test  117  in one or more ways. Demonstratively, the user  102  could indicate a particular test of which the tool is aware, and check off tasks failed or tasks performed correctly on a GUI with checkboxes. The user can alternatively scan the results into a utility that does the checking automatically. The tool  100  can calculate the user ability  116  by having knowledge of the difficulty levels of the items on the test. Thus, in addition to being functional with a CAT system, the tool  100  may function to train individuals based on results of a test on many task items with a wide range of levels of difficulty. The tool may also be functional to print out such a test for skills evaluation in a field of application prior to training using test database  118  and printer  119 . In such an embodiment, the test could be used during training to assess the user&#39;s progress. Alternatively, the tool  100  can electronically administer a test on many task items with a wide range of levels of difficulty and automatically store results  126 . As a final alternative, a user can input printed results of a training session.  
         [0018]    Referring to FIG. 2, an adaptive training system  130  according to the present invention preferably has test results  126 , training session results  128 , and ranked and categorized task items  110  available in computer memory. In operation, user input  104  receives a field of application selection  132  from a user and communicates it to learning management system  134 . In response, learning management system  134  accesses test results  126  and training session results  128  and retrieves user performance information  136  for the current user, and further accesses task items  110  and retrieves a list of category-specific items  138  ranked by difficulty that correspond to the selected category. As discussed previously, user input  104  can alternatively receive some or all of the user performance information from the user.  
         [0019]    According to the preferred embodiment, the application specific items  138  and user performance information  136  are communicated to an ability evaluator  140  that computes the user ability  116  based on the difficulty levels of items previously performed correctly by the user. In turn, the application specific items  138  and user performance information  136 , and user ability are communicated to an item selector  142 . The item selector  142  selects as training task items  114  those items in the category that the user recently failed to perform correctly, and also several unfamiliar task items in the category that the user did not recently encounter. These unfamiliar items are selected from the application specific items  138  as a number of items having levels of difficulty that fall within a difficulty range, wherein the range is determined by the user ability and difficulty range limits  144 . These difficulty range limits  144  generally take the form of an offset in difficulty above and/or below the user ability  116 , and preferably are defined in terms of the units describing the user ability  116  and the difficulty level of the task items  110 . Thus, where difficulty level of task items is described in terms of logit units as well known in the art of CAT systems, the user ability is preferably determined in terms of a logit level. Thus, the difficulty range limits preferably take the form of a predefined number of logits above and below the user ability. Selected training task items are communicated to user output  106 .  
         [0020]    An adaptive training method according to the present invention is described with reference to FIG. 3. Beginning at  146 , the method proceeds to step  148 , wherein a history of a last test and/or last training session is retrieved. The method proceeds to step  150  where a user ability is determined based on the user performance, and then to step  152  where a difficulty range is determined based on the user ability. The method then proceeds to training the user by training the user on items missed on the last test and/or last session at step  154 . Further, the method trains the user on unfamiliar task items not encountered in a last test and/or last session, by training the user on task items having difficulty levels in the range below the user ability at step  156 , and by training the user on task items having difficulty levels in the range above the user ability at step  158 . The method proceeds to step  160 , wherein the session history is recorded, and ends at  162 .  
         [0021]    The detailed logic flow diagram of FIG. 4 demonstrates a computer automated process suitable for accomplishing the method of the present invention in a manner that may be implemented with the adaptive training system of the present invention. This process focuses on generating a queue of training items based on a user selected category for delivery to the user for training purposes. Accordingly, the process begins at  164  and proceeds to step  166 , wherein the last test history for a selected category is retrieved. At step  168 , the process retrieves the last training session history for the category, if any. The process then retrieves the list of application specific task items at step  170 , and then proceeds to step  172  wherein item difficulties defined in terms of logits are retrieved. The process deselects passed items on the last test and last training session history at step  174 , thus effectively making them unselectable. Then the process proceeds to step  176  and calculates the user ability A u  in terms of logits as a sum of average difficulty level of task items performed correctly by the user, and a natural log of a quotient of a number of task items performed correctly by the user and a number of task items performed incorrectly by the user according to:  
           A   u   =avg _difficulty(items_correct)+In(items_correct/items_missed).  
         [0022]    The process then proceeds to step  178 , wherein items missed on the last test or during the last training session are placed in the training task item queue, and the selectable task items remaining in the list are sorted by difficulty level at step  180 . At step  182 , a total number of items to add to the training queue N is calculated as twenty minus the number of items missed according to:  
           N= 20−items_missed.  
         [0023]    The number 20 is chosen to ensure an adequate number of training items without overburdening the user, but may be modified without departing from the present invention. In an alternative embodiment, the total number is calculated as a function of user ability to ensure more training for users with lower ability levels and less training for users with higher ability levels. As a further alternative, distance of the user ability from a cut point used in the CAT exam to define user proficiency can be used to define or modify the total number. At step  184 , a numerical requirement is determined for a total number of items to add that have a difficulty level below the user ability, such as, for example, twenty percent of the total number determined in step  182 , and the process is ready to begin adding unfamiliar items to the training queue.  
         [0024]    At step  186 , the process selects an unfamiliar task item (one not passed on a previous test or training session) from the category-specific list of task items, and proceeds to  188 , wherein it is determined if the total number of items to add has been reached. If so, the method ends at  190 . Otherwise, a determination is made at  192  whether the numerical requirement for training items having difficulty levels below the user ability has been met. If not, the process checks whether the item difficulty is too low at  194 , and a lower bound difficulty range limit of one logit is preferred. The process further checks whether the selected task item has a difficulty level below the user ability at  196 . If the selected task item has a difficulty level below the user ability and is within the defined range, it is added to the training queue at step  198 . Otherwise, a new item is selected at step  186  and processing continues from there. Steps  186  through  198  proceed recursively until the total number of items is added, in which case the process ends, or the numerical requirement is filled, in which case the process proceeds to step  200 , wherein an unfamiliar task item (one not passed on a previous test or training session) from the category-specific list of task items is selected. The process then check whether the total number of items has been added at step  202 , in which case the process ends at  198 . If not, a determination is made at  204  as to whether the selected task item difficulty level is too high, and an upper bound difficulty range limit of two and one-half logits is preferred. The process further checks whether the difficulty level of the selected item is above the user ability. If the selected task item has a difficulty level above the user ability and is within the defined range, it is added to the training queue at step  208 . Otherwise, a new item is selected at step  186  and processing continues from there. Steps  200  through  208  proceed recursively until the total number of items is added and the process ends at  190 . Thus, a queue of training task items is generated and output (displayed) to the user for training purposes.  
         [0025]    The description of the invention is merely exemplary in nature and, thus, variations that do not depart from the gist of the invention are intended to be within the scope of the invention. Such variations are not to be regarded as a departure from the spirit and scope of the invention.