Abstract:
A method and computer program product and tool for providing a predictive model that evaluates business needs and requirements against factors of motivation, content relevance and design to provide an indication of a learner&#39;s success in applying a training in his/her job situation. The model can generate a value that is compared against a threshold. The threshold is dependent upon the skill type to be applied in executing a job role. Based on the comparison result, the manager or employer makes an informed decision as to the value of the prospective training of the learner. For example, if the value is greater than the threshold, it can be expected that the learner&#39;s training will be applied to the job. If the value is lower than the threshold, it can be expected that the learner&#39;s training will not be valuable post-training.

Description:
BACKGROUND 
       [0001]    1. Technical Field 
         [0002]    The present invention relates to the field of evaluating training efficacy for the work place, and more particularly, to a system and method for generating a single forecast success predictor used for forecasting learning and evaluating a likelihood that a trainee will utilize a skill obtained in training to an on-the-job activity. 
         [0003]    2. Description of the Related Art 
         [0004]    The American Society for Training and Development estimates that, in the US, companies spent $171.5 billion on employee training for 2012. Yet, research suggests that for every training dollar spent, as little as 20 cents of that is actually used in the work place. It had been observed that, generally, trained skills are applied in the work setting at an estimated rate of 10%. More specifically, statistics have been found that have determined that one year after instruction, just 15% of the learners were able to recall and use training. Currently managers, who are responsible for approval of funding for these endeavors do not have any system for forecasting the likelihood that the learner will ever utilize the learned skill in their work setting. 
         [0005]    Current methods for evaluation of skills of a trainee, e.g., in a corporate setting, require that the manager develop and understand the details of training and learning which development may require and take years of education and experience in the training and learning environment. 
         [0006]    For a manager, becoming this kind of subject matter expert costs thousands of real dollars. It also has opportunity cost because resources devoted to “learning about learning” are taken away from the tasks required to manage people. 
       SUMMARY 
       [0007]    In order to reduce education waste, before the training even takes place, a system and method provides for the evaluation of learning by providing a way to effectively determine whether a trained skill will translate into on-the-job use. 
         [0008]    The system and method combines separate, yet confounding factors into an approach that provides decision support for managers, learners and education designers thus, eliminating waste and eliminating the inefficiencies in the education sector. 
         [0009]    According to one aspect, the system and method provides a predictive model that evaluates business needs and requirements against factors of motivation, content relevance and design to provide an indication of a learner&#39;s success in applying the training in his/her job situation. The model can generate a value that is compared against a threshold. The threshold is dependent upon the job type. Based on the comparison result, the manager or employer can make an informed decision as to the value of the prospective training of the learner. For example, if the value is greater than the threshold, it can be expected that the learner&#39;s training will be applied to the job and hence valuable from a business prospective. If the value is lower than the threshold, it can be expected that the learner&#39;s training will not be valuable post-training. 
         [0010]    In one aspect, there is provided a method of forecasting skills transfer effectiveness. The method comprises: receiving indicator data relating to a skills transfer metric; assigning a numeric value corresponding to each received indicator data; computing a score based on the assigned numeric values; comparing the score against a predetermined threshold; and sending one or more signals to a user device for indicating a result of the comparing, the user device responsive to the result for recommending an action to admit a learner entry or deny a learner entry to a training program, wherein a programmed hardware processor device performs the receiving, assigning, computing, comparing, and sending result indicating signals. 
         [0011]    In a further aspect, there is provided a system of forecasting skills transfer effectiveness. The system comprises: a memory storage device, a hardware processor device coupled to the memory storage device and configured to perform a method to: receive indicator data relating to a skills transfer metric; assign a numeric value corresponding to each received indicator data; compute a score based on the assigned numeric values; compare the score against a predetermined threshold; and send one or more signals to a user device for indicating a result of the comparing, the user device responsive to the result for recommending an action to admit a learner entry or deny a learner entry to a training program. 
         [0012]    A computer program product is provided for performing operations. The computer program product includes a storage medium readable by a processing circuit and storing instructions run by the processing circuit for running a method. The storage medium readable by a processing circuit is not only a propagating signal. The method is the same as listed above. 
     
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
         [0013]      FIG. 1  depicts a Learner Enablement Forecasting System (LEFSys) implementing a Return-On-Investment (ROI) Training predictive model that runs on a computer system; 
           [0014]      FIG. 2  depicts an example spreadsheet implementation  100  of the predictor model showing receipt and processing of indicators that are associated with a LEFSys model Motivation processing component of  FIG. 1  in one embodiment; 
           [0015]      FIGS. 3A-3C  show an example spread sheet program implementation depicting receipt and processing of indicators that are associated with a LEFSys model Content and Delivery processing component of  FIG. 1  in one embodiment; 
           [0016]      FIGS. 4A-4B  show an example spread sheet program implementation depicting receipt and processing of indicators that are associated with a LEFSys model Post-Training processing component of  FIG. 1  in one embodiment; 
           [0017]      FIG. 5  shows the LEFSys modeler implementation of a Subject Matter Matrix used to compute an Adjusted Design Average in one embodiment; 
           [0018]      FIG. 6  shows the LEFSys modeler implementation of a Motor-Cognitive Environment Matrix used to compute a modified post-training fitness for environment sub-total; 
           [0019]      FIG. 7  shows the LEFSys modeler implementation of a Motor-Cognitive Application Matrix used to compute a modified post-training fitness for application sub-total; 
           [0020]      FIG. 8  shows a detailed flowchart indicating the LEFSys model process steps according to one embodiment; and 
           [0021]      FIG. 9  illustrates one embodiment of an exemplary hardware configuration of a computing system programmed to perform the method steps described herein. 
       
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
       [0022]    Detailed embodiments of the invention will be described in conjunction with the accompanying drawings. It should be appreciated that the following description of the detailed embodiments are to explain the execution of an example of the invention, rather to impose any limitation on the scope. 
         [0023]    A system and method forecasts whether a specific skill being transferred to a user (e.g., a learner, trainee or employee) during training is likely to be used by the learner or employee during post-training activity. The system is a computer implemented system, referred to herein as a Learning Evaluation Forecasting System (LEFSys), that uses multiple on-the-job performance factors related to employee record and business environment to predict skill application of a trainee or “learner”. Furthermore, since managers normally do not have extensive knowledge of training design and characteristics, LEFSys performs in a decision support role to recommend the best training solution from a business perspective. 
         [0024]    As shown in  FIG. 1 , the Learner Enablement Forecasting System (LEFSys)  10  includes a Return-On-Investment (ROI) Training predictive model  12  that runs on a computer system  400  and is configured to analyze the likelihood of a return on training investment based on the Motivation (pre-training), Content and Delivery (training) and Post-Training environments. The computer system  400  receives automatically (via the computer system) or by manual input, multiple attribute variables associated with each of the Motivation, Content and Delivery, and Post Training environments. The model  12  is configured for translating the received multiple attribute variables or “indicators” into quantitative (e.g., numeric) values. The Predictor model  12  assesses their collective impact on the skills transfer. The final output, a Forecast Value  50 , is an overall score forecasting whether or not the planned learning will transfer into on-the-job use. 
         [0025]    In particular, the predictive model  12  runs three main processing components: Motivation  15 , Content and Delivery  25 , and Post Training  35 . Each of these processing components are represented by further processing sub-components  13  of which various input attribute variables or “indicators” are associated. These input attribute variables are assigned quantitative values and uniquely used. 
         [0026]    In the embodiment depicted in  FIG. 1 , the Motivation processing component  15  of model  12  receives attribute variables or indicator inputs for processing by: Goal Setting processing sub-component  16 ; Learner Motivation processing sub-component 18 ; Learner Personality processing sub-component  20 ; and Environment processing sub-component  22 . Likewise, the Content and Delivery processing component  25  of the LEFSys modeler  12  receives attribute variables or indicator inputs for processing by: Architecture/Framework processing sub-component  26 ; subject matter processing sub-component 28 ; Practice processing sub-component  30 ; Training Setting processing sub-component  32 ; and Skill type processing sub-component  34 . Further, the Post Training processing component  35  of the LEFSys model  12  receives attribute variables or indicator inputs for processing by Fitness for Environment processing sub-component  36  and Fitness for Application processing sub-component  38 . 
         [0027]    In one embodiment, the predictive model  12  inputs and uses up to 41 attribute variables associated with the Motivation, Content and Delivery, and Post Training components. These variables may be automatically input to the system, or input by an individual who has information on the learner, skill, training and work environment. The variables may be a range, e.g., low, medium, high, or may be Boolean in nature (e.g., Yes/No values). The predictive model  12  performs methods to assign or map each of the attribute variables into a corresponding numeric value. In one embodiment, for example, an attribute variable of “low” may translate to a numeric value of 1, an attribute variable of “medium” may translate to a numeric value of 3, and an attribute variable of “high” may translate to a numeric value of 5. 
         [0028]    The predictive model  12  performs methods to combine the assigned or mapped numeric values of the attributes values according to a formula. In one embodiment, the formula includes a linear function. Based on the resultant numeric value, the model  12  generates the forecast value  50  indicating whether or not the training will transfer onto the job for that learner and funding sponsor. The forecast value  50  is the ROI (return on investment) predictor that indicates the likelihood of a return on training investment based on the motivation (pre-training), content/delivery (training) and post training environments. 
         [0029]      FIG. 8  depicts one implementation of the LEFSys modeling method  600  running in computing system  400 . The LEFSys modeler  12  run by a hardware processor in the computing system receives values indicating the various skill transfer metrics and computes a final forecast value. The modeler at  610  first receives data including indicator data characterizing a trainee&#39;s motivation factors. The data received by the LEFSys modeler  12  includes indicators or attributes relating to Goal Setting factors, learner motivation factors, learner personality factors, and environment factors. Concurrently or subsequently at  615 , the modeler assigns quantitative values corresponding to each trainee motivation factor indicator received and computes a Motivation component sub-total value from the assigned quantitative values. Then, at  620 , the LEFSys modeler  12  receives indicator data characterizing content and delivery factors of a job training program. The data received by the LEFSys modeler  12  includes content and delivery indicators or attributes relating to: an architecture/framework, subject matter, practice, training setting, and a skill type. Concurrently or subsequently, at  625 , the modeler assigns quantitative values corresponding to each content and delivery factor indicator received and computes a content and delivery sub-total value. Continuing at  627 , the modeler computes an adjusted Open-closed skill type subject matter average value based on implementation of a subject matter score mapping matrix. Then, the modeler computes a Content and Delivery Fitness value based on the content and delivery sub-total value and the computed average value. 
         [0030]    Continuing, at  630 , the LEFSys modeler  12  receives indicator data characterizing post-training factors of the job training program. The data received by the LEFSys modeler  12  includes post-training indicators or attributes characterizing a fitness for Environment and a fitness for application. Concurrently or subsequently, at  635 , the modeler assigns quantitative values corresponding to each post-training factor indicator received and computes post training factor sub-totals: an environment sub-total value and an application sub-total value. Then, at  640 , there are computed adjusted Post Training (environment and application) factors subtotals using respective Motor/Cognitive environment score mapping matrix and Motor/Cognitive application score mapping matrix, respectively. 
         [0031]    Then, at  645  the modeler initiates the hardware processor to compute a forecast value based on the motivation component sub-total, content and delivery fitness and adjusted post training factors values relating to the skill transfer metrics received. At  650 , the modeler initiates the hardware processor to compare the forecast value against a predetermined threshold value. If the comparison results in the forecast value being greater than the threshold, then at  655 , the hardware processor generates signals for a user device to indicate a recommendation to admit a trainee entry into the job training program. Otherwise, if the comparison results in the forecast value being less than the threshold, then at  660 , the hardware processor generates signals for a user device to indicate a recommendation to deny a trainee entry into the job training program. 
         [0032]    In one embodiment, LEFSys  10  and predictor model  12  may be implemented as a spreadsheet program (e.g., Excel® Spreadsheet (Trademark of Microsoft Corp.) running on computer system  200  that is programmed to receive the inputs associated with each attribute variable, and generate the forecast value according to formulae. It is understood that other spreadsheet and/or computer program implementations may be used. 
         [0033]      FIG. 2  depicts an example spreadsheet implementation  100  of the predictor model  12  that is associated with a LEFSys model Motivation processing component  15 . The model display implementation  100  is provided via a display interface  438  of computer system  400 . As shown in  FIG. 2 , from manual input provided by a user (e.g., a learner or manager) via the displayed spreadsheet interface, or via automated (computer system) signal inputs, the LEFSys model Motivation processing component  15  receives: Goal Setting attribute variables or indicators  110 ; Learner Motivation indicator inputs  120 ; Learner Personality indicator inputs  130 ; and Environment indicator inputs  140 . In one embodiment, each of the attribute variables or indicators  110 ,  120 ,  130  and  140  are input via an individual drop down selection menu  115  associated with each particular indicator. Generally, the model  12  assigns each entered low, medium or high indicator input a corresponding numeric value, e.g., 1, 3 or 5. This value is depicted in a corresponding spreadsheet value field  119  associated with each corresponding input numeric and each value is stored in a memory of computer system  400  for processing according to an applied function. 
         [0034]    With respect to Goal Setting processing sub-component inputs  110 : One goal setting input  112  is a value (e.g., low, medium or high) entered into the system  10  via menu  115  for indicating: a) a learner&#39;s understanding of the course objective. In one embodiment, LEFSys assigns this low, medium or high input value a corresponding numeric value 1, 3 or 5 and this value is stored in the system as a value in a variable, e.g., A1. A second goal setting input  114  is a value (e.g., low, medium or high) entered into the system via menu  115  that characterizes: b) a learner&#39;s understanding of the opportunity to apply the skill. This entered low, medium or high value input is assigned a corresponding numeric value 1, 3 or 5 and is stored by the system as a value in a variable A2. A third goal setting input  116  is a value entered into the system that characterizes: c) a manager&#39;s understanding of the course outcomes. This entered low, medium or high value input is assigned a corresponding numeric value 1, 3 or 5 and is stored by the system as a value in a variable A3. 
         [0035]    After inputting values for these Goal Setting indicators  16  the modeler further computes and stores in a register or computer memory location an overall Goal setting indicator sub-total value. In one embodiment, this Goal setting indicator sub-total value is computed by a processing device according to: 
         [0000]      Goal setting:  A 1+ A 2+ A 3 
         [0036]    With respect to Learner Motivation processing sub-component inputs  120 . One learner motivation input  121  is a value (e.g., low, medium or high) entered into the system  10  for indicating: a) a trainee&#39;s desire to take the course. This entered low, medium or high value input is assigned a corresponding respective numeric value 1, 3 or 5 and is stored by the system as a value in a variable, e.g., B1. A second learner motivation input  122  is a value (e.g., low, medium or high) entered into the system for indicating: b) a degree to which the learner can opt out of the training. This entered low, medium or high value input is assigned a corresponding respective numeric value 1, 3 or 5 and is stored by the system as a value in a variable B2. A third learner motivation input  123  is a value (e.g., low, medium or high) entered into the system for indicating: c) a trainee&#39;s history of learning and applying skills. This entered low, medium or high value input is assigned a corresponding respective numeric value 1, 3 or 5 and is stored by the system as a value in a variable B3. A fourth learner motivation input  124  is a value (e.g., low, medium or high) entered into the system for indicating: d) how training fits into career goals agreed upon by trainee. This entered low, medium or high value input is assigned a corresponding respective numeric value 1, 3 or 5 and is stored by the system as a value in a variable B4. A fifth learner motivation input  125  is a value (e.g., low, medium or high) entered into the system for indicating: e) a learner&#39;s prior knowledge of this skill area. This entered low, medium or high value input is assigned a corresponding respective numeric value 1, 3 or 5 and is stored by the system as a value in a variable B5. A sixth learner motivation input  16  is a value (e.g., low, medium or high) entered into the system for indicating: g) a learner&#39;s pre-training preparation. This entered low, medium or high value input is assigned a corresponding respective numeric value 1, 3 or 5 and is stored by the system as a value B6. A seventh learner motivation input  127  is a value (e.g., low, medium or high) entered into the system for indicating: h) a learner&#39;s performance level. This entered low, medium or high value input is assigned a corresponding respective numeric value 1, 3 or 5 and is stored by the system as a value B7. An eight learner motivation input  128  is a value (e.g., low, medium or high) entered into the system for indicating: I) a degree of work and stress load during training. This entered low, medium or high value input is assigned a corresponding respective numeric value 1, 3 or 5 and is stored by the system as a value B8. 
         [0037]    After inputting values for these Learner Motivation indicators  120  the modeler  12  further computes and stores in a register or computer memory location an overall Learner Motivation indicator sub-total value. In one embodiment, this Learner Motivation indicator sub-total value is computed by a processing device according to: 
         [0000]      Learner Motivation:  B 1+ B 2+ B 3+ B 4+ B 5+ B 6+ B 7+ B 8 
         [0038]    With respect to Learner Personality processing sub-component inputs  130 . One learner personality input  131  is a value (e.g., low, medium or high) entered into the system  10  for indicating: a) a learner&#39;s Self-efficacy. This entered low, medium or high value input is assigned a corresponding respective numeric value 1, 3 or 5 and is stored by the system as a value in a variable, e.g., C1. A second learner personality input  132  is a value (e.g., low, medium or high) entered into the system for indicating: b) a learner&#39;s conscientiousness. This entered low, medium or high value input is assigned a corresponding respective numeric value 1, 3 or 5 and is stored by the system as a value in a variable C2. A third learner personality input  133  is a value (e.g., low, medium or high) entered into the system for indicating: c) a learner&#39;s goal orientation. This entered low, medium or high value input is assigned a corresponding respective numeric value 1, 3 or 5 and is stored by the system as a value in a variable C3. A fourth learner personality input  134  is a value (e.g., low, medium or high) entered into the system for indicating: d) a learner&#39;s self-understanding of learning style (i.e., a learner&#39;s awareness of learning style). This entered low, medium or high value input is assigned a corresponding respective numeric value 1, 3 or 5 and is stored by the system as a value in a variable C4. 
         [0039]    After inputting values for these Learner Personality indicators  130 , the modeler  12  further computes and stores in a register or computer memory location an overall Learner Personality indicator sub-total value. In one embodiment, this Learner Personality indicator sub-total value is computed by a processing device according to: 
         [0000]      Learner Personality:  C 1+ C 2+ C 3+ C 4 
         [0040]    With respect to Environment sub-component processing inputs  140 . One environment input  141  is a value (e.g., low, medium or high) entered into the system  10  for indicating: a) whether the training supports business goals/objectives. This entered low, medium or high value input is assigned a corresponding respective numeric value 1, 3 or 5 and is stored by the system as a value in a variable, e.g., D1. A second environment input  142  is a value (e.g., low, medium or high) entered into the system for indicating: b) a learner&#39;s expectation to employ skill in training and leading others. This entered low, medium or high value input is assigned a corresponding respective numeric value 1, 3 or 5 and is stored by the system as a value in a variable D2. A third environment input  143  is a value (e.g., low, medium or high) entered into the system for indicating: c) a level of business critical. This entered low, medium or high value input is assigned a corresponding respective numeric value 1, 3 or 5 and is stored by the system as a value in a variable D3. A fourth environment input  144  is a value (e.g., low, medium or high) entered into the system for indicating: d) a learner&#39;s desire to allocate the learning effort through time off, work load, etc. This entered low, medium or high value input is assigned a corresponding respective numeric value 1, 3 or 5 and is stored by the system as a value in a variable D4. A fifth environment input  145  is a value (e.g., low, medium or high) entered into the system for indicating: e) a degree to which management supports training and implementation. This entered low, medium or high value input is assigned a corresponding respective numeric value 1, 3 or 5 and is stored by the system as a value in a variable D5. 
         [0041]    After inputting values for these Environment indicators  140 , the modeler  12  further computes and stores in a register or computer memory location an overall Environment indicator sub-total value. In one embodiment, a total Learner Personality indicator sub-total value is computed by a processing device according to: 
         [0000]      Environment:  D 1+ D 2+ D 3+ D 4+ D 5 
         [0042]    From each of the sub-totals for the Motivation component  15  indicator values, i.e., the “Goal Setting” indicators sub-total, the “Learner Motivation” indicators sub-total, the “Learner Personality” indicators sub-total, and the “Environment” indicators sub-total, the LEFSys model  12  is configured to compute an overall Motivation component sub-total is computed and stored in computing system  400  according to: 
         [0000]      Motivation=[ A 1:  A 3+ B 1: B 8+ C 1: C 4+ D 1: D 5]*(weight) 
         [0000]    where A1:A3 represents the Goal Setting subtotal A1+A2+A3, B1:B8 represents the Learner Motivation subtotal: B1+B2+B3+B4+B5+B6+B7+B8; C1:C4 represents the Learner Personality subtotal: C1+C2+C3+C4; and D1:D5 represents the Environment subtotal: D1+D2+D3+D4+D5 and the “weight” is a factor reflecting a percent proportional influence of the overall training outcome. In one example, this weight value is 40% or 0.4. But it may range between values 0.37 to 0.43. The Motivation component sub-total value is shown in the display of the spreadsheet implementation of  FIG. 2  in a sub-total field  149 . 
         [0043]      FIGS. 3A-3C  depict example spreadsheet implementations  200 A,  200 B and  200 C of the predictor model  12  that is associated with the LEFSys model Content and Delivery processing component  25 . The model display implementations are provided via a display interface  438  of computer system  400 . The Content and Delivery processing component  25  of the LEFSys modeler  12  is configured to evaluate content and delivery attributes for fitness with the pre-training attributes and the objectives of the training. This fitness is critical to the modeler&#39;s predictive capability. To produce this final value, as shown in  FIGS. 3A-3B , from manual input provided by a user (e.g., a learner or manager) via the displayed spreadsheet interface, or via automated (computer system) input, the LEFSys model Content and Delivery processing component  25  receives the attribute variables or indicators associated with the Architecture processing sub-component  26 , Practice processing sub-component  28 , Training Setting processing sub-component  32 , and Skill type processing sub-component  34 . 
         [0044]    In one aspect, the Architecture processing sub-component  26  integrates the “how”, “what” and “who” of the training into the modeler  12 . There are attribute variables or indicators input for three Architecture sub-component segments: a Framework segment  202 , a Subject Matter segment  204 , and a Learner knowledge segment  206 . 
         [0045]    In one embodiment, as shown in the example spread sheet program implementation of  FIG. 3A , the Architecture Framework segment  202  receives a single attribute variable or indicate input that considers the impact of the size of information transferred and the feedback loop of the learner, i.e., the “how” variable of the architecture. The feedback loop is the process whereby learner takes his/her basic understanding of a concept and refines and broadens this understanding through a variety of mechanism such as testing, practice, asking questions, and peer reviews. Each input carries a varying degree of training transfer risk and the user must select one out of four design options  203 . LEFSys modeler  12  translates that one user selection into a numeric value. For example, the Framework input is a value (e.g., A, B, C and D) that may be manually input into the system  10  (e.g., by a manager) via a drop down selection menu  215 . The selected input value, e.g., A, B, C and D, corresponds to a respective training program design option: a Simple text presentation with no interaction (i.e., Receptive), e.g., option A,  210 ; a Short sequences of information with response opportunity (i.e., Directive), e.g., option B  211 ; a Simulation, coaching, problem centered (i.e., Guided Discovery) option C  212 ; and an Open ended (i.e., Exploratory) option D  213 . This entered option value (e.g., Receptive A, Directive B, Guided Discovery C or Exploratory D) input is assigned by LEFSys modeler a corresponding respective numeric value 5, 4, 3 2, and is stored in a memory location in the system as a value in a variable, e.g., E. A corresponding numeric value may be displayed in a field  219  as shown in  FIG. 3A . 
         [0046]    In one embodiment, the Architecture Subject Matter segment  204  considers characteristics focusing on what is being communicated as it relates to the training method. The content of training carries a varying degree of training transfer risk. More than one characteristic can apply to what is being communicated during the training. Thus, the user may select or input system can designate one or more characteristics 205 of the subject matter. 
         [0047]    One Subject Matter attribute variable input  220  is a value (e.g., Yes, No) entered into the system  10  (e.g., by a manager) that corresponds to a “Notice” training content, (e.g., Regulatory, legal, bulletins; or content that is short in length). If a “yes” is selected for the Notice subject matter input, the modeler  12  assigns a quantitative (i.e., numeric) value of 5 that is stored by the system as a value in a variable, e.g., F1. Another Subject Matter input  221  that may be entered into the system  10  is a value (e.g., Yes, No) that corresponds to an “Announcement” training content (e.g., a Change in working conditions, job responsibilities). If a “yes” is selected for the Announcement subject matter input, the modeler  12  assigns a value of 5 that is stored by the system as a value in a variable F2. Another Subject Matter input  222  that may be entered into the system  10  is a value (e.g., Yes, No) that corresponds to a “Procedures” training content (e.g., Step by step activities or job tasks). If a “yes” is selected for the Procedures subject matter input, the modeler  12  assigns a quantitative value of 4 that is stored by the system as a value in a variable F3. Another Subject Matter input  223  that may be entered into the system  10  is a value (e.g., Yes, No) corresponding to a “Problem solving” training content (e.g., critical and analytical). If a “yes” is selected for the Problem solving subject matter input, the modeler  12  assigns a value of 3 that is stored by the system as a value in a variable F4. Another Subject Matter input  224  that may be entered into the system  10  is a value (e.g., Yes, No) that corresponds to “Skill” training content that can be applied into different contexts. If a “yes” is selected that corresponds to “Skill”, the modeler  12  assigns a value of 2 that is stored by the system as a value in a variable, e.g., F5. 
         [0048]    After inputting each of the one or more applicable Architecture Subject Matter segment  26  indicator inputs  205 , the modeler  12  further computes and stores in a register or computer memory location an overall Subject Matter segment sub-total according to: 
         [0000]      Subject Matter:  F 1+ F 2+ F 3+ F 4+ F 5 
         [0049]    As will be explained in greater detail, in one embodiment, the LEFSys modeler  12  uses the Architecture Subject Matter processing segment to manufacture two variables: the subject matter segment sub-total value, and a weighting for the overall Content and Delivery Open-closed Skill Type Fitness processing sub-component indicator  34 . The Subject Matter weighting is represented as impacting an Open-closed Skill Type Fitness sub-total value as depicted by the Skill Type impact line  40  shown in  FIG. 1 . 
         [0050]    Referring back to  FIG. 3A , in one embodiment, the Architecture Learner Knowledge segment  206  receives a single attribute variable or indicator input that considers the learner&#39;s level of knowledge by bringing in a variable that factors what the learner already knows about the skill. The user (e.g., learner or manager) selects one from of the following four attributes  207 : Novice, Advanced Beginner, Proficient, and Expert. 
         [0051]    The Novice Learner Knowledge attribute variable input  230  is a value that characterizes the learner as a beginner with no experience. The learner knows general rules to help perform tasks. Knows context-free rules, independent of specific cases, and applied universally. The learner knows simple and inflexible pieces of information. The learner must be told what to do related to the skill. When the Learner Knowledge Novice indicator is selected, the input is a value (e.g., a value “A”) entered via a selection menu into the system  10 , and is assigned a quantitative value, e.g., 5. 
         [0052]    The Advanced Beginner attribute variable input  231  characterizes the learner as demonstrating acceptable performance of the skill. The learner has gained prior experience in actual situations to recognize recurring meaningful components and knows the principles and based on experiences can begin to be formulated to guide actions. When the Learner Knowledge Advanced Beginner indicator is selected, the input is a value in a variable (e.g., “B”) entered via a selection menu into the system  10 , and is assigned by the LEFSys model a quantitative value of 4. 
         [0053]    The Proficient attribute variable input  232  characterizes the learner as perceiving and understanding situations employing the skill as whole parts. The learner has a more holistic understanding which improves decision-making and learns from experiences what to expect in certain situations and how to modify plans. The learner is more aware of long-term goals and gains perspective from planning own actions based on conscious, abstract, and analytical thinking. When the Proficient Learner Knowledge indicator is selected, the input is a value (e.g., a value “C”) entered via a selection menu into the system  10 , and is assigned a quantitative value of 3. 
         [0054]    The Expert attribute variable input  233  characterizes the learner as no longer relying on principles, rules, or guidelines to connect situations and determine actions. The learner has much more background of experience and has an intuitive grasp of clinical situations. The learner&#39;s performance is fluid, flexible, and highly proficient. When the Expert Learner Knowledge indicator is selected, the input is a value (e.g., a value “D”) entered via a selection menu into the system  10 , and is assigned a quantitative value of 2. 
         [0055]    From the single entered Learner Knowledge value (e.g., Novice A, Advanced Beginner B, Proficient C or Expert D) input, the LEFSys modeler assigns a corresponding respective numeric value 5, 4, 3 2, that is stored by the system as a value in a variable, e.g., G. The corresponding numeric value may be displayed in a field  234  as shown in  FIG. 3A . 
         [0056]    In one aspect, the Practice processing sub-component  28  considers the number of new skills or tasks the training covers, as well as how often these are reinforced during the course of the training. It also considers how realistic the training is. These attributes, and the degree to which they apply, are positively correlated to skill retention and usage after training. Users (or automated) input the degree to which each of these attributes are true and the LEFSys modeler  12  translates these inputs into numeric values for use in the forecasting formula. 
         [0057]    In one embodiment, as shown in the example spread sheet program implementation  200 B of  FIG. 3B , the Practice processing sub-component  28  receives attribute variables  250  from a user, e.g., a manager, or system. 
         [0058]    One Practice processing sub-component input  251  is a value or (e.g., low, medium or high) entered into the system  10  via a drop down selection menu (not shown), for indicating: a) a “Breadth” of training, e.g., representing a number of tasks to be learned in the training. This entered low, medium or high value input is assigned a corresponding numeric value 1, 3 or 5 respectively and is stored in a memory by the system as a value in a variable, e.g., H1. A second Practice indicator input  252  is a value (e.g., low, medium or high) entered into the system  10  for indicating: b) an Activity Level representing a degree of frequency and repetition involved with the task. This entered low, medium or high value input is assigned a corresponding numeric value 1, 3 or 5 respectively and is stored by the system as a value in a variable, e.g., H2. A further Practice indicator input  253  is a value (e.g., low, medium or high) entered into the system  10  that characterizes: c) a degree of similarity to actual job environment. This entered low, medium or high value input is assigned a corresponding numeric value 1, 3 or 5 respectively and is stored by the system as a value in a variable, e.g., H3. Numeric values assigned as H1-H3 are shown in corresponding display fields  255 . 
         [0059]    After receiving (via a manual or automated fashion) input values for the Practice processing sub-component indicators  250 , the LEFSys modeler  12  further computes and stores in a register or computer memory location an overall Practice indicator sub-total value. In one embodiment, this Practice indicator sub-total value is computed by a processing device according to: 
         [0000]      Practice:  H 1+ H 2+ H 3 
         [0060]    In one embodiment, as shown in the example spread sheet program implementation  200 B of  FIG. 3B , the Training Setting processing sub-component  32  receives from a user, e.g., a manager, or automated means, one of six attribute variables  260  that influences the actual training outcome. Via manual (or automated) inputs, one of the six possible settings  260  is selected via a drop down menu (not shown) and the LEFSys modeler  12  translates this input into a numeric value for use in the forecasting formula. 
         [0061]    One selectable input  261  characterizing the Training Setting is a.) Classroom training with an instructor. If this attribute is selected, the modeler assigns this input into a quantitative value, e.g., 5. Another selectable input  262  characterizing the Training Setting is b.) whether there is a one-on-one with instructor training. If this setting is selected, the modeler assigns this input into a numeric value of 5. Another selectable input  263  characterizing the Training Setting is c.) a Just-in-time training. If this setting is selected, the modeler assigns this input into a numeric value of 4. Another selectable input  264  characterizing the Training Setting is d.) if the training is E-learning facilitated. If this setting is selected, the modeler assigns this input into a numeric value of 4. Another selectable input  265  characterizing the Training Setting is e.) if the E-learning self paced. If this setting is selected, the modeler assigns this input into a numeric value of 3. A final selectable input  266  characterizing the Training Setting is f.) whether the training is Self paced. If this setting is selected, the modeler assigns this input into a numeric value of 2. 
         [0062]    After inputting a single Training Setting indicator, the LEFSys modeler  12  further computes and stores in a register or computer memory location an overall Training Setting processing sub-component sub-total, e.g., a value in a variable (I). In one embodiment, this Training Setting indicator sub-total value (I) is computed by a processing device. The corresponding numeric value may be displayed in a field  269  as shown in  FIG. 3A . 
         [0063]    With the above quantitative values entered into the system, LEFSys modeler  12  creates a Subtotal value for the Content and Delivery component  25  according to a formula: 
         [0000]      Subtotal Content and Delivery=(Framework))+(SubjectMatter)+(LearnerKnowlege)+(Practice)+(TrainingSetting) 
         [0064]    The Content and Delivery component sub-total value is shown in the display of the spreadsheet implementation of  FIG. 3B  in a sub-total field  275 . 
         [0065]    In one embodiment, as shown in the example spread sheet program implementation  200 B of  FIG. 3B , the Skill Type processing sub-component  34  receives a further input attribute  270  characterizing the skill type or nature of the skill. In one embodiment, the skill type attribute  270  is one of: Open or Closed. As described herein, the skill type (open or closed) drives an adjusted design average as described herein with respect to  FIG. 3C . In one embodiment, a Closed-loop skill type is where the work environment is predictable—the skill is performed in an unchanging environment. The action, physical or mental is the goal of the skill. An Open-loop skill type is where the work environment is variable and unpredictable during action. The task typically involves problem solving and/or continuous responses that are repeated and do not have a definite beginning or end. Via manual or automated technology, the skill type attribute  270  is entered into the system  10 . With the Skill type variable for Open-closed and Subject Matter (Fn)) selections known, the LEFSys modeler  12  creates an Adjusted Design Average (i.e., the final output of architecture, practice and training setting as determined by the skill type to be learned) using a Subject Matter Matrix such as shown in  FIG. 5 . In  FIG. 5 , a subject matter score matrix  350  maps each selected subject matter attribute(s)  352  that were selected with a corresponding value  355  or  357  depending upon the applicable Open-loop or Closed-loop nature of the selected skill type attribute  270 . That is, for each Subject Matter attribute selected from attributes  205  in  FIG. 3A , and as determined by the Open-closed loop nature of the skill, LEFsys system  12  assigns the corresponding qualitative value found in the Subject Matter Matrix  300 . For example, if the first three (3) Subject Matter attributes  220 ,  221  and  222  were selected and the Subject Matter is Closed loop type or Open loop the Notice qualitative value is 5 or 0, the Announcement qualitative value is 5 or 0, and the Procedures qualitative value is 5 or 0, etc. If the other subject matter attributes  223 ,  224  were selected and the Subject matter is Closed-loop type or Open-loop type, the Problem solving qualitative value is 0 or 5 and the Applied into different contexts qualitative value 0 or 5. In one embodiment, the system uses a prompted entry of a value to identify the correct divisor by indicating the amount of Subject Matter attributes not selected, i.e., not applicable (N/A) as indicated as an entry in field  278 . From this Subject Matter Matrix mapping, an Open Skill type or a Closed Skill Type Subject matter qualitative sub-total value  281  is computed from the matrix. 
         [0066]    In one example, given a Closed-loop skill type selection each selected Subject Matter attributes  220 ,  221  and  222  map to a value of 5, generating an Open-closed Skill Type Subject matter qualitative sub-total value  281  of 15, for example. Alternatively, in another example, given an Open-loop skill type selection each selected Subject Matter attribute  220 ,  221  and  222  map to a value of 0, generating a Open-closed Skill Type Subject matter subtotal  281  of 10, for example. 
         [0067]    An Open-closed Skill Type Subject matter average  282  is then calculated as a value in a variable (J) according to: 
         [0000]      Open-closed Skill Type Subject Matter Average:  J=t/n    
         [0000]    where n=the number of Subject Matter attributes selected, or alternatively, the number of non-N/A in Field and t=Open-closed Skill Type Subject matter qualitative sub-total value  281 . 
         [0068]    Thus, as depicted in the example presented spreadsheet portion in  FIG. 3C , the modeler further computes and stores in a register or computer memory location an Open-closed Skill Type Subject Matter Average value  282  (J)=15/3=5. 
         [0069]    In a further embodiment, the LEFSys modeler  12  weighs the Subject Matter Average  282  (J) by a factor reflecting a percent proportional influence of the overall training outcome. Thus the Open-closed Skill Type Subject Matter Average  282  is adjusted to generate an Adjusted Open-closed Skill Type Subject matter average value  283  as follows: 
         [0000]      Adjusted Open-closed Skill Type Subject Matter Average= J *(weight) 
         [0070]    In one embodiment, this weight value is 10% or 0.1. But it may be another value within a range between values 0.05 to 0.15. Thus, in the embodiment depicted, the Adjusted Open-closed Skill Type Subject Matter Average=J*(0.1)=5*(0.1)=0.5. 
         [0071]    After mapping using the subject matter score matrix  350  of  FIG. 5 , using calculations performed by a processor and presented via the spreadsheet program portion of  FIG. 3C , a processing device functioning to perform the modeler  12  computes an Adjusted Design Average sub-total value for the Content and Delivery component in a variable (K). The Adjusted Design Average sub-total value is shown in the display of the spreadsheet implementation of  FIG. 3B  in a sub-total field  280 . That is, the final step in arriving at the Content and Delivery Fitness value  280  is a weighting of the Content and Delivery Subtotals performed by the LEFSys modeler  12  as follows: 
         [0000]      Content and Delivery Fitness:  K =[( E )+( F 1:  F 5)+ G +( H 1:  H 3)+ I ]( J *(weight)) 
         [0000]    where E represents the Architecture Framework subtotal, F1:F5 represents the Subject Matter subtotal: F1+F2+F3+F4+F5; G represents the Learner Knowledge subtotal; H1:H3 represents the Practice subtotal: H1+H2+H3; I represents the Training Setting subtotal; and J*(weight) represents the Adjusted Open-closed Skill Type Subject Matter Average. The value displayed in field  280  is alternately computed as a product of the Content and Delivery component sub-total value  275  and the Adjusted Open-closed Skill Type Subject Matter Average  283  computed by processing depicted in  FIG. 3C . 
         [0072]    In a further embodiment, as shown in the example spread sheet program implementation  200 B of  FIG. 3B , the Skill Type processing sub-component  34  receives a further input attribute  272  characterizing the nature of the skill as being motor or cognitive. Motor tasks include physical tasks (acts of physically performing an action), e.g., that require muscular strength, endurance, coordination. Cognitive (skill) tasks include tasks that involve, e.g., perceptual input, mental operation, problem solving, and decision making. Thus, there is entered into the system  10  a selection  272  of whether the training is for a motor or a cognitive skill. As will be explained in greater detail herein below, the selection of a motor or a cognitive Skill Type impacts a fitness score for the Post-Training component Fitness for Environment and Fitness for Application attributes,  36 ,  38  as depicted by the Skill Type impact line  44  in  FIG. 1 . 
         [0073]      FIG. 4A  depicts an example spreadsheet implementation  300  of the predictor model  12  that is associated with the LEFSys model Post Training processing component  35  that evaluates what happens after training occurs in forecasting the actual outcome. The model display implementation  300  is provided via a display interface  438  of computer system  400 . As shown in  FIG. 4A , from manual input provided by a user (e.g., a learner or manager) via the displayed spreadsheet interface, or via automated (computer system) input, the LEFSys model Post Training processing component  15  receives two indicators: a Fitness for Environment  36  and a Fitness for Application  38 . Both of these indicators incorporate motor or cognitive skill considerations into determining fitness of the training, given the environment and the application. The Fitness for Environment  36  receive variables or indicators  310  and the Fitness for Application  38  receive indicator inputs  320 . In one embodiment, each of the attribute variables or indicators  310 ,  320  are input via an individual drop down selection menus associated with each particular indicator. Generally, the model  12  assigns each entered low, medium or high indicator input a corresponding numeric value, e.g., 1, 3 or 5. Additionally, the model  12  may assign each entered Yes or No indicator input a corresponding numeric value, e.g., 3 or 0. In one instance, an entered No indicator input may assigned a numeric value of −3. These values are depicted in a corresponding spreadsheet value field  319  associated with each corresponding input numeric and each value is stored in a memory of computer system  400  for processing according to an applied function. 
         [0074]    In one embodiment, as will be described in greater detail herein, the Fitness for Environment indicator inputs  310  is a function of a Fitness for Motor-Cognitive Environment Matrix  375  such as shown in  FIG. 6 . First, a user (or automated technology) inputs the following values for attributes which the LEFSys modeler  12  converts into a numeric equivalent and then totals. 
         [0075]    Referring back to  FIG. 4A , a first Fitness for Environment attribute input  311  includes a value (e.g., none, low, medium or high) entered into the system  10  that characterizes a degree to which resources and support are in place to enable transfer of learning into on-the-job performance. This entered low, medium or high value input is assigned a corresponding quantitative value 1, 3 or 5 respectively and is stored by the system as a value in a variable L1. In one embodiment, the “none” value input is assigned a corresponding quantitative value of −10. 
         [0076]    A second Fitness for Environment attribute input  312  is a value (e.g., Yes, No) entered into the system  10  that indicates whether there is a management plan to assess inhibitors to application of the learning. If a “yes” is selected for this Fitness for Environment attribute, the modeler  12  assigns a corresponding number value, e.g., +3 and is stored by the system as a value in a variable L2. If a “no” is selected for this Fitness for Environment attribute, the modeler  12  assigns a corresponding number value of, e.g., −3, and is stored by the system as a value in a variable L2. 
         [0077]    A third Fitness for Environment attribute input  313  is a value (e.g., low, medium or high) entered into the system  10  that indicates a willingness of management to execute a mitigation plan to application inhibitors. This entered low, medium or high value input is assigned a corresponding quantitative value 1, 3 or 5 respectively and is stored by the system as a value in a variable L3. 
         [0078]    A fourth Fitness for Environment attribute input  314  is a value (e.g., Yes, No) entered into the system  10  that indicates whether the trainee has a plan to assess any obstacles to perform the learned skill. If a “yes” is selected for this Fitness for Environment attribute, the modeler  12  assigns a corresponding quantitative value, e.g., +3 and is stored by the system as a value L4. If a “no” is selected for this Fitness for Environment attribute, the modeler  12  assigns a corresponding number value of, e.g., 0, and is stored by the system as a value in a variable L4. 
         [0079]    A fifth Fitness for Environment attribute input is a value (e.g., Yes, No) entered into the system  10  that indicates whether the trainee has the ability to bring forward and execute risk mitigation if there are obstacles to performing the learned skill. If a “yes” is selected for this Fitness for Environment attribute, the modeler  12  assigns a corresponding number value, e.g., +3 and is stored by the system as a value L5. If a “no” is selected for this Fitness for Environment attribute, the modeler  12  assigns a corresponding quantitative value of, e.g., 0, and is stored by the system as a value in a variable L5. 
         [0080]    After receiving (via a manual or automated fashion) input values for the Fitness for Environment processing sub-component indicators  310 , the LEFSys modeler  12  further computes and stores in a register or computer memory location an overall Fitness for Environment indicator sub-total value  329 . In one embodiment, this Post Training Fitness for Environment indicator sub-total value  329  is computed by a processing device according to: 
         [0000]      Post Training Environment Subtotal:  L 1+ L 2+ L 3+ L 4+ L 5 
         [0081]    In one embodiment, from the prior input indicator  272  of  FIG. 3B  indicating whether the training is for a motor or a cognitive skill, the LEFSys modeler  12  creates an environment fitness score  339  using a Motor-Cognitive Environment Matrix  375  such as shown in  FIG. 6 . In  FIG. 6 , the Motor-Cognitive Environment Matrix  375  maps the Post Training Environment Subtotal value  329  into the fitness score  339  depending upon the applicable skill being a cognitive skill type  405  or a motor skill type  407 . The LEFSys modeler determines the Fitness for Environment score  339  by referencing the corresponding value in the Fitness for Motor-Cognitive Environment Matrix  375 . For example, if the Post Training Fitness for Environment indicator sub-total value  329  is a value between −10 to 3.4, then the mapped environment fitness score value is 1 or 0 depending if the skill type is motor  407  or cognitive  405  respectively. Otherwise, if the Post Training Fitness for Environment indicator sub-total value  329  is a value between 3.5 to 19, then the mapped environment fitness score value is 3 or 5 depending if the skill type is motor  407  or cognitive  405 , respectively. As shown in the example LEFSys spreadsheet implementation portion of  FIG. 4B , a Post Training Environment Subtotal value  329  of 11 maps into a fitness score  339  of a high value 5 or a low value 3. The LEFSys modeler  12  then stores this environment fitness value  339  as a value in a variable, e.g., M. 
         [0082]    Referring back to the LEFSys modeler implementation depicted via an example display spreadsheet interface  300  of  FIG. 4A , the computer system  375  further receives a Fitness for Application  38  indicator variables  320 . The Fitness for Application indicator inputs  38  consider how often and how wide-ranging the application of the skill under training is expected to be once training is complete. This indicator also incorporates motor or cognitive skill considerations into determining fitness of the training, given the application. Thus, in one embodiment, as will be described in greater detail herein, the Fitness for Application indicator inputs  320  are a function of a Fitness for Motor-Cognitive Environment Matrix  500  such as shown in  FIG. 7 . First, a user (or automated technology) inputs the following values for attributes which the LEFSys modeler  12  converts into a numeric equivalent and then totals. 
         [0083]    Referring back to  FIG. 4A , a first Fitness for Environment attribute input  311  includes a value (e.g., none, low, medium or high) entered into the system  10  that characterizes a degree to which resources and support are in place to enable transfer of learning into on-the-job performance. This entered low, medium or high value input is assigned a corresponding quantitative value 1, 3 or 5 respectively and is stored by the system as a value in a variable L1. In one embodiment, the “none” value input is assigned a corresponding quantitative value of −10. 
         [0084]    Referring back to  FIG. 4A , a first Fitness for Application attribute input  321  is a value (e.g., daily, weekly or monthly or greater than 6 months) selected for entry into the system  10  that indicates: a Maintenance, i.e., a frequency of which skill will be used. If “daily” is selected, LEFSys modeler  12  assigns a corresponding number value of 5, for example, and is stored by the system as a value in a variable N1. If “Weekly/Monthly” is selected, LEFSys modeler assigns a corresponding number value of 3, for example, and is stored by the system as a value in a variable N1. If “&gt;6 months” is selected, LEFSys modeler assigns a corresponding number value of (−10), for example, and is stored by the system as a value in a variable, e.g., N1. 
         [0085]    A second Fitness for Application attribute input  322  is a value (e.g., Yes, No) entered into the system  10  that indicates: a Generalization, i.e., whether skill usage be applied beyond the original training context. If a “yes” is selected for this Fitness for Application attribute, the modeler  12  assigns a corresponding number value, e.g., +5 and is stored by the system as a value in a variable N2. If a “no” is selected for this Fitness for Environment attribute, the modeler  12  assigns a corresponding number value of, e.g., 0, and is stored by the system as a value in a variable N2. 
         [0086]    A third Fitness for Application attribute input  323  is a value (e.g., Yes, No) entered into the system  10  that indicates: whether the training is included in the learner&#39;s development plan. If a “yes” is selected for this Fitness for Application attribute, the modeler  12  assigns a corresponding number value, e.g., +5 and is stored by the system as a value in a variable N3. If a “no” is selected for this Fitness for Environment attribute, the modeler  12  assigns a corresponding number value of, e.g., 0, and is stored by the system as a value in a variable N3. 
         [0087]    After receiving (via a manual or automated fashion) input values for the Fitness for Application processing sub-component indicators  320 , the LEFSys modeler  12  further computes and stores in a register or computer memory location an overall Fitness for Application indicator sub-total value  349 . In one embodiment, this Post Training Fitness for Application indicator sub-total value  349  is computed by a processing device according to: 
         [0000]      Application Subtotal:  N 1+ N 2+ N 3 
         [0088]    In one embodiment, from the prior input indicator  272  of  FIG. 3B  indicating whether the training is for a motor or a cognitive skill, the LEFSys modeler  12  creates an application fitness score  359  using a Motor-Cognitive Application Matrix  500  such as shown in  FIG. 7 . In  FIG. 7 , the Motor-Cognitive Application Matrix  375  maps the Post Training Environment Subtotal value  349  into the fitness score  359  depending upon the applicable skill being a cognitive skill type  405  or a motor skill type  407 . The LEFSys modeler determines the Fitness for Application score  359  by referencing the corresponding value in the Fitness for Motor-Cognitive Application Matrix  500 . For example, if the Post Training Fitness for Application indicator sub-total value  349  is a value between −10 to 2.4, then the mapped application fitness score value is 1 or 0 depending if the skill type is motor  407  or cognitive  405  respectively. Otherwise, if the Post Training Fitness for Application indicator sub-total value  349  is a value between 2.5 to 15, then the mapped environment fitness score value is 3 or 5 depending if the skill type is motor  407  or cognitive  405 , respectively. As shown in the example LEFSys spreadsheet implementation portion of  FIG. 4B , a Post Training Application Subtotal value  349  of 0 maps into a fitness score  339  of a high value 3 or a low value 0. The LEFSys modeler  12  then stores this environment fitness value  339  as a value in a variable, e.g., 0. 
         [0089]    Recognizing that the above components have a collective impact on skills transfer, the above inputs are used to generate a final Forecast Value  50  using the following formula: 
         [0000]      Forecast Value=(Motivation)+(Content and Delivery Fitness)+(Post Training) 
         [0000]    where Motivation=[A1::A,3+B1::B8+C1:C4+D1:D5] *(0.40); Content and Delivery=[(E1:E4)+(F1:F5)+G+(H1:H3)+I](J*weight) and Post Training=M+O. 
         [0090]    Finally, LEFSys modeler  12  uses the Forecast Value  50  to convert the quantitative information back into actionable output, e.g., recommending to admit or deny a learner entry into a training program, which recommendation may be communicated to a manager or like decision maker. 
         [0091]    In one embodiment, the LEFSys modeler  12  compares the computed forecast value  50  against a threshold value, e.g., 75, where a forecast value of 75 or greater indicates that the training is a worthwhile endeavor. Forecast value scores less than 75 forecasts that the planned learning will not transfer into on-the-job use. In one embodiment, the threshold value is dependent upon the skill type to be applied in executing a job role. 
         [0092]    Then, a hardware processor device of the computer system makes the forecast data and/or recommendation  51  immediately available to a user device, e.g., a display, such as by generating signals for receipt by a user display device for access by a user, e.g., a manager. For example, the forecast value  50  is generated in the spreadsheet implementation portion  300  on a user display device  438  shown in  FIG. 1 . Alternatively, the computer device  400  may automatically generate signals in the form of an e-mail message which may be communicated to a remote user via a network. 
         [0093]    Thus, based on a result of said comparing, a user, such as a manager, may respond to the value for recommending an action to admit a learner entry or deny a learner entry to a training program. 
         [0094]    Thus, LEFSys modeler  12  bridges the gap of managers&#39; lack of expertise in training design. 
         [0095]    The Advantages of the LEFSys  10  over a manager developing a detailed understanding of training and learning are multi-fold: The LEFSys  10  returns results quicker in that once variables are input, the results are immediate. Enabling the decision support aspects of LEFsys involve newer technologies (e.g., cloud and open technology) that allow greater access to information and advanced analytics. The LEFSys  10  uses collaborative and shared services technologies to remove traditional boundaries managers face in responding to learning requests as well as supporting a continuum of education. 
         [0096]    One example application of the LEFSys modeler  12  implemented in computer system  400  is a scenario relating to training on an automatic external defibrillator for a customer software call center support technician. In this scenario, a customer support technician is asked to take training on an automated external defibrillator. The technician and manager fully understand that the course will be on operating a specific model defibrillator. The technician has read up on the device. The technician&#39;s long term professional objectives are documented and include the role as safety lead. Given these motivation factors, this motivation processing component will be positively correlated. With respect to Content and Delivery: it has been selected that the given training is coaching and simulation. The act of operating the mechanism is procedure based and the learner has no experience on the subject. Operating the mechanism is a three-step operation and is motor skills based. With respect to the Post Training Environment: The employee works at night and there is one other person in the building at that time. Management has agreed to move the technician in to a day time slot a time with 100 other people are in the building. It is likely the skill will never be used. In this first example scenario, the LEFSys model result may be a forecast value of: 86. This training, though likely never to be used, will result in skills transfer when needed, due to motivation of learner, manager and skills type. 
         [0097]    A second example application of the LEFSys modeler  12  implemented in computer system  400  is a scenario relating to training on a process improvement, black belt, Lean Six Sigma methodology for a business analyst. In this scenario, with respect to motivation: a poorly rated transactional business analyst is being asked by management to take the training because funding is available for it this year. The manager has no plans to rearrange the learners schedule and is unsure of using this methodology in the day to day operation in the department. With respect to content and delivery: the training is self paced and uses manufacturing process as examples. The methodology involves 13 tools and these tools are simulated once, each, during the training. The learner has never heard of Lean Six Sigma before and as such has no experience using these tools. The skills involve, cognitive, open loop skills. With respect to Post Training: there is no plans for the learner to apply these skills to a current job role. The training used manufacturing environment examples of a complex process where the learner works in a transactional setting it will be difficult to apply to another context. In this second example scenario, the LEFSys model result may be a forecast value of: 8.93. This training, covers a complex process using material not fitted to the scenario within which is likely to be used by an unmotivated learner, it will not result in skills transfer. 
         [0098]    A third example application of the LEFSys modeler  12  implemented in computer system  400  is a scenario relating to a learner must take training on fraud prevention. With respect to motivation: the learner is low motivated, and cannot opt out of the training. The company fully supports this training and has integrated this message into all aspects of their internal business. The learner does not believe they can impact outcome and is not goal oriented. With respect to Content and Delivery: the training covers the regulatory nature of the law and is a skill that can be applied to different context. The learner has been trained on this topic annually. The opportunity to commit fraud occurs daily. Fraud prevention is an open loop, cognitive task. With respect to Post Training: the opportunity to use this skill exists, daily. The skill can be applied to various scenarios and is part of the learner&#39;s performance plan. In this third example scenario, the LEFSys model result may be a forecast value of: 70.2. This training involves a low motivated, ineffective learner. Even though the cognitive skill could be applied daily in this environment, the learner likely will not transfer this knowledge on to the job. Since the training is not justified, the manager should be aware that the learner will need additional coaching for this needed skill. 
         [0099]    Referring to  FIG. 8  illustrates one embodiment of an exemplary hardware configuration of a computing system  400  programmed to perform the method steps described herein with respect to  FIG. 8 . The hardware configuration preferably has at least one processor or central processing unit (CPU)  411 . The CPUs  411  are interconnected via a system bus  412  to a random access memory (RAM)  414 , read-only memory (ROM)  416 , input/output (I/O) adapter  418  (for connecting peripheral devices such as disk units  421  and tape drives  440  to the bus  412 ), user interface adapter  422  (for connecting a keyboard  424 , mouse  426 , speaker  428 , microphone  432 , and/or other user interface device to the bus  412 ), a communication adapter  434  for connecting the system  400  to a data processing network, the Internet, an Intranet, a local area network (LAN), etc., and a display adapter  436  for connecting the bus  412  to a display device  438  and/or printer  439  (e.g., a digital printer of the like). 
         [0100]    The present invention may be a system, a method, and/or a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
         [0101]    The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
         [0102]    Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
         [0103]    Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
         [0104]    Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
         [0105]    The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks. 
         [0106]    The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. 
         [0107]    The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.