Technical skill training effect three-level evaluation quantitative method based on data flow

Abstract

The invention discloses a technical skill training effect three-level evaluation quantitative method based on data streams, and belongs to the technical field of training effect evaluation. Three-level evaluation is carried out for a training project, and a mapping path method of'ability quality item-key behavior link-data field 'is designed by constructing a key behavior link list; quantitative calculation and presentation of three-level evaluation of training items are innovatively realized by utilizing real business data flow matched with the data flow, the requirements of operability, convenience and scientificity are effectively balanced, the data does not need to be manually collected, the data is fair and objective, the workload of data evaluation analysis is reduced, and the method and the system have good application prospects in the face of different training items. According to the method, simple, convenient and scientific quantitative evaluation can be carried out.

Description

Technical Field

[0001] The present invention belongs to the technical field of training effect evaluation, and in particular relates to a three-level quantitative evaluation method for technical skill training effect based on data stream. Background Art

[0002] Training effectiveness evaluation is a process that uses scientific theories, methods and procedures to collect data from training programs, link it with the needs and goals of the entire organization, and determine the value and quality of training programs. It mainly includes comprehensive evaluations of training organization management, training services, training faculty capabilities and levels, trainee team knowledge and skill mastery, trainee behavior changes, and impact on employee performance. At present, the classic technical method at home and abroad is the Kirkpatrick (four-level) evaluation technical method. In addition, there are also representative technical methods such as the CIPP model, CIRO model, and Phillips return on investment model.

[0003] However, the prior art has the following problems: 1. The current technical methods for evaluating training effectiveness are complex and mainly based on qualitative evaluation; 2. Relying on repeated and inefficient manual data collection, it is difficult to use fair and objective data, and the workload of data evaluation and analysis is huge; 3. Training programs vary greatly, making it difficult to conduct simple and scientific quantitative evaluation. Summary of the invention

[0004] In order to solve the problems raised in the above background technology, the present invention provides a three-level quantitative evaluation method for technical skill training effects based on data flow, so as to solve the problems that the existing technology is mainly based on qualitative evaluation, relies on repeated and inefficient manual data collection, is difficult to use fair and objective data, and has a large workload for data evaluation and analysis, and there are large differences in training projects, making it difficult to conduct simple and scientific quantitative evaluation.

[0005] To achieve the above object, the present invention provides the following technical solutions: A three-level quantitative method for evaluating the effect of technical skill training based on data flow, comprising the following steps: S1: Obtain job descriptions for technical skill positions within the enterprise or unit; S2: Draw a flowchart of the business work items of technical skill positions through the job description, and obtain a list of key behavioral links of technical skill positions through the flowchart; S3: Construct a competency model for technical skill positions, and pair the competency items in the competency model with each key behavior link in the key behavior link list. Each key behavior link can correspond to n competency items, where n ≥ 1. S4: Assign "importance" to all the competence and quality items of each key behavior link, and obtain a list of weight coefficients of competence and quality items; S5: Extract all data fields related to technical skill positions from the business digital platform within the enterprise or unit; S6: For each key behavior link of technical skill positions, the data fields are screened and matched. The screened data fields are associated with the number of behaviors, behavior efficiency, and behavior quality in the key behavior link; S7: Set up a three-level evaluation data indicator library with key behavior links as the center, and ability and quality items and data fields linked to the center; S8: Select corresponding training courses according to the weight coefficient list of key behavior links to train the trainees. After the training is completed, the change in the content of the data fields screened out in the key behavior links before and after the training is used as the basis for ranking the trainees; S9: Based on the three-level evaluation data indicator library, the ranking is displayed according to the ranking criteria to complete the three-level quantitative evaluation of the technical skills training effect.

[0006] Preferably, in S3, the competency and quality items are category names of personal conditions and behavioral characteristics that can directly affect work performance, including general competencies, transferable competencies, and unique competencies.

[0007] Preferably, in S6, the content of the data field associated with the number of behaviors includes the number of operations, the content of the data field associated with the behavior efficiency includes the operation time, the relative operation time and the piece quantity, and the content of the data field associated with the behavior quality includes the number of commendations, the number of praises, the product qualification rate, the rework rate per unit time and the second maintenance time interval.

[0008] Preferably, in S6, when there is no target data field that can be directly paired with the key behavior link, the target data field is obtained by performing calculation transformation on other data associated with the target data field. When the target data field cannot be obtained through calculation transformation, the target data field is obtained by reversely adjusting the business key behavior link through integration optimization. When the target data field still cannot be obtained through reversely adjusting the business key behavior link through integration optimization, it is obtained through an evaluation questionnaire.

[0009] Preferably, the evaluation questionnaire includes two parts, one part is the basic information of the evaluated object, and the other part uses a degree scale to conduct an evaluation survey related to the target data fields of the key behavioral links.

[0010] Preferably, in S8, the content of the data field before training is selected from the content mean or evaluation value of the data field one month before the start of the training program, and the content of the data field after training is selected from the content mean or evaluation value of the data field in the third and sixth months after the start of the training program.

[0011] Preferably, the ranking in S9 is displayed on a large data screen, and the displayed content includes the overall change range of the training object, the change details and the cumulative ranking.

[0012] Compared with the prior art, the present invention has the following beneficial effects: This application conducts a three-level evaluation of training projects. By constructing a list of key behavioral links, a mapping path method of "capability and quality items-key behavioral links-data fields" is designed. By using the matching real business data flow, the quantitative calculation and presentation of the three-level evaluation of training projects is innovatively realized, effectively balancing the requirements of "operability, convenience, and scientificity". The data of this application does not need to be collected manually, and the data is fair and objective, reducing the workload of data evaluation and analysis. In addition, this application can conduct simple and scientific quantitative evaluation for different training projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A flowchart of key behavior steps for matching data fields; Figure 2 A flowchart for compiling a checklist of key behavioral steps. DETAILED DESCRIPTION

[0014] In order to facilitate those skilled in the art to understand the technical content of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific examples. It should be understood that the specific examples described herein are only used to explain the present invention and are not used to limit the present invention.

[0015] Example 1 A three-level quantitative evaluation method for the effectiveness of technical skills training based on data flow, such as Figure 1 , Figure 2 As shown, the following steps are included: S1: Obtain job descriptions for technical skill positions within the enterprise or unit, including: S1.1: Obtain job descriptions for technical skill positions; S1.2: Confirm the content of the obtained job description with senior personnel of the position (no less than 2 people), find the differences between the actual work and the job description, and write a text description of the differences; S1.3: Confirm the extracted job description and the textual description of the differences with the direct supervisor of the position, and revise the job description to form a true job description according to the direct supervisor's opinion; S2: Draw a flowchart of the business work items of technical skill positions through the job description, and obtain a list of key behavioral links of technical skill positions through the flowchart, including: S2.1: Divide the first-level work modules according to job responsibilities. The first-level work modules should be independent of each other and have weak correlations. The number of first-level work modules is generally controlled at 5-8. For particularly complex positions, the number of modules can be increased by 2-3; S2.2: Each primary work module is further disassembled into secondary submodules. The disassembly can be done in the order of the workflow or in parallel. Each primary work module generally contains 5-10 secondary work submodules. S2.3: Draw a specific business work flow chart for the secondary work submodule; S2.4: Organize relevant personnel and direct supervisors to review, revise and confirm the above work modules and specific business flow charts; S2.5: Based on the specific business process diagram, invite senior personnel or direct supervisors to mark and correct the key behavior links in the process, and finally form a list of key behavior links; S3: Construct a competency model for technical skill positions, and pair the competency items in the competency model with each key behavior link in the key behavior link list. Each key behavior link can correspond to n competency items, n ≥ 1. The competency items are the category names of personal conditions and behavioral characteristics that can directly affect work performance, including general competencies, transferable competencies, and unique competencies. S4: Organize senior employees or direct superiors of the position to assign "importance" to all competency and quality items belonging to each key behavior link, and obtain a list of weight coefficients for competency and quality items; S5: Extract all data fields related to technical skill positions from the business digital platform within the enterprise or unit; S6: For each key behavior link of technical skill positions, the data fields are screened and matched. The screened data fields are associated with the number of behaviors, behavior efficiency and behavior quality in the key behavior links. The content of the data fields associated with the number of behaviors includes the number of operations, the content of the data fields associated with the behavior efficiency includes the operation time, the relative operation time and the number of pieces, and the content of the data fields associated with the behavior quality includes the number of commendations, the number of praises, the product qualification rate, the unit time return rate and the second maintenance time interval; When there is no target data field that can be directly paired with the key behavior link, the target data field is obtained by calculating and transforming other data associated with the target data field, for example: When you need fields related to "Operation Time", if there is no direct "Operation Time" field, but there are work start record time (A hour B minute), work end record time (C hour D minute), etc., you can use the calculation formula: Operation Time = (C*60+D)-(A*60+B). Here, because the business data generated by different businesses have different names, there is no fixed formula.

[0016] When the target data field cannot be obtained through calculation and conversion, the target data field is obtained by reversely adjusting the business key behavior links through integration and optimization. The basic technical steps of reverse adjustment are: Step 1: Clarify the existing data (or converted) fields; Step 2: Take the unadapted business key behavior links as the main line, sort out the list of all existing business key behavior links, and find N related links among them; Step 3: Organize senior employees, supervisors, etc. in this position to conduct experimental configuration for the (N+1) links in step 2 according to the fields prompted in step 1; Step 4: According to the experimental configuration results, determine the adjustment of (N+1) links to M links, and ensure that the M links have successfully adapted the relevant data (or converted) fields.

[0017] When the target data field cannot be obtained through reverse adjustment of the key business behavior links through integration optimization, the evaluation questionnaire is used to obtain the target data field. The evaluation questionnaire consists of two parts: one part is the basic information of the evaluated object, such as name, etc., and the other part uses a degree scale (1-no change, 2-some change, 3-some change, 4-some change, 5-very obvious change) to conduct an evaluation survey related to the target data field of the key behavior link; S7: Set up a three-level evaluation data indicator library with key behavior links as the center, and ability and quality items and data fields linked to the center; S8: According to the weight coefficient list of key behavior links, corresponding training courses are selected to train the trainees. After the training is completed, the change in the content of the data field screened out in the key behavior links before and after the training is used as the basis for the ranking of the trainees. The content of the data field before training is the mean value or evaluation value of the data field one month before the start of the training project, and the content of the data field after training is the mean value or evaluation value of the data field in the third and sixth months after the start of the training project, that is: post-training value = (relevant mean value of the third month after training + relevant mean value of the sixth month after training) / 2; S9: Based on the three-level evaluation data indicator library, the ranking is displayed according to the ranking criteria, and the three-level quantitative evaluation of the technical skill training effect is completed. The ranking is displayed on the data screen, and the display content includes the overall change range of the training objects, the change details and the cumulative ranking.

[0018] In this embodiment, the application conducts a three-level evaluation for training projects. By constructing a list of key behavioral links, a mapping path method of "capability and quality items-key behavioral links-data fields" is designed, and the real business data flow that matches it is used to innovatively realize the quantitative calculation and presentation of the three-level evaluation of training projects, effectively balancing the requirements of "operability, convenience, and scientificity". The data of this application does not need to be collected manually, and the data is fair and objective, which reduces the workload of data evaluation and analysis. In addition, for different training projects, this application can perform simple and scientific quantitative evaluation.

Claims

1. A three-level quantitative evaluation method for technical skill training effect based on data flow, characterized in that: The following steps are involved: S1: Obtain job descriptions for technical skill positions within the enterprise or unit; S2: Draw a flowchart of the business work items of technical skill positions through the job description, and obtain a list of key behavioral links of technical skill positions through the flowchart; S3: Construct a competency model for technical skill positions, and pair the competency items in the competency model with each key behavior link in the key behavior link list. Each key behavior link can correspond to n competency items, where n ≥ 1. S4: Assign "importance" weights to all competence and quality items belonging to each key behavior link, and obtain a list of weight coefficients for competence and quality items; S5: Extract all data fields related to technical skill positions from the business digital platform within the enterprise or unit; S6: For each key behavior link of technical skill positions, the data fields are screened and matched. The screened data fields are associated with the number of behaviors, behavior efficiency, and behavior quality in the key behavior link; S7: Set up a three-level evaluation data indicator library with key behavior links as the center, and ability and quality items and data fields linked to the center; S8: Select corresponding training courses according to the weight coefficient list of key behavior links to train the trainees. After the training is completed, the change in the content of the data fields screened out in the key behavior links before and after the training is used as the basis for ranking the trainees; S9: Based on the three-level evaluation data indicator library, the ranking is displayed according to the ranking criteria to complete the three-level quantitative evaluation of the technical skills training effect.

2. According to the three-level quantitative evaluation method of technical skill training effect based on data flow in claim 1, it is characterized in that: In S3, the competency and quality items are the category names for personal conditions and behavioral characteristics that can directly affect work performance, including general competencies, transferable competencies, and unique competencies.

3. According to the three-level quantitative evaluation method of technical skill training effect based on data flow in claim 1, it is characterized in that: In S6, the content of the data field associated with the number of behaviors includes the number of operations, the content of the data field associated with the efficiency of the behavior includes the operation time, the relative operation time and the piece quantity, and the content of the data field associated with the quality of the behavior includes the number of commendations, the number of praises, the product qualification rate, the return rate per unit time and the time interval between secondary maintenance.

4. According to claim 3, a three-level quantitative evaluation method for technical skill training effect based on data flow is characterized in that: In S6, when there is no target data field that can be directly paired with the key behavior link, the target data field is obtained by calculating and transforming other data associated with the target data field. When the target data field cannot be obtained through calculation and transformation, the target data field is obtained by integrating and optimizing the reverse adjustment of the business key behavior link. When the target data field still cannot be obtained through integrating and optimizing the reverse adjustment of the business key behavior link, it is obtained through an evaluation questionnaire.

5. According to claim 4, a three-level quantitative evaluation method for technical skill training effect based on data flow is characterized in that: The evaluation questionnaire consists of two parts: one part is the basic information of the evaluated object, and the other part uses a degree scale to conduct an evaluation survey related to the target data fields of the key behavioral links.

6. According to the three-level quantitative evaluation method of technical skill training effect based on data flow in claim 1, it is characterized in that: In S8, the content of the data field before training is selected from the content mean or evaluation value of the data field one month before the start of the training project, and the content of the data field after training is selected from the content mean or evaluation value of the data field at the third and sixth months after the start of the training project.

7. According to the data flow-based three-level quantitative evaluation method for technical skill training effects according to claim 1, it is characterized in that: The ranking in S9 is displayed on a large data screen, which includes the overall change of the training subjects, change details and cumulative ranking.

Images