CN108596461B - Intelligent system and method for training effect evaluation - Google Patents
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Abstract
The invention discloses an intelligent system for evaluating training effect, which comprises: an input/output unit; the training system comprises a storage unit and an analysis unit, wherein before the training is started, the input/output unit acquires first performance data and stores the first performance data in the storage unit, the analysis unit calculates potential capability promotion data based on the first performance data so as to match training courses, and the training courses comprise a plurality of skill training items; after the training is completed, the input/output unit acquires training effect data and second performance data and stores the training effect data and the second performance data in the storage unit; the analysis unit obtains performance improvement data based on the second performance data and the first performance data, and obtains a correlation coefficient between the performance improvement data and the training effect data according to big data analysis.
Description
Technical Field
The invention relates to the technical field of computing, in particular to a system and a method for evaluating learning effect by utilizing big data analysis technology.
Background
With the arrival of the strategic human resource management era, staff training and development play an important role in the aspects of improving staff skills and quality, enhancing enterprise core competitiveness and the like.
The existing on-duty training management system generally collects training requirements of enterprise employees of different departments at different levels, combines training arrangement and effect of the enterprise in the past year and targets of medium and short term development of the enterprise, classifies the requirements of each department according to different departments, and finally makes an annual training plan, a training form and training contents based on the requirements.
Enterprises generally desire to generate benefits through training, i.e., actually improve the performance of employees through training. Therefore, enterprises need to perform detailed evaluation on training courses according to actual requirements to determine the effectiveness of the courses. However, how to evaluate the effect and how to determine the relationship between training and actual performance of the staff is a difficult problem.
At present, no matter on-line learning or off-line classroom, no hook with the actual performance value of a student exists, and most people mainly carry out course allocation or course selection. In addition, the study effect is also checked by the study time and examination of the student. The disadvantage is that only the knowledge mastery can be detected, the skill assessment cannot be checked, namely the frequently-mentioned high score and low energy, and the relation between learning and performance cannot be determined, so that the intelligent learning system hooked with the performance cannot be formed in the form.
Disclosure of Invention
In view of the problems in the prior art, according to one aspect of the present invention, there is provided an intelligent system for training effectiveness evaluation, including:
an input/output unit; a storage unit and an analysis unit, wherein,
before the training is started, the input/output unit acquires first performance data and stores the first performance data in the storage unit, the analysis unit calculates potential capability improvement data based on the first performance data so as to match training courses, and the training courses comprise a plurality of skill training items;
after the training is completed, the input/output unit acquires training effect data and second performance data and stores the training effect data and the second performance data in the storage unit;
the analysis unit obtains performance improvement data based on the second performance data and the first performance data, and obtains a correlation coefficient between the performance improvement data and the training effect data according to big data analysis.
In one embodiment of the invention, the first performance data, the potential performance enhancement data, the training effectiveness data and the second performance data are data of the same student.
In an embodiment of the present invention, the potential capacity improvement data is data of percentage of capacity potential improvement of the trainee calculated by the analysis unit based on the first performance data.
In an embodiment of the invention, the training effect data is data of percentage of actual increase of each ability of the trainee after the training is completed.
In one embodiment of the present invention, the calculating, by the analyzing unit, the obtained ability potential promotion percentage data of the trainee based on the first performance data includes:
determining a standard deviation of a first performance data of the trainee;
locking to the confidence interval positive distribution range of sample data needing to be referred to;
obtaining reference data of the trainee;
determining performance-enhancing data for the reference data;
determining various actual capacity improvement percentage data corresponding to the performance improvement data of the reference data; and
and obtaining the potential promotion percentage data of each ability of the student.
According to another aspect of the present invention, there is provided a training effectiveness evaluation system including:
the pre-training data acquisition unit is used for acquiring first performance data of the trainee;
the intelligent matching unit is used for matching a training course based on the first performance data, and the training course comprises a plurality of skill training items;
the training effect data acquisition unit is used for acquiring percentage data of each skill improvement of the student self-evaluation;
the post-training data acquisition unit is used for acquiring second performance data of the trainee after the skill training so as to acquire performance improvement data of the trainee; and
and the analysis unit is used for obtaining a correlation coefficient between the performance improvement data and the percentage of each skill improvement according to big data analysis.
In one embodiment of the invention, said matching training courses based on said first performance data comprises:
calculating obtained potential promotion percentage data of each item of ability of the student based on the first performance data; and
and intelligently matching training courses based on the potential promotion percentage data of each ability of the student.
In one embodiment of the present invention, the calculating of the potential percentage increase data of the abilities of the trainee based on the first performance data includes:
determining a standard deviation of a first performance data of the trainee;
locking to the confidence interval positive distribution range of sample data needing to be referred to;
obtaining reference data of the trainee;
determining performance-enhancing data for the reference data;
determining various actual capacity improvement percentage data corresponding to the performance improvement data of the reference data; and
and obtaining the potential promotion percentage data of each ability of the student.
In one embodiment of the invention, the analysis unit infers the student's percentage potential increase data for each item of competency based on the following formula:
(i＝1…k)
wherein the content of the first and second substances,
za/2 is a positive distribution coefficient of an alpha confidence interval;
YNfirst performance data before training for N trainees;
σ is the Y standard deviation;
n is the sample volume;
x1iis the percentage value of the first item capacity improvement of the i-number trainee;
x2ithe percentage value of the second item capacity improvement of the i-number student.
In one embodiment of the invention, the intelligent matching unit optimizes the scheme of the matching course based on the correlation coefficient between the performance improvement data and the percentage of each skill improvement.
By the system and the method for evaluating the learning effect by utilizing the big data analysis technology, which skill training can form the influence on the performance or which skill training has no influence on the performance can be judged, so that the training efficiency can be improved, and the learning is more accurate.
Drawings
To further clarify the above and other advantages and features of embodiments of the present invention, a more particular description of embodiments of the invention will be rendered by reference to the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. In the drawings, the same or corresponding parts will be denoted by the same or similar reference numerals for clarity.
Fig. 1 shows an architecture diagram of a training system 100 according to an embodiment of the invention.
Fig. 2 shows a schematic structural diagram of a learning evaluation system 200 according to an embodiment of the present invention.
FIG. 3 shows a flow diagram 300 of a learning evaluation process according to one embodiment of the invention.
Detailed Description
In the following description, the invention is described with reference to various embodiments. One skilled in the relevant art will recognize, however, that the embodiments may be practiced without one or more of the specific details, or with other alternative and/or additional methods, materials, or components. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring aspects of embodiments of the invention. Similarly, for purposes of explanation, specific numbers, materials and configurations are set forth in order to provide a thorough understanding of the embodiments of the invention. However, the invention may be practiced without specific details. Further, it should be understood that the embodiments shown in the figures are illustrative representations and are not necessarily drawn to scale.
Reference in the specification to "one embodiment" or "the embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment.
The training aims at increasing the performance, the training is formed by training of N skills, and based on big data analysis, the embodiment of the invention can calculate the percentage of the N skills to be improved, so that corresponding skill improvement is carried out on the basis of the percentage, and the performance increase is driven. Currently, in the market, a learning scheme of a user is provided based on subjective judgment, and a learning plan based on big data analysis and artificial intelligence is not provided. The following is a detailed description of a specific implementation of the embodiments of the present invention.
Fig. 1 shows an architecture diagram of a training system 100 according to an embodiment of the invention. As shown in fig. 1, the training system 100 may include at least a trainee terminal 110 and a learning evaluation system 120. The student terminal 110 may be connected to the learning assessment system 120 through a wired or wireless connection, such as the internet, a corporate lan, or the like.
The learning evaluation system 120 may include an input-output unit 121, a storage unit 122, and an analysis unit 123. The learning assessment system 120 may also communicate with the enterprise database 130 to obtain information from the enterprise database 130, including performance information for the trainee, etc.
Before the skill training, the trainee can input the self-evaluation values of each skill of the trainee through the trainee terminal 110. After the skill training is completed, the trainee self-evaluates the percentage data of each skill improvement and inputs the percentage data into the system through the trainee terminal 110. After the skill training is completed, the trainee can also re-enter or import the actually achieved performance values through the trainee terminal 110.
Before the skills are trained, the learning evaluation system 120 may obtain the actual performance data of the trainee from the enterprise database and store the actual performance data in the storage unit 122, and the analysis unit 123 matches training courses based on the current actual performance data, wherein the courses contain N skills for training. After the skill training is completed, the learning evaluation system 120 obtains percentage data of each skill improvement of the student self-evaluation through the input/output unit 121. The learning evaluation system 120 acquires actual performance data after the skill training is completed from the enterprise database 130 or the student terminal 110 through the input/output unit 121, and compares the actual performance data with performance data before the skill training to obtain performance improvement data of the student.
In the aspect of data acquisition of the trainees for mastering the skills, the embodiment of the invention takes the trainees to evaluate the skill improvement data after the trainees train themselves as the basis, and the trainees improve the skills most intuitively and more directly compared with other external evaluations, so the embodiment of the invention adopts the progress data after the trainees train the trainees self-evaluate as the data.
The analysis unit 123 finds the relationship between the "performance improvement data" and the "percentage improvement of each capability" based on the big data analysis. When a new student starts training, the analysis unit 123 can deduce the ability growth space, i.e. the ability improvement percentage, of the student as long as "actual performance data before training" is input, and after finding the growth space, a training plan can be configured reasonably according to the data of the growth space.
The specific configuration of the learning evaluation system is described in detail below with reference to fig. 2.
Fig. 2 shows a schematic structural diagram of a learning evaluation system 200 according to an embodiment of the present invention. As shown in fig. 2, the learning evaluation system 200 may specifically include a pre-training data collection unit 210, an intelligent matching unit 220, a self-evaluation data collection unit 230, a post-training data collection unit 240, and an analysis unit 250.
The pre-training data collection unit 210 is used for acquiring actual performance data of the trainee before training. Specifically, the pre-training data collection unit 210 may be connected to the enterprise database through wired or wireless communication, and reads the actual performance data of the trainee from the enterprise database. Alternatively, the pre-training data collection unit 210 may be connected to the trainee terminal through wired or wireless communication, and receive actual performance data of the trainee from the trainee terminal. The wired or wireless communication techniques employed by the present invention may include local area networks LAN, wireless local area networks WLAN, wide area networks, the internet, etc. Those skilled in the art will appreciate that the manner in which the pre-training data collection unit 210 obtains the trainee's actual performance data is not limited to the two forms listed above.
The intelligent matching unit 220 is used for matching training courses based on actual performance data of trainees before training, wherein the courses comprise N skills for training.
The self-evaluation data collection unit 230 is used for acquiring percentage data of each skill improvement of the self-evaluation of the trainees. Similarly, the self-evaluation data collection unit 230 may receive percentage data of each skill improvement of the self-evaluation of the trainee from the trainee terminal in a wired or wireless communication manner.
The post-training data collection unit 240 is used to obtain actual performance data of the trainee after the skill training. Specifically, the post-training data collection unit 240 may be connected to the enterprise database via wired or wireless communication, and read updated actual performance data of the trainee from the enterprise database. Alternatively, the post-training data collection unit 240 may be connected to the trainee terminal through wired or wireless communication, and receive actual performance data input by the trainee from the trainee terminal. The post-training data acquisition unit 240 compares the performance data before and after the skill training to obtain the performance improvement data of the trainee.
The analysis unit 250 is used for obtaining the relationship between the "performance improvement data" and the "percentage improvement of each capability" according to the big data analysis. When a new student starts training, the analysis unit 250 can deduce the ability growth space of the student, i.e. the ability improvement percentage, as long as "actual performance data before training" is input, and after finding the growth space, a training plan is configured reasonably according to the data of the growth space. Therefore, the analysis unit 250 may transmit the ability growth space of the trainee to the intelligent matching unit 220, and the intelligent matching unit 220 may configure the training program according to the data of the growth space.
In some embodiments of the present invention, the analysis unit 250 may also send the relationship between the "performance improvement data" and the "percentage improvement of individual capabilities" to the intelligent matching unit 220. The intelligent matching unit 220 optimizes the scheme of the intelligent matching lesson based on the relationship between the "performance improvement data" and the "percentage improvement of each capability".
In the whole learning process of the client, the learning evaluation system 200 acquires relevant data, which are "actual performance data before training", "skill improvement percentage after training", and "performance data achieved after training", respectively. When a new student enters "actual performance data before training", the learning evaluation system 200 intelligently determines the "percentage of capacity improvement" of the student through a big data analysis scheme.
The following description will be made with reference to specific sample data.
Description of the nouns:
the performance is as follows: performance is the describable work behavior and the measurable work result of an individual (group) in an organization within a specific time. For example, an individual completed 12 visits to a customer in 12 months.
Skill (ability): skills are the various abilities needed to accomplish performance, which is a 1-to-many relationship to skills. For example, the number of visits includes: time management capabilities, customer relationship capabilities, communication capabilities, and the like.
Table 1 is the sampled data, with data that did not improve in performance after training being ignored.
TABLE 1
Combining sample data, wherein the fifth king is a new student, and the performance data before training input by the fifth king is as follows: 9 times per month.
The first step is as follows: finding the standard deviation of the 'performance data before training' to be 2.08, then locking to the confidence interval positive distribution range (between 6.92 and 11.08) of the sample data needing to be referred to, and obtaining the reference data range of the king five to be Zhang three.
The second step is that: "the performance data is reached after the actual combat of the project (task)" of zhang san has promoted 2 times respectively, has corresponded to their promotion percentage of ability again 2 times respectively, so promote the relation: the performance improvement of Zhang III for 2 times is due to the improvement of communication capacity by 30% and the improvement of relationship capacity of customers by 20%. Therefore, the performance improvement space of king five is 2, and the capacity improvement spaces are respectively: communication capacity ↓ [ 30%, client relation capacity [ × [ 20% ].
The above embodiment example uses two existing sample data to infer the "percentage capacity boost" of the new learner, and when the amount of the existing sample data increases, the accuracy of the "percentage capacity boost" inferred by the above method linearly increases.
When the existing data is a large data sample, the "percent capacity boost" is analyzed using the following equation [1 ]:
wherein the content of the first and second substances,
za/2 is a positive distribution coefficient of an alpha confidence interval;
YNactual performance data before training for N trainees;
σ is the Y standard deviation;
n is the sample volume;
x1ipercentage data for communication capacity improvement of the i-number student;
x2ipercentage data for the increase in client relationship ability for student i.
FIG. 3 shows a flow diagram 300 of a learning evaluation process according to one embodiment of the invention. The process disclosed in fig. 3 may be implemented by the learning evaluation system shown in fig. 1 or fig. 2, or may be implemented by other suitable computer systems, servers, etc.
First, at step 310, actual performance data of the trainee before skill training is acquired. Specifically, the actual performance of the trainee before skill training can be obtained from an enterprise database or through a self-evaluation mode of the trainee on each skill.
At step 320, training courses are matched based on the trainee's actual performance data, the courses containing N skills of training. The trainees finish the skill training after obtaining the training course.
In step 330, percentage data of each skill improvement self-appraised by the trainee is obtained.
At step 340, actual performance data of the trainee after skill training is acquired. Specifically, the actual performance data of the trainee after the skill training may be acquired from the business database, and the actual performance data input by the trainee may be received from the trainee terminal. And then comparing the performance data before and after the skill training to obtain the performance improvement data of the student.
In step 350, the relationship between the obtained performance improvement data and the performance improvement percentage is analyzed to deduce the capacity growth space of the new student, i.e. the capacity improvement percentage, and after finding the growth space, the training scheme is configured reasonably according to the data of the growth space.
Optionally, in some embodiments of the invention, the scheme of matching courses may be optimized based on the relationship between "performance improvement data" and "percentage of improvement of individual capacity".
Specifically, inferring the capacity growth space for the new student at step 350 may include:
receiving performance data of a new student before training;
determining a standard deviation of the pre-training performance data;
locking to the confidence interval positive distribution range of sample data needing to be referred to;
acquiring reference data of a new student;
determining performance-enhancing data for the reference data;
determining a percentage boost in capacity corresponding to the performance boost data; and
and obtaining a performance improvement space and a capacity improvement space of the new student.
The system and the method for vocational training disclosed above couple different skill training and performance data in training, and calculate each skill improvement percentage by using big data analysis, thereby driving performance increase. Aiming at the improvement of the skills of the students, self-improvement evaluation data of the students are collected, and the method is direct, objective and effective.
While various embodiments of the present invention have been described above, it should be understood that they have been presented by way of example only, and not limitation. It will be apparent to persons skilled in the relevant art that various combinations, modifications, and changes can be made thereto without departing from the spirit and scope of the invention. Thus, the breadth and scope of the present invention disclosed herein should not be limited by any of the above-described exemplary embodiments, but should be defined only in accordance with the following claims and their equivalents.
Claims (5)
1. An intelligent system for training effectiveness assessment, comprising:
an input/output unit; a storage unit and an analysis unit, wherein,
before the training is started, the input/output unit acquires first performance data and stores the first performance data in the storage unit, the analysis unit calculates potential capability improvement data based on the first performance data so as to match training courses, and the training courses comprise a plurality of skill training items;
after the training is completed, the input/output unit acquires training effect data and second performance data and stores the training effect data and the second performance data in the storage unit;
the analysis unit obtains performance improvement data based on the second performance data and the first performance data, obtains a correlation coefficient between the performance improvement data and the training effect data according to big data analysis,
wherein the potential capacity improvement data is the data of the percentage of potential improvement of each item of capacity of the trainee calculated and obtained by the analysis unit based on the first performance data,
the analysis unit calculating the obtained potential promotion percentage data of the abilities of the student based on the first performance data comprises the following steps: determining a standard deviation of a first performance data of the trainee; locking to the confidence interval positive distribution range of sample data needing to be referred to; obtaining reference data of the trainee; determining performance-enhancing data for the reference data; determining various actual capacity improvement percentage data corresponding to the performance improvement data of the reference data; and obtaining the potential promotion percentage data of each ability of the student,
wherein the analysis unit infers the student's percentage potential improvement in competency data based on the following formula:
wherein the content of the first and second substances,
za/2 is a positive distribution coefficient of an alpha confidence interval;
YNfirst performance data before training for N trainees;
σ is the Y standard deviation;
n is the sample volume;
x1iis the percentage value of the first item capacity improvement of the i-number trainee;
x2ithe percentage value of the second item capacity improvement of the i-number student.
2. The intelligent system for training effectiveness assessment according to claim 1, wherein the first performance data, the potential performance enhancement data, the training effectiveness data, and the second performance data are data of the same trainee.
3. The intelligent system for training effectiveness evaluation according to claim 1 or 2, wherein the training effectiveness data is data of actual percentage of increase of each ability of the trainee after the training is completed.
4. A training effectiveness evaluation system comprising:
the pre-training data acquisition unit is used for acquiring first performance data of the trainee;
the intelligent matching unit is used for matching a training course based on the first performance data, and the training course comprises a plurality of skill training items;
the training effect data acquisition unit is used for acquiring percentage data of each skill improvement of the student self-evaluation;
the post-training data acquisition unit is used for acquiring second performance data of the trainee after the skill training so as to acquire performance improvement data of the trainee; and
an analysis unit for obtaining a correlation coefficient between the performance improvement data and the percentage of each skill improvement according to big data analysis,
wherein said matching training lessons based on the first performance data comprises: calculating obtained potential promotion percentage data of each item of ability of the student based on the first performance data; and intelligently matching training courses based on the student's ability potential boost percentage data,
wherein the calculating of the potential percentage elevation data of the student's abilities based on the first performance data comprises: determining a standard deviation of a first performance data of the trainee; locking to the confidence interval positive distribution range of sample data needing to be referred to; obtaining reference data of the trainee; determining performance-enhancing data for the reference data; determining various actual capacity improvement percentage data corresponding to the performance improvement data of the reference data; and obtaining the potential promotion percentage data of each ability of the student,
wherein the analysis unit infers the student's percentage potential improvement in competency data based on the following formula:
wherein the content of the first and second substances,
za/2 is a positive distribution coefficient of an alpha confidence interval;
YNfirst performance data before training for N trainees;
σ is the Y standard deviation;
n is the sample volume;
x1iis the percentage value of the first item capacity improvement of the i-number trainee;
x2ithe percentage value of the second item capacity improvement of the i-number student.
5. The training effectiveness evaluation system according to claim 4, wherein the intelligent matching unit optimizes a scheme of matching courses based on a correlation coefficient between the performance improvement data and the percentages of skill improvement.
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