CN113741865B - Thinking assessment method based on programming machine data - Google Patents
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Abstract
The invention discloses a thinking force assessment method based on programming machine data, which is characterized by comprising the following steps of: the system comprises a robot, a programming machine and a background server; the robot is connected with the programming machine for communication, and the programming machine is connected with the background server for communication through a network; the specific thinking force evaluation steps are as follows: 1) a data acquisition step, 2) a data classification processing step, 3) a thinking force each dimension scoring step, and 4) a thinking force grade judging step; the invention provides a thinking force assessment method based on programming machine data, which is used for rapidly and conveniently processing corresponding data according to basic data of the programming machine, obtaining thinking force grades and giving capability enhancement directions.
Description
Technical Field
The invention relates to the technical field of evaluation based on the use condition of a programming machine, in particular to a thinking force evaluation method based on programming machine data.
Background
Educational evaluation refers to the process of making decisions about the value of education based on systematic, scientific, comprehensive collection, arrangement, processing, and analysis of educational information. The method aims to know the development condition of students in all aspects, objectively summarize the learning condition of the students and evaluate the teaching quality of teachers so as to promote education reform and improve the education and teaching quality. Educational evaluation is to let us better understand students, examine our class and teaching process.
However, at present, only indirect evaluation is performed through the results of various assessment tests, and better assessment on thinking force is difficult to effectively perform. With the appearance of a programming machine, modeling evaluation of thinking force becomes possible, the use process of the programming machine has extremely strong thinking force, how to acquire data from the programming machine, objective child thinking force evaluation is established, and a guiding direction is established, so that the programming machine becomes one of key subjects of education evaluation.
Disclosure of Invention
The invention overcomes the defects of the prior art, and provides the thinking force assessment method based on the programming machine data, which is used for rapidly and conveniently processing corresponding data according to the basic data of the programming machine, acquiring the thinking force level and giving the capability enhancement direction.
The technical scheme of the invention is as follows:
a thinking force assessment method based on programmer data comprises a robot, a programmer and a background server; the robot is connected with the programming machine for communication, and the programming machine is connected with the background server for communication through a network; the specific thinking force evaluation steps are as follows:
1) And a data acquisition step: the user normally starts the robot and the programming machine, and selects the corresponding game level; until the user finishes using; the programming machine transmits corresponding data of the whole process to a background server; the transmitted data comprise user information and operation data on a programming machine;
2) Data classification processing step: dividing the operation data of the programming machine into basic data and special data, wherein the basic data comprises a code block, a checkpoint basic score, a checkpoint task optimal solution and a checkpoint task suboptimal solution; the method comprises the steps of gate try times, gate optimal solution try times, gate correct solution numbers, gate optimal solution time, gate optimal solution re-completion times, gate optimal solution multi-solution times, gate milestones and gate milestone coefficients;
the special data comprises the number of equivalent code blocks, the checkpoint completion time coefficient, the suboptimal solution coefficient and the checkpoint optimal solution coefficient; equivalent code block number refers to equivalent functional code block number; the time coefficient of the checkpoint is set up by segments according to the time length of the checkpoint, the time length is divided into x segments, and x >2, m is the time segment where the time length of the checkpoint is actually completed, then the scoring formula when the time is in the segment is as follows:
when m=1, the checkpoint-completion time coefficient=1;
when m=2, the checkpoint-completion time coefficient= (1/2) + (1/2) 2 +…+(1/2) x-2 ；
When m is>2, the checkpoint completion time coefficient=1 { [ (1/2) + (1/2) 2 +…+(1/2) x-2 ]-[(1/2) +(1/2) 2 +…+(1/2) m-2 ]}；
Comparing the suboptimal solution coefficient with the level of the level task optimal solution and the game level, and carrying out segmentation scoring according to the quantity difference of the equivalent code blocks; the value of the equivalent code block quantity difference is divided into a sections, a >2, b is the section of the actual equivalent code block quantity difference, c is the grade quantity of the game, d is the grade of the selected corresponding game level, and the score formula of the specific suboptimal solution coefficient is as follows:
when the value of the equivalent code block quantity difference is zero, the suboptimal solution coefficient is 1;
remaining suboptimal solution coefficients= (1/2) + (1/2) 2 +…+(1/2) d+a-b ；
The formula of the checkpoint optimal solution coefficients is as follows:
the optimal solution coefficient of the checkpoint=1× [ (1/2) + (1/2) 2 +…+(1/2) d+e-f ]；
The total number of the checkpoint attempts and the optimal solution attempts of the checkpoint is divided into e sections, and f is the section position where the actual total number is located;
3) The step of scoring each dimension of thinking force: thinking ability assessment includes resolution, generalization, algorithm, concentration, logic, and creativity;
specific calculations of the resolving power include the following:
the decomposition capacity value = gate base score x gate milestone coefficient x gate completion time coefficient x sub-optimal solution coefficient;
decomposition capacity remaining value = checkpoint base score (1-checkpoint milestone coefficient);
decomposition ability compensation value=decomposition ability remaining value (1-1/2 n ) N is the level of the corresponding game level; at the same timeUpdating the decomposition capability remaining value, decomposition capability remaining value=decomposition capability remaining value-decomposition capability compensation value;
specific calculations of the generalization capability include the following:
inductive capacity value = gate base score x gate optimal solution coefficient;
inductive capacity remaining value = gate base score (1-gate optimal solution coefficient);
specific calculations of the algorithmic capabilities include the following:
algorithm capability value = checkpoint base score x checkpoint completion time coefficient;
algorithm capability remaining value = checkpoint base fraction (1-checkpoint completion time coefficient);
the concentration capability only obtains the concentration score by the gateway with the highest level, and obtains the optimal solution compensation integral after obtaining the optimal solution, and the specific calculation comprises the following steps:
remaining total value = decomposition capability remaining value + generalization capability remaining value + algorithm capability remaining value;
concentration value = remaining total value (1/4) Number of checkpoint optimal solution attempts ；
Specific calculations of logic capabilities include the following:
logic capability value = remaining total value (1/2) (number of exact solutions for the checkpoint/number of attempts for optimal solutions for the checkpoint);
specific calculations of the creative capabilities include the following:
creating capability value = gate base score × number of times of gate optimal solution;
4) And (3) judging the thinking force level: the thinking level is obtained by combining the sum of the decomposing capability, the generalizing capability, the algorithm capability, the concentration capability, the logic capability and the creativity of the step 3);
thinking ability value = decomposition ability value + induction ability value + algorithm ability value + concentration ability value + logic ability value + creation ability value;
every time the thinking force value is increased by a certain value, the level is increased; thereby providing the user with a level of mental effort and giving the user a direction of increased ability.
Further, setting the game level in the operation data of the programming machine into a class; setting the game level by taking the number of equivalent code blocks of the optimal solution of the level task as a level class basis;
the gate base score refers to a base score represented by the gate itself;
the optimal solution of the checkpoint task refers to a solution for completing one checkpoint by using the least code blocks;
the suboptimal solution of the checkpoint task refers to a solution that uses non-minimum code blocks to complete a checkpoint;
the checkpoint completion time coefficient is a score coefficient of the completion time of the checkpoint;
the code blocks include functional code blocks, logical code blocks and event code blocks; functional code blocks represent a class of basic functions; logical code blocks represent logical functions, one logical code block being equivalent to two functional code blocks in terms of code block computation; the event code block is an event trigger type function, and needs to execute a section of function after an event is triggered, and one event code block is equivalent to two functional code blocks in the calculation of the code block.
Further, the number of checkpoint attempts refers to the number of attempts made by the user before the correct solution of the checkpoint is completed;
the number of the optimal solutions of the checkpoint refers to the number of times the user tries before completing the optimal solutions of the checkpoint;
the number of correct solutions of the checkpoint refers to the number of correct solutions in the number of attempts by the user before the optimal solution of the checkpoint is completed;
the gate optimal solution coefficient refers to the score coefficient of different class games according to the gate try times and the gate optimal solution try times;
the time of the optimal solution of the checkpoint refers to the time taken by the user to complete the optimal solution of the checkpoint for the first time;
the number of the re-completion of the optimal solution of the checkpoint refers to the number of the optimal solution completed by the checkpoint after the user completes the optimal solution for the first time;
the multi-solution times of the optimal solutions of the checkpoints refer to the times that the user finishes the checkpoints by using different optimal solutions for multiple times;
level milestones refer to the optimal solution in obtaining a high-level game level the low-level checkpoint code blocks that the user has used are included, each high-level checkpoint contains at most two checkpoint milestones;
the gate milestone coefficients confirm the score coefficients of their games from the number of gate milestones.
Compared with the prior art, the invention has the advantages that:
the invention provides a system which has simple structure and convenient data acquisition, and reasonably utilizes basic data to reasonably evaluate thinking force in six dimensions. Specific thinking ability assessment includes resolution ability, generalization ability, algorithm ability, concentration ability, logic ability, and creativity ability.
Wherein the resolution capability breaks down the data, flow, or problem into smaller and easier to handle portions. The final program distribution is designed. The game task is decomposed into a plurality of subtasks, and the subtasks are realized respectively through programming, so that the theme specific task is finally realized. The distributed task program and the main task program are collected. The main program can be decomposed into a plurality of small programs and finally completed, so that the code blocks are reasonably analyzed as a data basis.
The generalization ability identifies patterns/trends and rules for solving the problem and finds a better solution. Observing the mode and rule (the procedure of the scene of the first three against the first three) for solving the same kind of problems. Other similar programs that solve the primary task or similar programs for similar scenarios are collected. The optimal solution can be obtained rapidly. Therefore, the optimal solution of the checkpoint task and the suboptimal solution of the checkpoint task are used as effective basic data for evaluation.
The algorithmic capabilities are the means and steps to find out the details of the method and steps to solve this or similar problems, and thus solve this problem according to this description. 1, a program for improving task difficulty; 2, optimizing the program. And acquiring a program for improving task difficulty or an optimized program. The problem can be positioned quickly, and a scheme for solving the problem is completed. And taking the checkpoint completion time coefficient as a judgment basis.
Concentration is the ability to continue to drill against a problem. While facing the game problem of high-level checkpoints, attempts can be continually made until the problem is resolved.
Logic forces find the correct solution with the correct program capabilities. The method is mainly based on the accurate solution quantity of the checkpoints and the optimal solution try times of the checkpoints.
The creativity faces the same problem, different attempts can be made, and different optimal solutions are found to evaluate based on the number of times of multi-solutions of the optimal solutions of the checkpoints.
The programming thinking model can objectively generate educational evaluation which can help children in the process of using the programming machine, and can help the children to continuously provide own thinking ability in the playing process.
Detailed Description
The invention is further described below in connection with the following detailed description.
Examples:
a thinking force assessment method based on programmer data comprises a robot, a programmer and a background server; the robot is connected with the programming machine for communication, and the programming machine is connected with the background server for communication through a network. The specific thinking force evaluation steps are as follows:
1) And a data acquisition step: the user normally starts the robot and the programming machine, and selects the corresponding game level. Until the user finishes using; the programming machine transmits corresponding data of the whole process to a background server; the transmitted data includes user information, operating data on the programming machine.
The user information includes userNo: a user name; clientId: a device unique ID; version: version number. The operating data on the programmer includes runTime: the equipment operation time length is in units of milliseconds; seq: program instruction list; the Seq stores a code block list representing code data that has been executed, and the model data analysis is mainly to analyze the code block list information.
After the power-on is connected to the internet, the following data contents are sent:
{"userNo":"18818227302","clientId":"4C:11:AE:F7:9E:AC","version" :"1.0","runTime":0,"seq":["xxx","xxx","xxx",......],"mode":"START"}
when the automatic shutdown is carried out, the following data contents are sent, and the shutdown is carried out after the sending is completed:
{"userNo":"18818227302","clientId":"4C:11:AE:F7:9E:AC","version": "1.0","runTime":xxx,"seq":["xxx","xxx","xxx",......],"mode":"AUTO_ST OP"}
manual shutdown, transmitting the following data contents, and automatically shutting down after the transmission:
{"userNo":"18818227302","clientId":"4C:11:AE:F7:9E:AC","version" :"1.0","runTime":xxx,"seq":["xxx","xxx","xxx",......],"mode":"STOP"}
during use, the user stores programming data in a list, and the data is sent to the cloud as long as the data exists in the list. The data content is as follows:
{"userNo":"18818227302","clientId":"4C:11:AE:F7:9E:AC","version" :"1.0","runTime":xxx,"seq":["code1","code2","code3",......],"mode":" PLAY"}
2) Data classification processing step: the entire program machine operation data, i.e., the list of stored code blocks in the Seq, can also be divided into basic data and special data. The basic data comprises a code block, a checkpoint basic SCORE STAGE_BASE_SCORE, a checkpoint task OPTIMAL SOLUTION STAGE_OPTIMAL_SOLUTION, and a checkpoint task SUBOPTIMAL SOLUTION STAGE_SUBOPTIMAL_SOLUTION; the number of the gate attempts is stage_TRY_TIMES, the number of the gate OPTIMAL SOLUTION attempts is stage_OPTIMAL_SOLUTION_TRY_TIMES, the number of the gate correct SOLUTIONs is stage_RIGHT_SOLUTION_TIMES, the gate OPTIMAL SOLUTION TIME is stage_OPTIMAL_SOLUTION_TIME the method comprises the steps of obtaining the number of re-completion of the OPTIMAL SOLUTION of the checkpoint, namely, the number of re-completion of the OPTIMAL SOLUTION of the checkpoint, the number of multi-SOLUTION of the OPTIMAL SOLUTION of the checkpoint, namely, the number of re-completion of the OPTIMAL SOLUTION of the checkpoint, the number of re-completion of the SOLUTION of the checkpoint, namely, the number of re-completion of the SOLUTION of the checkpoint, and the parameters of the checkpoint.
The special data includes EQUIVALENT CODE BLOCK number EQUIVALENT_CODE_BLOCK_NUM, checkpoint completion TIME coefficient STAGE_TIME_FACTOR, sub-OPTIMAL SOLUTION coefficient STAGE_OPTIMAL_SOLUTION_FACTOR.
Setting the game level in the operation data of the programming machine into a class. And setting the game level by taking the number of equivalent code blocks of the optimal solution of the level task as a level class basis.
The checkpoint base score refers to a base score that the checkpoint itself represents.
The optimal solution of the checkpoint task refers to a solution that uses the least number of code blocks to complete a checkpoint.
The sub-optimal solution of the checkpoint task refers to a solution that uses non-minimum code blocks to complete a checkpoint.
The checkpoint completion time coefficient is a score coefficient of the completion checkpoint time.
Wherein the equivalent number of code blocks refers to the equivalent number of functional code blocks.
The specific checkpoint is used as a preset task, a plurality of code blocks are required to cooperatively operate, and the characteristic function is completed, so that the checkpoint challenge is completed. The code block difficulty level is classified into four levels, for example, from the number of code blocks and the task completion logic complexity level. From easy to difficult, the method is divided into four grades of A, B, C, D, and the grades correspond to four basic values of 1, 2, 3 and 4 respectively. Namely, c is the number of the levels of the game, d is the level value of the selected corresponding game level, and the specific level design is as follows:
class a checkpoints, complete task optimal solution equal_code_block_num < =3, stage_base_code=1, d=1.
Class B checkpoints, complete task optimal solution 3< tasks_code_block_num < =8, stage_base_code=2, d=2.
Class C checkpoints, complete task optimal solution 8< tasks_code_block_num < =16, stage_base_code= 5,d =3.
Class D checkpoints, complete task optimal solution equal_code_block_num >16, stage_base_code=10, d=4.
The time coefficient of the checkpoint is set up by segments according to the time length of the checkpoint, the time length is divided into x segments, and x >2, m is the time segment where the time length of the checkpoint is actually completed, then the scoring formula when the time is in the segment is as follows:
when m.ltoreq.d, the checkpoint completion time coefficient=1.
When m is>d, checkpoint completion time coefficient=1 { [ (1/2) + (1/2) 2 +…+(1/2) x-d ]-[(1/2) +(1/2) 2 +…+(1/2) m-d-1 ]}。
Specifically, the above four classes are combined, and 7 time periods are divided into the following classes:
the code blocks include functional code blocks, logical code blocks, and event code blocks. Functional code blocks represent a basic class of functions such as forward, reverse, turn, etc. Logical code blocks represent logical functions such as condition and loop logic. One logical code block is equivalent to two functional code blocks in terms of code block computation. The event code block is an event trigger type function, and needs to execute a section of functions, such as a voice control code block and a light control code block, after an event is triggered. One event code block is equivalent to two functional code blocks in terms of code block computation.
The number of checkpoint attempts refers to the number of attempts a user has made before completing the correct solution for the checkpoint.
The number of checkpoint optimal solution attempts refers to the number of attempts a user has made before completing the checkpoint optimal solution.
The number of correct solutions of the checkpoint refers to the number of correct solutions in the number of attempts made by the user before the optimal solution of the checkpoint is completed.
The gate optimal solution coefficient refers to the score coefficient of games of different classes set according to the gate try times and the gate optimal solution try times.
The time of the optimal solution of the checkpoint refers to the time taken by the user to complete the optimal solution of the checkpoint for the first time.
The number of the optimal solutions of the checkpoint is the number of times that the user challenges the optimal solutions of the checkpoint again after completing the optimal solutions for the first time.
The multiple solutions of the optimal solutions of the checkpoints refer to the times that the user completes the checkpoints by using different optimal solutions for multiple times.
Level milestones refer to the optimal solution in obtaining a high-level game level the low-level checkpoint code blocks that the user has used are included, each high-level checkpoint contains at most two checkpoint milestones.
The gate milestone coefficients confirm the score coefficients of their games from the number of gate milestones.
Wherein the equivalent number of code blocks refers to the equivalent number of functional code blocks.
And comparing the suboptimal solution coefficient with the level of the level task optimal solution and the game level, and carrying out segmentation scoring according to the quantity difference of the equivalent code blocks. The value of the equivalent code block quantity difference is divided into a sections, a >2, b is the dividing section where the actual equivalent code block quantity difference is located, namely the grouping sequence number, c is the level quantity of the game, d is the level where the selected corresponding game level is located, and the score formula of the specific suboptimal solution coefficient is as follows:
when the value of the equivalent code block number difference is zero, the suboptimal solution coefficient is 1.
Remaining suboptimal solution coefficients= (1/2) + (1/2) 2 +…+(1/2) d+a-b 。
The grading list is specifically as follows:
the formula of the checkpoint optimal solution coefficients is as follows:
the optimal solution coefficient of the checkpoint=1× [ (1/2) + (1/2) 2 +…+(1/2) d+e-f ]。
The total of the number of the gate attempts and the number of the gate optimal solution attempts is divided into e segments, namely 5 segments of the following table, and f is the segment position where the actual total number is located.
The checkpoint MILESTONE coefficient (STAGE_MILESTONE_FACTOR) example data is as follows
| Number of milestones | Class A | Class B | Class C | Class D |
| 1 | 0.5 | 0.5 | ||
| 2 | 1 | 1 |
3) The step of scoring each dimension of thinking force: thinking ability assessment includes resolution, generalization, algorithm, concentration, logic, and creativity.
Resolution capability (decompfigure_capability): the data, flow, or problem is broken down into smaller and easily handled portions. The final program distribution is designed.
The specific tasks are realized by decomposing the main task into a plurality of subtasks and programming the subtasks respectively, and finally the theme specific tasks are realized. The distributed task program and the main task program are collected. Can decompose the main program into a plurality of small programs and finally finish
INDUCTIVE capacity (inductive_potential): patterns/trends and rules of solving the problem are identified, and a more optimal solution is found. Observing the mode and rule (the procedure of the scene of the first three against the first three) for solving the same kind of problems. Other similar programs that solve the primary task or similar programs for similar scenarios are collected. The optimal solution can be obtained rapidly.
Algorithmic capability (Algorithm_ABILITY): the algorithm design is to find a detailed description of the methods and steps that solve such problems or similar problems, and thus solve such problems based on this description. 1, a program for improving task difficulty; 2, optimizing the program. And acquiring a program for improving task difficulty or an optimized program. The problem can be positioned quickly, and a scheme for solving the problem is completed.
CONCENTRATION (concentration_potential): concentration is the ability to continue to drill against a problem. In the face of a class D problem, attempts can be continually made until the problem is resolved.
Logic force (logic_logic): correct program capabilities. In the face of the A, B, C, D problem, the correct solution can be found.
Creativity (creativity): different attempts may be made to find different optimal solutions to the same problem.
Specific calculations of the resolving power include the following:
the resolution capability value = gate base score x gate milestone coefficient x gate completion time coefficient x sub-optimal solution coefficient.
Decomposition capability remaining value = gate base score (1-gate milestone coefficient).
Decomposition ability compensation value=decomposition ability remaining value (1-1/2 n ) N is the level of the corresponding game level. The decomposition capability remaining value=decomposition capability remaining value-decomposition capability compensation value is updated at the same time.
Specific calculations of the generalization capability include the following:
inductive capacity value = gate base score. Gate optimal solution coefficient.
Inductive capacity remaining value = gate base score (1-gate optimal solution coefficient).
Specific calculations of the algorithmic capabilities include the following:
algorithm capability value = gate base score x gate completion time coefficient.
Algorithm capability remaining value = gate base score (1-gate completion time coefficient).
The concentration capability only obtains the concentration score by the gateway with the highest level, and obtains the optimal solution compensation integral after obtaining the optimal solution, and the specific calculation comprises the following steps:
remaining total value=decomposition capability remaining value+generalization capability remaining value+algorithm capability remaining value.
Concentration value = remaining total value (1/4) Number of checkpoint optimal solution attempts 。
Specific calculations of logic capabilities include the following:
logic capability value=remaining total value (1/2) × (number of exact solutions for the checkpoint/number of attempts for optimal solutions for the checkpoint).
Specific calculations of the creative capabilities include the following:
creativity value = gate base score × number of times of gate optimal solution.
4) And (3) judging the thinking force level: and (3) combining the decomposition capacity, the generalization capacity, the algorithm capacity, the concentration capacity, the logic capacity and the creativity capacity of the step (3) to obtain the thinking power level.
Thinking ability value = decomposition ability value + induction ability value + algorithm ability value + concentration ability value + logic ability value + creation ability value.
Every time the thinking force value is increased by a certain value, the level is increased. Thereby providing the user with a level of mental effort and giving the user a direction of increased ability.
Taking example data as an example, THINKING force integration level (thinking_power_points)
The first order integral (THINKING_POWER_POINTS_1) is 20 minutes.
The second order integral (THINKING_POWER_POINTS_2) is 40 minutes, and the second order integral difference is (THINKING_POWER_POINTS_DIFF_1) 20 minutes.
The rank judgment formula for each stage is then as follows:
THINKING_POWER_POINTS_DIFF_p＝THINKING_POWER_POINTS_DIFF_p-1* 1.1 p-1 the method comprises the steps of carrying out a first treatment on the surface of the p is the grade of the determination.
THINKING_POWER_POINTS_p=40+ (THINKING_POWER_POINTS_DIFF_2+ … +THINKING_POWER_POINTS_DIFF_p-1). I.e. the third pole has an integral of 40+20×1.1 minutes and an integral difference of 20×1.1×1.1. The fourth integration is 40+20 x 1.1+20 x 1.1, integral difference 20 x 1.1.
Parents can obtain the current thinking ability of children according to the score of the integral grade and the six dimensions of thinking ability. According to the scheme, the next game level of the child can be recommended according to the obtained thinking force score, and the child is guided to select the game level, so that thinking force is improved as much as possible.
In the scheme, 50 children are selected for cultivation in each age group by different ages. After half a year, the third party evaluation is carried out on 50 children who do not use the programming machine in the same age group. According to the result of the conventional test paper, the children cultivated by the scheme are obviously higher than those not using the programming machine.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the concept of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the present invention.
Claims (3)
1. A thinking power assessment method based on programming machine data is characterized in that: the system comprises a robot, a programming machine and a background server; the robot is connected with the programming machine for communication, and the programming machine is connected with the background server for communication through a network; the specific thinking force evaluation steps are as follows:
1) And a data acquisition step: the user normally starts the robot and the programming machine, and selects the corresponding game level; until the user finishes using; the programming machine transmits corresponding data of the whole process to a background server; the transmitted data comprise user information and operation data on a programming machine;
2) Data classification processing step: dividing the operation data of the programming machine into basic data and special data, wherein the basic data comprises a code block, a checkpoint basic score, a checkpoint task optimal solution and a checkpoint task suboptimal solution; the method comprises the steps of gate try times, gate optimal solution try times, gate correct solution numbers, gate optimal solution time, gate optimal solution re-completion times, gate optimal solution multi-solution times, gate milestones and gate milestone coefficients;
the special data comprises the number of equivalent code blocks, the checkpoint completion time coefficient, the suboptimal solution coefficient and the checkpoint optimal solution coefficient; equivalent code block number refers to equivalent functional code block number; the time coefficient of the checkpoint is set up by segments according to the time length of the checkpoint, the time length is divided into x segments, and x >2, m is the time segment where the time length of the checkpoint is actually completed, then the scoring formula when the time is in the segment is as follows:
when m is less than or equal to d, the checkpoint completion time coefficient=1;
when m is>d, checkpoint completion time coefficient=1 { [ (1/2) + (1/2) 2 +…+(1/2) x-d ]-[(1/2)+(1/2) 2 +…+(1/2) m-d-1 ]}；
Comparing the suboptimal solution coefficient with the level of the level task optimal solution and the game level, and carrying out segmentation scoring according to the quantity difference of the equivalent code blocks; the value of the equivalent code block quantity difference is divided into a sections, a >2, b is the section of the actual equivalent code block quantity difference, c is the grade quantity of the game, d is the grade of the selected corresponding game level, and the score formula of the specific suboptimal solution coefficient is as follows:
when the value of the equivalent code block quantity difference is zero, the suboptimal solution coefficient is 1;
remaining suboptimal solution coefficients= (1/2) + (1/2) 2 +…+(1/2) d+a-b ；
The formula of the checkpoint optimal solution coefficients is as follows:
the optimal solution coefficient of the checkpoint=1× [ (1/2) + (1/2) 2 +…+(1/2) d+e-f ]；
The total number of the checkpoint attempts and the optimal solution attempts of the checkpoint is divided into e sections, and f is the section position where the actual total number is located;
3) The step of scoring each dimension of thinking force: thinking ability assessment includes resolution, generalization, algorithm, concentration, logic, and creativity;
specific calculations of the resolving power include the following:
the decomposition capacity value = gate base score x gate milestone coefficient x gate completion time coefficient x sub-optimal solution coefficient;
decomposition capacity remaining value = checkpoint base score (1-checkpoint milestone coefficient);
decomposition ability compensation value=decomposition ability remaining value (1-1/2 n ) N is the level of the corresponding game level; simultaneously updating the decomposition capability remaining value, the decomposition capability remaining value=the decomposition capability remaining value-the decomposition capability compensation value;
specific calculations of the generalization capability include the following:
inductive capacity value = gate base score x gate optimal solution coefficient;
inductive capacity remaining value = gate base score (1-gate optimal solution coefficient);
specific calculations of the algorithmic capabilities include the following:
algorithm capability value = checkpoint base score x checkpoint completion time coefficient;
algorithm capability remaining value = checkpoint base fraction (1-checkpoint completion time coefficient);
the concentration capability only obtains the concentration score by the gateway with the highest level, and obtains the optimal solution compensation integral after obtaining the optimal solution, and the specific calculation comprises the following steps:
remaining total value = decomposition capability remaining value + generalization capability remaining value + algorithm capability remaining value;
concentration value = remaining total value (1/4) Number of checkpoint optimal solution attempts ；
Specific calculations of logic capabilities include the following:
logic capability value = remaining total value (1/2) (number of exact solutions for the checkpoint/number of attempts for optimal solutions for the checkpoint);
specific calculations of the creative capabilities include the following:
creating capability value = gate base score × number of times of gate optimal solution;
4) And (3) judging the thinking force level: the thinking level is obtained by combining the sum of the decomposing capability, the generalizing capability, the algorithm capability, the concentration capability, the logic capability and the creativity of the step 3);
thinking ability value = decomposition ability value + induction ability value + algorithm ability value + concentration ability value + logic ability value + creation ability value;
every time the thinking force value is increased by a certain value, the level is increased; thereby providing the user with a level of mental effort and giving the user a direction of increased ability.
2. The thinking assessment method based on programmer's data as claimed in claim 1, wherein: setting the game level in the operation data of the programming machine into a class; setting the game level by taking the number of equivalent code blocks of the optimal solution of the level task as a level class basis;
the gate base score refers to a base score represented by the gate itself;
the optimal solution of the checkpoint task refers to a solution for completing one checkpoint by using the least code blocks;
the suboptimal solution of the checkpoint task refers to a solution that uses non-minimum code blocks to complete a checkpoint;
the checkpoint completion time coefficient is a score coefficient of the completion time of the checkpoint;
the code blocks include functional code blocks, logical code blocks and event code blocks; functional code blocks represent a class of basic functions; logical code blocks represent logical functions, one logical code block being equivalent to two functional code blocks in terms of code block computation; the event code block is an event trigger type function, and needs to execute a section of function after an event is triggered, and one event code block is equivalent to two functional code blocks in the calculation of the code block.
3. The thinking assessment method based on programmer's data as claimed in claim 1, wherein:
the checkpoint attempts refer to the number of attempts a user has made before completing the checkpoint correct solution;
the number of the optimal solutions of the checkpoint refers to the number of times the user tries before completing the optimal solutions of the checkpoint;
the number of correct solutions of the checkpoint refers to the number of correct solutions in the number of attempts by the user before the optimal solution of the checkpoint is completed;
the gate optimal solution coefficient refers to the score coefficient of different class games according to the gate try times and the gate optimal solution try times;
the time of the optimal solution of the checkpoint refers to the time taken by the user to complete the optimal solution of the checkpoint for the first time;
the number of the re-completion of the optimal solution of the checkpoint refers to the number of the optimal solution completed by the checkpoint after the user completes the optimal solution for the first time;
the multi-solution times of the optimal solutions of the checkpoints refer to the times that the user finishes the checkpoints by using different optimal solutions for multiple times;
level milestones refer to the optimal solution in obtaining a high-level game level the low-level checkpoint code blocks that the user has used are included, each high-level checkpoint contains at most two checkpoint milestones;
the gate milestone coefficients confirm the score coefficients of their games from the number of gate milestones.
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