Computing thinking evaluation method based on BP neural network and multi-source data fusion

Abstract

The calculation thinking evaluation method based on the BP neural network and the multi-source data fusion in the application fuses multi-source calculation thinking evaluation data, constructs secondary indexes and primary indexes based on a clustering algorithm, constructs a BP neural network with self-feedback regulation by using the standardized calculation thinking evaluation data as input, is used for fusing the calculation thinking evaluation data collected in various ways, solves the calculation thinking ability evaluation problem under the unlabeled data set, can shield the multi-source heterogeneous characteristics of the evaluation data, and predicts the calculation thinking ability of learners.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer data intelligent processing, and in particular to a computing thinking evaluation method based on BP neural network and multi-source data fusion. BACKGROUND

[0002] Computational thinking (CT) is a problem-solving process, which is a habit and mode of thinking formed on the basis of computer science, and includes many characteristics, such as logical sequencing and analyzing data, using a series of ordered steps (or algorithms) to create solutions and handling strategies, such as confidently dealing with complexity and open-ended problems. CT is essential for the development of computer applications, but it can also be used to support problem solving in all disciplines, including mathematics, science and humanities. The cultivation of computational thinking cannot be separated from the research on the evaluation of computational thinking. Since computational thinking is implicit, it cannot be directly measured by related tools like height, weight and temperature, therefore, how to evaluate computational thinking, how to judge whether students have relevant computational thinking literacy and the degree they have reached, and from which aspects and indicators to measure students' computational thinking ability have become the premise and urgent needs of the research on the cultivation of computational thinking.

[0003] There are various ways to evaluate computational thinking, such as observing students' specific performance in diverse situations, analyzing and evaluating through students' works, and investigating students' computational thinking through questionnaires or test questions.

[0004] The current process of establishing the evaluation index system of computational thinking is generally to first establish the index system, usually according to the opinions of experts, select a part of indicators as the first-level indicators, then further decompose each first-level indicator into second-level indicators, and then from the second-level indicators to the third-level indicators, and the second step is to determine the weight of each indicator.

[0005] The evaluation scheme should not only have high representativeness, but also be easy to understand and use, and have strong operability. However, in the evaluation index system of computational thinking, the total number of indicators at all levels may be large, and the difference between each indicator may be large, which leads to great difficulty in the final selection of indicators.

[0006] Therefore, it is necessary to study an efficient and accurate computational thinking evaluation method. SUMMARY

[0007] The main purpose of the present application is to provide a computational thinking evaluation method based on BP neural network and multi-source data fusion to solve the above technical problems.

[0008] To achieve the above purpose, the present application provides a computational thinking evaluation method based on BP neural network and multi-source data fusion, characterized in that it comprises the following steps:

[0009] S1, constructing a training data sample set according to the computational thinking ability evaluation data, each sample data in the training data sample set comprising a plurality of sets of secondary index evaluation data and a plurality of primary index values corresponding to the plurality of sets of secondary index evaluation data; wherein the secondary index evaluation data comprises concept knowledge secondary index test evaluation data, problem exploration secondary index test evaluation data, algorithm thinking secondary index test evaluation data, and value concept secondary index scale evaluation data;

[0010] S2, taking the score values of the secondary index evaluation data as input items of a neural network and the primary index values as output items, training a preset neural network prediction model according to the training data sample set to obtain a trained evaluation prediction model;

[0011] S3, taking the score values of the secondary index evaluation data as input items of a neural network, obtaining a primary index evaluation value by calculating through the trained evaluation prediction model, taking the primary index evaluation value as an input item of a back propagation training algorithm model, and taking the score values of the secondary index evaluation data as output items, optimizing the weight parameters of the preset neural network prediction model to obtain an optimized evaluation prediction model;

[0012] S4, inputting the secondary index evaluation data to be evaluated as an input value into the optimized evaluation prediction model to obtain a primary index evaluation value to be evaluated, and obtaining a computational thinking evaluation result according to the primary index evaluation value to be evaluated.

[0013] Preferably, the method further comprises a test data sample set, each sample data in the training data sample set being identical in data structure and form to the training data sample set; the method further comprises a step of correcting the trained evaluation prediction model and / or the optimized evaluation prediction model according to the test data sample set to obtain a corrected evaluation prediction model; and the step S4 is performed by using the corrected evaluation prediction model.

[0014] Preferably, the concept knowledge secondary index test evaluation data comprises evaluation results of a preset number of randomly selected test questions from a test question bank; and the problem exploration secondary index test evaluation data and the algorithm thinking secondary index test evaluation data comprise answer evaluation data of programming test questions randomly selected from the test question bank.

[0015] Preferably, the concept knowledge secondary index test evaluation data includes two secondary index test evaluation data of definitional knowledge test evaluation data and operational knowledge test evaluation data; the problem exploration secondary index test evaluation data includes three secondary indexes of abstraction and decomposition evaluation data, module definition evaluation data and module design evaluation data obtained according to the programming test results; the algorithm thinking secondary index test evaluation data includes three secondary indexes of logic evaluation data, control evaluation data and construction evaluation data obtained according to the programming test results; and the value concept index scale evaluation data includes three secondary indexes of questioning evaluation data, cooperation evaluation data and innovation evaluation data obtained according to the scale evaluation results.

[0016] Preferably, the primary index value includes a concept knowledge primary index value obtained according to the concept knowledge secondary index test evaluation data, a problem exploration primary index value obtained according to the problem exploration secondary index test evaluation data, an algorithm thinking primary index value obtained according to the algorithm thinking secondary index test evaluation data, and a value concept primary index value obtained according to the value concept secondary index scale evaluation data.

[0017] Preferably, the method further includes the step of standardizing and normalizing the secondary index evaluation data, so that the total standard score of each primary index value is the same.

[0018] The computing thinking evaluation method based on the BP neural network and the multi-source data fusion in the application fuses multi-source computing thinking evaluation data, constructs secondary indexes and primary indexes based on a clustering algorithm, constructs a BP neural network with feedback regulation by using the standardized computing thinking evaluation data as input, is used for fusing computing thinking evaluation data collected in multiple ways, solves the computing thinking ability evaluation problem under a no-label data set, can shield the multi-source heterogeneous characteristics of the evaluation data, and predicts the computing thinking ability of learners. BRIEF DESCRIPTION OF DRAWINGS

[0019] The accompanying drawings, which are part of the present application, serve to further understand the present application, and the schematic embodiments of the present application and the description thereof serve to explain the present application but do not constitute an improper limitation on the present application. Obviously, the accompanying drawings in the following description are only some embodiments, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the drawings.

[0020] Figure 1 FIG. 1 is a flowchart of the computing thinking evaluation method based on the BP neural network and the multi-source data fusion in an embodiment of the present application.

[0021] Figure 2A neural network structure schematic diagram of a computing thinking evaluation method based on a BP neural network and multi-source data fusion in an embodiment of the present application.

[0022] Figure 3 A hardware structure schematic diagram of a computing thinking evaluation method based on a BP neural network and multi-source data fusion in an embodiment of the present application.

[0023] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. Embodiment

[0024] The technical problems solved by the embodiments of the present application, the technical solutions adopted and the technical effects achieved will be described clearly and completely below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other equivalent or obvious modified embodiments obtained by those skilled in the art without creative effort fall within the protection scope of the present application. The embodiments of the present application can be embodied in various different ways as defined and covered in the claims.

[0025] It should be noted that in the following description, many specific details are given to facilitate understanding. However, it is obvious that the implementation of the present application can be implemented without these specific details.

[0026] It should be noted that in the absence of explicit limitations or conflicts, the various embodiments in the present application and the technical features therein can be combined with each other to form technical solutions.

[0027] The present application proposes a computing thinking evaluation method based on a BP neural network and multi-source data fusion, characterized in that it comprises the following steps:

[0028] S1, constructing a training data sample set according to computing thinking ability evaluation data, each sample data in the training data sample set comprising multiple sets of secondary index evaluation data and multiple primary index values corresponding to the multiple sets of secondary index evaluation data; wherein the secondary index evaluation data comprises concept knowledge secondary index test evaluation data, problem exploration secondary index test evaluation data, algorithm thinking secondary index test evaluation data and value concept secondary index scale evaluation data;

[0029] S2, taking the score value of the secondary index evaluation data as the input item of the neural network and the primary index value as the output item, training a preset neural network prediction model according to the training data sample set to obtain a trained evaluation prediction model;

[0030] S3, taking the score value of the secondary index evaluation data as an input item of the neural network, calculating the primary index evaluation value through the trained evaluation prediction model, and taking the primary index evaluation value as an input item of the back propagation training algorithm model, the score value of the secondary index evaluation data as an output item, optimizing the weight parameters of the preset neural network prediction model to obtain an optimized evaluation prediction model;

[0031] S4, inputting the secondary index evaluation data to be evaluated into the optimized evaluation prediction model as an input value, thereby obtaining a primary index evaluation value to be evaluated, and obtaining a calculation thinking evaluation result according to the primary index evaluation value to be evaluated.

[0032] Specifically, the construction method of the secondary index and the primary index in the calculation thinking ability evaluation data is as follows: a plurality of domestic and foreign calculation thinking concepts are collected and sorted through literature, the calculation thinking concepts are accurately segmented by using Jieba segmentation tool, punctuation marks, stop words and other irrelevant words in the segmentation result are removed, a set of calculation thinking ability related words is obtained, and the set of words is converted into a set of word vectors by using word vector data trained based on Wiki Chinese corpus. The calculation thinking word vectors are clustered using a clustering algorithm. The specific clustering method is as follows: a word vector undirected weighted graph is constructed based on the similarity between word vectors, the graph is divided, the sum of edge weights in the subgraph is as large as possible, and the sum of edge weights between subgraphs is as small as possible, thereby obtaining a primary clustering result. The clustering process is repeated, and the clustering results are clustered respectively. Finally, key words are extracted from the secondary clustering results and appropriately adjusted to serve as secondary indexes of the calculation thinking evaluation index system. The primary index name is determined according to the secondary indexes, thereby establishing the calculation thinking evaluation index system, as shown in Table 1:

[0033] Table 1 Calculation thinking evaluation index system

[0034]

[0035] According to the characteristics of the primary index in the calculation thinking ability evaluation, calculation thinking ability evaluation data is collected through three ways, thereby realizing the multi-sourcing of data:

[0036] (1) Concept knowledge has the characteristics of basic knowledge, so the method of test evaluation is adopted to evaluate the students' mastery level of definitional knowledge and operational knowledge, which is one of the sources of evaluation data. A test bank is developed to generate a set of random test questions including 15 questions related to definitional knowledge and 15 questions related to operational knowledge, which are used to collect the scores of learners in definitional knowledge and operational knowledge;

[0037] (2) Problem exploration and algorithm thinking have practical attributes, so the programming question method is used to collect evaluation data, and the ability level of the two secondary indicators of the learners' problem exploration and algorithm thinking is obtained by analyzing the program, which is the second source of evaluation data. Specifically, a mapping relationship between programming and computational thinking is established, and the programming characteristics of learners are mined based on the mapping relationship to obtain the scores of learners in abstraction and decomposition, module definition, module design, logic, control, and construction.

[0038] (3) Values are more suitable for collecting using a scale, which measures the performance of learners in questioning, cooperation, and innovation, which is the third source of evaluation data. According to existing relevant scales, a set of computational thinking values scale involving questioning, cooperation, and innovation is developed to evaluate learners using the scale.

[0039] The data collected in the three ways needs to be standardized in the end, and the maximum score of each secondary indicator of computational thinking is shown in Table 2, which ensures that the total score of the primary indicator is 100 points.

[0040] Table 2 Standardization of computational thinking evaluation data

[0041]

[0042] Preferably, the concept knowledge secondary indicator test evaluation data includes the evaluation results of a preset number of randomly selected test questions from the question bank; the problem exploration secondary indicator test evaluation data and the algorithm thinking secondary indicator test evaluation data include the answer evaluation data of the programming questions randomly selected from the question bank.

[0043] Preferably, the concept knowledge secondary indicator test evaluation data includes definition knowledge test evaluation data and operational knowledge test evaluation data; the problem exploration secondary indicator test evaluation data includes abstraction and decomposition evaluation data, module definition evaluation data, and module design evaluation data obtained from the answer results of programming questions; the algorithm thinking secondary indicator test evaluation data includes logic evaluation data, control evaluation data, and construction evaluation data obtained from the answer results of programming questions; the values scale evaluation data includes questioning evaluation data, cooperation evaluation data, and innovation evaluation data obtained from the scale evaluation results.

[0044] Preferably, the primary indicator value includes the concept knowledge primary indicator value obtained from the concept knowledge secondary indicator test evaluation data, the problem exploration primary indicator value obtained from the problem exploration secondary indicator test evaluation data, the algorithm thinking primary indicator value obtained from the algorithm thinking secondary indicator test evaluation data, and the values primary indicator value obtained from the values secondary indicator scale evaluation data.

[0045] Preferably, it further comprises the steps of standardizing and normalizing the secondary index evaluation data, so that the total standard score of each primary index value is the same.

[0046] Please combine Figure 2 and Figure 3 The standardized computational thinking evaluation data is used as input to build a BP neural network with feedback regulation, which is used to fuse the computational thinking evaluation data collected from the three approaches, and solve the problem of computational thinking ability evaluation under the unlabeled data set. The structure of the bi-BP neural network is shown in Figure 2 The network structure mainly includes a neural network prediction model and a back propagation training algorithm model.

[0047] For the neural network prediction model, the input is the computational thinking evaluation data (i.e. the computational thinking secondary index evaluation data collected by the three evaluation methods), for example, [definitional knowledge = 45, operational knowledge = 40, abstraction and decomposition = 20, module definition = 23, module design = 32, logic = 24, control = 31, construction = 28, questioning = 25, cooperation = 19, innovation = 24]; The output is the score of the computational thinking primary index evaluation value, which needs to be converted to 0-100, for example, [conceptual knowledge = 85, problem exploration = 90, algorithm thinking = 81, concept attitude = 79]. The neural network prediction model is trained by the existing training data sample set, and finally the trained evaluation prediction model is obtained.

[0048] The back propagation training algorithm model is used to optimize the weight set, so that the neural network has the smallest error for the training data sample set. The input of the back propagation training algorithm model is the output result of the neural network prediction model, and the output is the prediction of the secondary index score. Through back propagation, the parameters of the neural network prediction model and the back propagation training algorithm model are adjusted, and the weight parameters of the preset neural network prediction model are optimized to obtain the optimized evaluation prediction model.

[0049] Preferably, it further comprises a test data sample set, each sample data in the training data sample set is the same as the data structure and form of the training data sample set; The method further comprises the steps of: correcting the trained evaluation prediction model and / or the optimized evaluation prediction model according to the test data sample set to obtain a corrected evaluation prediction model; The step S4 is calculated by the corrected evaluation prediction model.

[0050] Specifically, the optimized evaluation prediction model can also be verified by the existing test data sample set. The score value of the secondary index evaluation data in the test data sample is taken as the input item of the neural network, the first-level index evaluation value is calculated through the trained evaluation prediction model and / or the optimized evaluation prediction model, and then the first-level index evaluation value is compared with the actual first-level index value. If the hit rate is lower than expected, the model is corrected again, and the corrected evaluation prediction model is returned to step S2 and / or S3 for further training and optimization.

[0051] The calculation thinking evaluation method based on the BP neural network and multi-source data fusion in the application fuses multi-source calculation thinking evaluation data, constructs secondary indexes and first-level indexes based on a clustering algorithm, constructs a BP neural network with feedback regulation by using standardized calculation thinking evaluation data as input, is used for fusing calculation thinking evaluation data collected in various ways, solves the calculation thinking ability evaluation problem under a no-label data set, can shield the multi-source heterogeneous characteristics of the evaluation data, and predicts the calculation thinking ability of learners.

[0052] Figure 3 It is a hardware structure schematic diagram for running the calculation thinking evaluation method based on the BP neural network and multi-source data fusion provided by an embodiment of the application. As shown in the figure, Figure 3 The embodiment / computer 6 includes a processor 60, a memory 61, and a computer program 62 stored in the memory 61 and executable on the processor 60, for example, a program for running the calculation thinking evaluation method based on the BP neural network and multi-source data fusion. The processor 60 implements the steps in each of the above-mentioned various embodiments of the calculation thinking evaluation method based on the BP neural network and multi-source data fusion when executing the computer program 62. Alternatively, the processor 60 implements the functions of each module / unit in each of the above-mentioned various device embodiments when executing the computer program 62.

[0053] For example, the computer program 62 can be divided into one or more modules / units, which are stored in the memory 61 and executed by the processor 60 to complete the application. The one or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the computer program 62 in the computer 6.

[0054] The computer 6 can be a desktop computer, a notebook computer, a palm computer, and a cloud server, etc. The computer 6 device can include, but is not limited to, a processor 60, a memory 61. Those skilled in the art can understand, Figure 3The example of the computer 6 does not constitute a limitation on the computer 6, and can include more or fewer components than shown, or combine some components, or have different components, for example, the computer 6 can also include an input / output device, a network access device, a bus, etc.

[0055] The processor 60 can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0056] The memory 61 can be an internal storage unit of the computer 6, for example, a hard disk or a memory of the computer 6. The memory 61 can also be an external storage device of the terminal device 6, for example, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory 61 can also include both the internal storage unit of the computer 6 and the external storage device. The memory 61 is used to store the computer program and other programs and data required by the terminal device. The memory 61 can also be used to temporarily store data that has been output or will be output.

[0057] It can be clearly understood by those skilled in the art that, for the convenience and brevity of description, only the division of the above functional units and modules is exemplified, and in actual application, the above functions can be completed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit, and the integrated unit can be realized in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction, and do not limit the protection scope of the present application. The specific working process of the units and modules in the system can refer to the corresponding process in the foregoing method embodiments, which will not be described here.

[0058] In the above embodiments, the description of each embodiment has its own focus, and the parts not described or recorded in a certain embodiment can be referred to the relevant description of other embodiments.

[0059] Those skilled in the art can understand that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0060] In the embodiments provided by the present application, it should be understood that the disclosed apparatus / terminal device and method can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely schematic, and the division of the modules or units is merely a logical function division, and there can be another division manner in actual implementation. For example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or in other forms.

[0061] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e. they can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0062] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can exist physically independently, or two or more units can be integrated into one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0063] The integrated module / unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. The computer program can implement the steps of each method embodiment when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or some intermediate forms. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the contents included in the computer readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0064] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A computational thinking evaluation method based on BP neural network and multi-source data fusion, characterized in that, Including the following steps: S1. A training data sample set is constructed based on the computational thinking ability assessment data. Each sample data in the training data sample set includes multiple sets of secondary indicator assessment data, and multiple primary indicator values ​​corresponding to the multiple sets of secondary indicator assessment data. The secondary indicator assessment data includes assessment data for conceptual knowledge, problem exploration, algorithmic thinking, and value-based secondary indicator scales. The conceptual knowledge secondary indicator assessment data includes assessment data for definitional knowledge and operational knowledge. The assessment data includes two secondary indicators: the assessment data for identification questions and the assessment data for problem exploration. The secondary indicator assessment data includes three secondary indicators: abstraction and decomposition assessment data, module definition assessment data, and module design assessment data, all derived from the results of programming question answers. The secondary indicator assessment data for algorithmic thinking includes three secondary indicators: logic assessment data, control assessment data, and construction assessment data, all derived from the results of programming question answers. The assessment data for value-based indicators includes three secondary indicators: questioning assessment data, cooperation assessment data, and innovation assessment data, all derived from the scale assessment results. S2, the scores of the secondary indicator evaluation data are used as input to the neural network, and the values ​​of the primary indicator are used as output. The preset neural network prediction model is trained according to the training data sample set to obtain the trained evaluation prediction model. S3, the scores of the secondary indicator evaluation data are used as input to the neural network, and the primary indicator evaluation value is calculated by the trained evaluation prediction model. The primary indicator evaluation value is used as input to the backpropagation training algorithm model, and the scores of the secondary indicator evaluation data are used as output. The weight parameters of the preset neural network prediction model are optimized to obtain the optimized evaluation prediction model. S4. Input the evaluation data of the secondary indicator to be evaluated as the input value into the optimized evaluation prediction model to obtain the evaluation value of the primary indicator to be evaluated, and obtain the computational thinking evaluation result based on the evaluation value of the primary indicator to be evaluated. The method further includes a step of standardizing and normalizing the evaluation data of the secondary indicators so that the total standard score of each of the primary indicator values ​​is the same.

2. The computational thinking evaluation method based on BP neural network and multi-source data fusion as described in claim 1, characterized in that, It also includes a test data sample set, wherein each sample data in the training data sample set has the same data structure and form as the training data sample set; the method further includes the steps of: correcting the trained evaluation prediction model and / or the optimized evaluation prediction model according to the test data sample set to obtain a corrected evaluation prediction model; in step S4, calculations are performed using the corrected evaluation prediction model.

3. The computational thinking evaluation method based on BP neural network and multi-source data fusion as described in claim 1, characterized in that, The assessment data for the conceptual knowledge secondary indicator includes the assessment results of a predetermined number of questions randomly selected from the question bank; the assessment data for the problem exploration secondary indicator and the assessment data for the algorithmic thinking secondary indicator include the assessment results of answers to programming questions randomly selected from the question bank.

4. The computational thinking evaluation method based on BP neural network and multi-source data fusion according to claim 1, characterized in that, The primary indicator values ​​include the primary indicator values ​​for conceptual knowledge obtained from the secondary indicator test data for conceptual knowledge, the primary indicator values ​​for problem exploration obtained from the secondary indicator test data for problem exploration, the primary indicator values ​​for algorithmic thinking obtained from the secondary indicator test data for algorithmic thinking, and the primary indicator values ​​for values ​​obtained from the secondary indicator scale assessment data for values.

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