Learning evaluation method and device
By obtaining and processing students' activity data on the online learning platform, combining the preset learning evaluation framework, diversified and accurate learning evaluation is achieved, the shortcomings of existing evaluation methods are solved, and in-depth analysis and constructive feedback are provided.
Patent Information
- Application Number
- CN202311003347.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-08-09
- Publication Date
- 2025-02-18
AI Technical Summary
The existing online learning platforms rely on traditional methods in learning evaluation, neglecting process evaluation and diversified evaluation, and failing to provide accurate and constructive feedback.
By obtaining students' activity data on the teaching platform, data processing and quantification are carried out according to the preset learning evaluation framework, the weights of each evaluation indicator are determined, and in-depth analysis is carried out based on these data, so as to achieve the combination of process evaluation and result evaluation.
It achieves diverse, scientific and accurate evaluation of students' learning situation, provides constructive feedback, and helps students understand and adjust their learning status and strategies.
Smart Images
Figure CN119477018A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of computer technology, and in particular to a learning evaluation method, device, equipment and storage medium. Background Art
[0002] Affected by the changing times and complex social environment, online education has shown a strong development trend. More and more online learning platforms have emerged, broadening students' learning channels. However, behind the rapid development of online learning platforms, learning evaluation has encountered unnecessary neglect. According to the survey, most online learning platforms still use traditional evaluation methods, only evaluating students through academic examinations, ignoring the evaluation of students' learning process and the evaluation of morality, quality, etc., which does not meet the needs of cultivating all-round talents; although some online learning platforms have begun to pay attention to process evaluation, there is a deviation in the understanding of process evaluation, which leads to the evaluation of students' learning stopping at data stacking, without deep mining, scientific analysis, and the establishment of a practical evaluation system. It is impossible to make accurate evaluations of students to provide them with constructive suggestions and feedback. Therefore, how to build a diversified, scientific, and accurate evaluation system to achieve the combination of process evaluation and result evaluation, value-added evaluation and comprehensive evaluation has become the top priority of current teaching reform. Summary of the invention
[0003] Based on this, the present application provides a learning evaluation method, apparatus, device and storage medium to solve the problems existing in the prior art.
[0004] In a first aspect, a task processing method is provided, the method comprising:
[0005] Obtain students' activity data on the teaching platform system according to the needs of the preset learning evaluation framework;
[0006] Processing the activity data according to preset rules to obtain processed data;
[0007] A learning situation and evaluation for the student is determined based on the processed data.
[0008] According to an achievable method in the embodiment of the present application, the obtaining of student activity data on the teaching platform system specifically includes:
[0009] Data on students’ operation of digital resources, participation in classroom activities, independent learning and homework completion, and practical training.
[0010] According to an achievable method in an embodiment of the present application, the activity data is processed according to a preset rule to obtain the processed data, which specifically includes:
[0011] Preprocessing the collected activity data to obtain preprocessed data;
[0012] Performing quantization processing on the preprocessed data to obtain quantized data;
[0013] Determine the weight distribution of evaluation indicators in the learning evaluation framework to obtain the weight of each evaluation indicator;
[0014] The comprehensive score of the corresponding evaluation index is determined according to the quantitative data and the weight of each evaluation index.
[0015] According to an achievable method in an embodiment of the present application, after obtaining the quantized data, the method further includes:
[0016] The data belonging to the evaluation index in the quantitative data is visualized.
[0017] According to an achievable method in an embodiment of the present application, the collected activity data is preprocessed, and the preprocessed data obtained includes:
[0018] The collected data are subjected to format conversion, data completion and useless information deletion operations to obtain preprocessed data.
[0019] According to an achievable manner in an embodiment of the present application, the quantizing the preprocessed data to obtain quantized data includes:
[0020] The pre-processed data is subjected to absolute evaluation and / or relative evaluation processing to obtain quantitative data.
[0021] According to an achievable method in an embodiment of the present application, determining the weight distribution of the evaluation indicators in the learning evaluation framework to obtain the weight of each evaluation indicator includes:
[0022] Determine the dominance relationship of each evaluation indicator in the evaluation framework;
[0023] Constructing a judgment matrix of the next layer of indicators to the previous layer of indicators based on the dominance relationship;
[0024] The weight of each indicator in the next layer of indicators to the previous layer of indicators is determined according to the judgment matrix.
[0025] According to an achievable method in an embodiment of the present application, determining the comprehensive score of the corresponding evaluation index according to the quantitative data and the weight of each evaluation index includes:
[0026] The comprehensive scores of the evaluation indicators of the result evaluation and the decision evaluation are determined according to the weight values and the quantitative data.
[0027] According to an achievable method in an embodiment of the present application, determining the learning situation and evaluation of the student based on the processed data includes:
[0028] Convert the comprehensive score of the result evaluation and the comprehensive score of each evaluation index of the decision evaluation into corresponding grade representation;
[0029] A grade evaluation in a preset grade evaluation set is retrieved according to the grade representation, and the grade evaluation is displayed.
[0030] In a second aspect, a learning evaluation device is provided, the device comprising:
[0031] Data acquisition module: used to obtain students' activity data on the teaching platform system according to the needs of the preset learning evaluation framework;
[0032] Data processing module: used to process the activity data according to preset rules to obtain processed data;
[0033] Evaluation module: used for determining the learning situation and evaluation of the student based on the processed data.
[0034] According to an achievable method in an embodiment of the present application, the data processing module includes:
[0035] Preprocessing module: used to preprocess the collected activity data to obtain preprocessed data;
[0036] Data quantization module: used for quantizing the preprocessed data to obtain quantized data;
[0037] A weight calculation module is used to determine the weight distribution of the evaluation indicators in the learning evaluation framework and obtain the weight of each evaluation indicator;
[0038] The comprehensive score determination module is used to determine the comprehensive score of the corresponding evaluation index according to the quantitative data and the weight of each evaluation index.
[0039] According to an achievable method in an embodiment of the present application, the evaluation module includes:
[0040] Grade expression determination module: used to convert the comprehensive score of the result evaluation and the comprehensive score of each evaluation index of the decision evaluation into the corresponding grade expression;
[0041] The evaluation determination and display module is used to retrieve the grade evaluation in the preset grade evaluation set according to the grade representation, and display the grade evaluation.
[0042] In a third aspect, a computer device is provided, comprising:
[0043] at least one processor; and
[0044] a memory communicatively connected to the at least one processor; wherein,
[0045] The memory stores computer instructions that can be executed by the at least one processor, and the computer instructions are executed by the at least one processor to enable the at least one processor to perform the method involved in the first aspect above.
[0046] In a fourth aspect, a computer-readable storage medium is provided, on which computer instructions are stored, characterized in that the computer instructions are used to enable a computer to execute the method involved in the first aspect above.
[0047] According to the technical content provided in the embodiments of this application, this application obtains the activity data of students on the teaching platform system according to the needs of the preset learning evaluation framework; processes the activity data according to preset rules to obtain processed data; and determines the learning situation and evaluation for the students based on the processed data. The learning evaluation framework determined in advance by professionals in this application can effectively integrate process evaluation and result evaluation, value-added evaluation and comprehensive evaluation, and can conduct in-depth and scientific analysis of students' learning performance based on students' operational behaviors and learning data, so as to make accurate evaluations and give constructive suggestions, which can well help students understand their own learning situation and adjust their learning status and strategies. BRIEF DESCRIPTION OF THE DRAWINGS
[0048] Figure 1 A flowchart of a learning evaluation method in one embodiment;
[0049] Figure 2 A diagram of a structural model with a multi-level hierarchy of dominance relationships;
[0050] Figure 3 It is a three-level hierarchical evaluation index structure model diagram;
[0051] Figure 4 The structural model diagram of the evaluation index with n levels of hierarchy
[0052] Figure 5 It is a structural block diagram of a learning evaluation device in one embodiment;
[0053] Figure 6 is a structural block diagram of a data processing module 502 of a learning evaluation device in one embodiment;
[0054] Figure 7 is a structural block diagram of an evaluation module 503 of a learning evaluation device in an embodiment;
[0055] Figure 8FIG. 4 is a schematic structural diagram of a computer device in one embodiment. DETAILED DESCRIPTION
[0056] The present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.
[0057] Figure 1 A flow chart of a learning evaluation method provided in an embodiment of the present application, such as Figure 1 As shown, the method may include the following steps:
[0058] Step 101: Obtaining student activity data on the teaching platform system according to the requirements of the preset learning evaluation framework;
[0059] Specifically, the learning evaluation framework is an evaluation factor extracted from education and teaching based on professional teaching standards, talent training programs, curriculum standards, skill competition procedures, occupational standards, job tasks, and corporate needs. It is jointly discussed and decided by corporate practitioners, front-line teachers, and teaching and research experts. After determining the learning evaluation framework, collect student activity data on the teaching platform based on the type of data that needs to be collected in the framework. The data of students on the teaching platform is diverse, and the educational functions and models carried by the teaching platform are also diverse. This disordered and diverse data needs to be collected purposefully to avoid a large amount of noise data interfering with the accuracy of student learning evaluation.
[0060] Step 102: Processing the activity data according to preset rules to obtain processed data;
[0061] Specifically, since the student data obtained are original operational data, these data have various defects and cannot be used directly. They need to be converted and processed to obtain a data form that can correspond to the indicators of learning evaluation.
[0062] Step 103: Determine the learning situation and evaluation for the student based on the processed data.
[0063] Specifically, the learning evaluation framework is an evaluation factor extracted from education and teaching based on professional teaching standards, talent training programs, curriculum standards, skill competition procedures, occupational standards, job tasks, and corporate needs. It is jointly discussed and decided by corporate practitioners, front-line teachers, and teaching and research experts. After determining the standards for each evaluation factor, the data type required for the evaluation factor can be further determined. Since different data types correspond to different learning activities of students, collecting data on the performance of corresponding students in corresponding learning activities can build a data basis for scoring the student's learning situation. Then, the student's learning performance can be evaluated based on the data score obtained from the evaluation factor and corresponding evaluations or suggestions can be given.
[0064] It can be seen that the embodiment of the present application obtains the student's activity data on the teaching platform system according to the needs of the preset learning evaluation framework; processes the activity data according to preset rules to obtain processed data; and determines the learning situation and evaluation for the student based on the processed data. The learning evaluation framework determined in advance by professionals in this application can effectively integrate process evaluation and result evaluation, value-added evaluation and comprehensive evaluation, and can conduct in-depth and scientific analysis of students' learning performance based on students' operational behaviors and learning data, so as to make accurate evaluations and give constructive suggestions, which can well help students understand their own learning situation and adjust their learning status and strategies.
[0065] The following is a detailed description of step 101, i.e., "the acquisition of student activity data on the teaching platform system specifically includes: student operation data on digital resources, data on participation in classroom activities, data on independent learning and homework completion, and data on practical training," in conjunction with an embodiment.
[0066] In one embodiment of the present application, the entire system application in step 101 logically includes two main parts: a teaching platform system and a learning evaluation system. The teaching platform system runs a learning program and is the basic system for learning evaluation. It is physically connected to the learning evaluation system through a network, and the network includes but is not limited to the Internet, a local area network, a self-organizing network with a custom protocol, etc. Among them, students mainly carry out activities on the teaching platform system, and the teaching platform system is deployed with course resource modules, classroom activity modules, question bank modules, homework assessment modules and practical training modules as nodes; the functions of each module are as follows:
[0067] The course resource module distributes digital resources based on blockchain and is used to record students’ operations on digital resources;
[0068] The classroom activity module is used to record the classroom activities that students participate in and their performance, including roll call, Q&A, discussion, selection, in-class tests, notes, attachment learning, and group discussions;
[0069] The question bank module stores and retrieves test questions, and is the basic support module for students' independent practice and homework assessment modules;
[0070] Homework assessment module, used to record students' completed and unfinished homework and assessment status;
[0071] The practical training module is used to record students’ participation in practical training exercises.
[0072] When students operate on the above modules, the corresponding modules will record the students' corresponding operation data and data usage results. Therefore, the student activity data on the teaching platform system specifically includes the student's operation data on digital resources, participation in classroom activities, independent learning and homework completion data, and practical training data.
[0073] The above step 102, namely "processing the activity data according to preset rules to obtain processed data", is described in detail below in conjunction with an embodiment.
[0074] In one embodiment of the present application, it specifically includes: preprocessing the collected activity data to obtain preprocessed data; quantifying the preprocessed data to obtain quantified data; determining the weight distribution of the evaluation indicators in the learning evaluation framework to obtain the weight of each evaluation indicator; determining the comprehensive score of the corresponding evaluation indicator according to the quantified data and the weight of each evaluation indicator. That is, collecting and processing a large amount of information data, behavior data and operation data generated by students in the learning process, using blockchain to achieve traceability and evidence, and provide evaluation support; quantifying the collected data according to the rules; and then using the comprehensive scoring calculation model to calculate the comprehensive scores of the evaluation indicators at all levels based on the stored quantified data and the determined weights of the evaluation indicators at all levels.
[0075] The preprocessing of the collected activity data to obtain the preprocessed data includes: performing format conversion, data completion and useless information deletion operations on the collected data to obtain the preprocessed data.
[0076] The quantizing the preprocessed data to obtain the quantized data includes: performing absolute evaluation and / or relative evaluation on the preprocessed data to obtain the quantized data.
[0077] Furthermore, the data quantization rules are as follows:
[0078] Relative evaluation is calculated based on the data types of times, duration and number of questions. The data types of times include but are not limited to the number of quick answers and submissions. The duration refers to the length of time students spend studying the course. The number of questions includes but is not limited to the number of wrong questions and the number of exercises. Suppose the numerical change of a unit student is a, the total value of n accumulated times is A, and the reference value is M, then the quantitative result L1 is:
[0079] in M = {Max (number of times), Max (duration), total number of questions}
[0080] According to the quantitative result L1, refer to the following Table 1 to determine the score of the evaluation index.
[0081] Table 1
[0082]
[0083] For absolute value data, an absolute evaluation calculation method is used. The absolute value data includes but is not limited to learning progress, homework scores and test scores, which also have quantitative results. According to the calculation results, refer to Table 2 below to determine the evaluation index scores.
[0084] Table 2
[0085]
[0086] In one embodiment of the present application, after obtaining the quantitative data of each of the above evaluation indicators, the data of the evaluation indicators belonging to the process evaluation are visualized. The process evaluation indicators include but are not limited to the number of learning times, learning time, class participation, practical training, homework and examination results.
[0087] In one embodiment of the present application, determining the weight distribution of the evaluation indicators in the learning evaluation framework to obtain the weight of each evaluation indicator includes:
[0088] Determine the dominance relationship of each evaluation indicator in the evaluation framework;
[0089] Constructing a judgment matrix of the next layer of indicators to the previous layer of indicators based on the dominance relationship;
[0090] The weight of each indicator in the next layer of indicators to the previous layer of indicators is determined according to the judgment matrix.
[0091] Specifically, firstly, a multi-level hierarchical structural model is constructed according to the dominance relationship:
[0092] Reference Figure 2Taking the three-layer evaluation index system established from top to bottom as an example, the top layer of the evaluation system is the target A, the middle layer is the criterion layer (B1, B2, B3 and B4), and the bottom layer is the lowest layer (C1, C2, C3...C11). And the criterion layer and the bottom layer in the evaluation index system have the following corresponding relationship:
[0093] B1: C1, C2 and C3; B2: C4; B3: C5, C6, C7 and C8; B4: C9, C10 and C11.
[0094] Secondly, construct a pairwise comparison judgment matrix
[0095] For the elements at the same level, they are compared pair by pair based on the elements at the previous level to establish a judgment matrix.
[0096] Use a ij Indicates element B i With element B j The degree of influence on target A, the judgment matrix of target layer A on criterion layer B is:
[0097]
[0098]
[0099] Furthermore, if the number of elements B is n, the constructed judgment matrix is as follows
[0100] A <![CDATA[B1]]> <![CDATA[B2]]> … <![CDATA[B n <!-- 6 -->]]> <![CDATA[B1]]> <![CDATA[a 11 ]]> <![CDATA[a 12 ]]> … <![CDATA[a 1n ]]> <![CDATA[B2]]> <![CDATA[α 21 ]]> <![CDATA[a 22 ]]> … <![CDATA[a 2n ]]> … … … <![CDATA[a ij ]]> … <![CDATA[B n ]]> <![CDATA[a n1 ]]> <![CDATA[a n2 ]]> … <![CDATA[a nn ]]>
[0101] Matrix A=(a ij ) is the factor judgment matrix, and the ratio of the relative importance of the i element to the j element to the target is expressed as a ij It means that the larger the ratio is, the higher the importance of i is.
[0102] In order to quantify the judgment, the 9-scale method was used to determine the relative importance of each factor.
[0103] Scale Definition and Explanation 1 Two elements have the same importance for a property 3 When comparing two elements, the first element is slightly more important than the second element. 5 When comparing two elements, the first element is obviously more important than the second element. 7 When comparing two elements, the first element is much more important than the second element. 9 When comparing two elements, the first element is extremely important than the second element. 2,4,6,8 The importance of the previous element to the next element is between the specified standards.
[0104] Again, calculate the weight:
[0105] Use the root-finding method to calculate the approximate value of the eigenvector of the judgment matrix:
[0106]
[0107] Then, the feature vector is normalized to obtain the weight vector W = (W1, W2, ... W n ) T
[0108] Finally, perform a consistency check:
[0109] In order to ensure the correctness and rationality of the weights, a consistency check is required:
[0110] Consistency Index in
[0111] The larger n is, the greater the error of the consistency index CI is. Therefore, the random consistency ratio CR is introduced during the test. When n=1,2,…15, the value of RI is:
[0112] Order n 1 2 3 4 5 6 7 8 9 RI 0 0 0.52 0.89 1.12 1.26 1.36 1.41 1.46 Order n 10 11 12 13 14 15 RI 1.49 1.52 1.54 1.56 1.58 1.59
[0113] When the random consistency ratio , the calculated hierarchical ranking weights are considered correct and reasonable, otherwise the judgment matrix needs to be readjusted until the consistency test is qualified.
[0114] The present invention takes the first three evaluation indicators of the result evaluation as an example, and continues to use the 9-indicator method. The specific application implementation is as follows:
[0115] Reference Figure 3 , construct a multi-level hierarchical structural model:
[0116] The target layer is the result evaluation; the criterion layer is professional knowledge, professional skills, professional ability and professional quality; the bottom layer is a number of secondary evaluation indicators, and the inclusion relationship between the layers is as follows:
[0117] The result evaluation includes four first-level evaluation indicators: professional knowledge, professional skills, professional ability, and professional quality. The four result evaluation indicators are composed of several second-level evaluation indicators:
[0118] Professional knowledge, including three secondary evaluation indicators: professional learning, consolidation and reinforcement, and learning attitude;
[0119] Professional skills, including a secondary evaluation indicator of applying what you have learned;
[0120] Professional ability, including four secondary evaluation indicators: expression ability, independent thinking, teamwork, and self-improvement;
[0121] Professionalism includes three secondary evaluation indicators: sense of responsibility, sense of participation, and review and reflection.
[0122] Construct a pairwise comparison judgment matrix and calculate the weight ratio and consistency test.
[0123] The following tables show examples of judgment matrices:
[0124] Result evaluation-professional knowledge / professional skills / professional ability / professional quality judgment matrix 1
[0125]
[0126] Professional knowledge-professional learning / consolidation and reinforcement / learning attitude judgment matrix 2
[0127]
[0128] Professional ability-expressive ability / independent thinking / teamwork / self-improvement judgment matrix 3
[0129]
[0130] Professionalism-responsibility / sense of participation / review and reflection judgment matrix 4
[0131]
[0132] It should be noted that in actual situations, the composition of the judgment matrix is jointly discussed and decided by enterprise practitioners, front-line teachers, and teaching and research experts.
[0133] In one embodiment of the present application, determining the comprehensive score of the corresponding evaluation index according to the quantitative data and the weight of each evaluation index includes:
[0134] The comprehensive scores of the evaluation indicators of the result evaluation and the decision evaluation are determined according to the weight values and the quantitative data.
[0135] Specifically, a comprehensive scoring calculation model is constructed, and the comprehensive scores of evaluation indicators at all levels of outcome evaluation and decision-making evaluation are calculated using the linear weighted method.
[0136] The calculation formula is: TS is the final score of the result evaluation / decision evaluation, w1 is the weight of the first-level evaluation index, and w 1m is the weight of the mth sub-indicator of the first-level evaluation indicator, L is the number of first-level indicators, and M is the number of sub-indicators.
[0137] Further, using Figure 4 The multi-level model shown demonstrates the composite score calculation process:
[0138] Among them, the top layer of the evaluation system is the result evaluation; the first-level evaluation index includes four elements, represented by B1, B2, B3 and B4 respectively; the second-level evaluation index includes 11 elements, represented by C1, C2, C3...C11 respectively; and the corresponding relationship between the first-level evaluation index and the second-level evaluation index is B1: C1, C2 and C3; B2: C4; B3: C5, C6, C7 and C8; B4: C9, C10 and C11. There are different numbers of secondary indicators directly below each indicator under the second-level evaluation index, and these secondary indicators are collectively referred to as n-level indicators. Each specific indicator in the lower-level rating indicator has a weight relative to its upper-level evaluation indicator.
[0139] M B1 (x n1 p n1 +x n2 p n2 +x n3 p n3 ) c1 +(x n3 p n3 +x n4 p n4 ) c2 +(x n5 p ns ) c3
[0140] M B2 (x n6 p n6 +x n7 p n7 ) c4
[0141] M B3 =(x n8 p n8 +x n9 p n9 +x n10 p n10 ) c5 +(x n11 p n11 +x n12 p n12 +x n13 p n13 ) c6
[0142] +(x n14 p n14 ) c7 +(x n15 p n15 ) c8
[0143] MB4 (x n16 p n16 ) c9 +(x n17 p n17 ) c10 +(x n18 p n18 +x n19 p n19 ) c11
[0144] M 结果性评价 =M B1 P B1 +M B2 P B2 +M B3 P B3 +M B4 P B4
[0145] M is the comprehensive score of the evaluation indicators at a certain level, X is the determined score obtained after the evaluation indicators at level n are quantified according to the rules, and P is the weight of the evaluation indicators at different levels. Based on this calculation method, the comprehensive scores of the evaluation indicators at all levels of the result evaluation and decision-making evaluation can be obtained.
[0146] In one embodiment of the present application, determining the learning situation and evaluation of the student based on the processed data includes:
[0147] Convert the comprehensive score of the result evaluation and the comprehensive score of each evaluation index of the decision evaluation into corresponding grade representation;
[0148] A grade evaluation in a preset grade evaluation set is retrieved according to the grade representation, and the grade evaluation is displayed.
[0149] The evaluation presentation of the result evaluation module takes the first-level evaluation indicators as the final display, extracts the comprehensive score of the first-level evaluation indicators of the result evaluation, converts it into a grade representation, and constructs a grade evaluation set:
[0150] V1=(v1,2,v3,v4,v5,v6,v7,v8,v9)
[0151] =(Excellent++, Excellent+, Excellent, Good++, Good+, Good, Fair+, Fair, Poor)
[0152] =(96~100,91~95,86~90,81~85,76~80,71~75,66~70,61~65,0~60)
[0153] The final presentation of the decision-making evaluation module includes summary evaluation and suggestion guidance. The summary evaluation takes the entire module as the final presentation, extracts the comprehensive score of the module, converts it into a grade representation, and constructs a summary evaluation set:
[0154] V2=(v1,v2,v3,v4)
[0155] =(Excellent, Good, Fair, Poor)
[0156] =(86~100,71~85,61~70,0~60)
[0157] Furthermore, when the grades obtained by the decision-making evaluation modules are the same, the grade combinations of the first-level evaluation indicators of the decision-making evaluation, namely, solid foundation, refined skills, and strong quality, will be different. To achieve accurate evaluation, it is necessary to evaluate each grade combination. To this end, it is necessary to calculate each real situation in order to facilitate the next step of constructing a summary comment set and reduce the workload of writing comments for non-existent situations.
[0158] For example, the decision-making evaluation includes three first-level evaluation indicators: strengthening the foundation, refining skills, and strengthening quality. Each of the three first-level evaluation indicators is composed of several second-level evaluation indicators:
[0159] Lay a solid foundation, including three secondary evaluation indicators: professional learning, consolidation and reinforcement, and learning attitude;
[0160] Skills, including a secondary evaluation indicator of learning and application;
[0161] Strong quality, including five secondary evaluation indicators: expression ability, independent thinking, teamwork, self-improvement, and professional attitude.
[0162] Use permutations, combinations and limit values to judge the combination situation.
[0163] The three first-level evaluation indicators of the decision-making evaluation module, namely, solid foundation, refined skills, and strong quality, have the following level combinations: , construct judgment formula
[0164] Minimum score ≤ X 夯 P 夯 +X 精 P 精 +X 强 P 强 ≤highest score
[0165] By matching the scores with the summary evaluation set V2, we can get
[0166] Low level ≤X 夯 P 夯 +X 精 P 精 +X 强 P强 ≤High Grade
[0167] The levels between the low level and the high level (including this number) are the levels that the combination may eventually obtain. By eliminating some unreal situations, 148 real situations are calculated.
[0168] The suggested guidelines are based on the secondary evaluation indicators of decision-making evaluation as the final display, extract the final scores of the secondary evaluation indicators of decision-making evaluation, and convert them into grade representation.
[0169] Construct a graded comment set for result evaluation, a summary comment set for decision evaluation, and a set of suggested guidelines to form an extraction model:
[0170] For the graded comment set of the result evaluation, the corresponding comments are extracted from the graded comment set according to the grades obtained by the first-level evaluation indicators of the result evaluation and displayed;
[0171] For the summary comment set of decision-making evaluation, according to the comprehensive score level obtained by the decision-making evaluation module and the level combination of the first-level evaluation indicators, the corresponding comments are extracted from the summary comment set for display;
[0172] For the set of recommended guidelines for decision-making evaluation, based on the secondary evaluation indicators of decision-making evaluation, improvement suggestions are given for each secondary evaluation indicator with a grade below good (including good).
[0173] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in the application, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.
[0174] Figure 5 A structural diagram of a task processing device provided in an embodiment of the present application is shown in FIG. Figure 5 As shown, the device may include: a data acquisition module 201, a data processing module 202, and an evaluation module 203, wherein the main functions of each component module are as follows:
[0175] Data acquisition module: used to obtain students' activity data on the teaching platform system according to the needs of the preset learning evaluation framework;
[0176] Data processing module: used to process the activity data according to preset rules to obtain processed data;
[0177] Evaluation module: used for determining the learning situation and evaluation of the student based on the processed data.
[0178] According to the specific embodiments provided in this application, the technical solution provided in this application can have the following advantages:
[0179] By obtaining the student's activity data on the teaching platform system according to the needs of the preset learning evaluation framework; processing the activity data according to the preset rules to obtain the processed data; and determining the learning situation and evaluation of the student based on the processed data. In this application, based on the learning evaluation framework determined in advance by professionals, it can effectively integrate process evaluation and result evaluation, value-added evaluation and comprehensive evaluation, and can conduct in-depth and scientific analysis of students' learning performance based on their operational behaviors and learning data, so as to make accurate evaluations and give constructive suggestions, which can well help students understand their own learning situation and adjust their learning status and strategies.
[0180] Combination Figure 6 As shown, in one embodiment of the present application, the data processing module 202 includes:
[0181] Preprocessing module 301: used to preprocess the collected activity data to obtain preprocessed data;
[0182] Data quantization module 302: used to perform quantization processing on the pre-processed data to obtain quantized data;
[0183] A weight calculation module 303 is used to determine the weight distribution of the evaluation indicators in the learning evaluation framework and obtain the weight of each evaluation indicator;
[0184] The comprehensive score determination module 304 is used to determine the comprehensive score of the corresponding evaluation index according to the quantitative data and the weight of each evaluation index.
[0185] Combination Figure 7 As shown, in one embodiment of the present application, the evaluation module 203 includes:
[0186] The grade expression determination module 401 is used to convert the comprehensive score of the result evaluation and the comprehensive score of each evaluation index of the decision evaluation into a corresponding grade expression;
[0187] The evaluation determination and display module 402 is used to retrieve the grade evaluation in the preset grade evaluation set according to the grade representation, and display the grade evaluation.
[0188] The same or similar parts between the above embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. Especially for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.
[0189] It should be noted that the embodiments of the present application may involve the use of user data. In actual applications, user-specific personal data can be used in the scheme described in this article within the scope permitted by applicable laws and regulations, provided that the applicable laws and regulations of the country are met (for example, the user's explicit consent, effective notification to the user, and the user's explicit authorization, etc.).
[0190] According to an embodiment of the present application, the present application also provides a computer device and a computer-readable storage medium. The computer device includes at least one processor and a memory connected to the at least one processor in communication; wherein the memory stores computer instructions that can be executed by the at least one processor, and the computer instructions are executed by the at least one processor so that the at least one processor can execute the task processing method described in any of the above embodiments.
[0191] like Figure 8 , is a block diagram of a computer device according to an embodiment of the present application. The computer device is intended to represent various forms of digital computers or mobile devices. The digital computer may include a desktop computer, a portable computer, a workbench, a personal digital assistant, a server, a mainframe computer, and other suitable computers. The mobile device may include a tablet computer, a smart phone, a wearable device, and the like.
[0192] like Figure 8 As shown, the computer device 500 includes a computing unit 501, a ROM 502, a RAM 503, a bus 504, and an input / output (I / O) interface 505, and the computing unit 501, the ROM 502, and the RAM 503 are connected to each other through the bus 504. The input / output (I / O) interface 505 is also connected to the bus 504.
[0193] The computing unit 501 can perform various processes in the method embodiment of the present application according to the computer instructions stored in the read-only memory (ROM) 502 or the computer instructions loaded from the storage unit 508 to the random access memory (RAM) 503. The computing unit 501 can be various general and / or special processing components with processing and computing capabilities. The computing unit 501 may include, but is not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. In some embodiments, the method provided in the embodiment of the present application can be implemented as a computer software program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 508.
[0194] RAM 503 may also store various programs and data required for the operation of device 500. Part or all of the computer program may be loaded and / or installed on device 500 via ROM 502 and / or communication unit 509.
[0195] The input unit 506, output unit 507, storage unit 508 and communication unit 509 in the computer device 500 can be connected to the I / O interface 505. The input unit 506 can be, for example, a keyboard, a mouse, a touch screen, a microphone, etc. The output unit 507 can be, for example, a display, a speaker, an indicator light, etc. The device 500 can exchange information, data, etc. with other devices through the communication unit 509.
[0196] It should be noted that the device may also include other components necessary for normal operation, or may only include components necessary for implementing the solution of the present application, rather than all the components shown in the figure.
[0197] Various implementations of the systems and techniques described herein can be realized in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof.
[0198] The computer instructions for implementing the method of the present application can be written in any combination of one or more programming languages. These computer instructions can be provided to the computing unit 501, so that when the computer instructions are executed by the computing unit 501 such as a processor, the computer instructions are executed in the method embodiment of the present application.
[0199] each step.
[0200] The present application also provides a computer-readable storage medium on which computer instructions are stored, and the computer instructions are used to enable a computer to execute the task processing method described in any of the above embodiments.
[0201] The computer-readable storage medium provided in the present application may be a tangible medium that may contain or store computer instructions for executing the steps involved in the method embodiments of the present application. The computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, and other forms of storage media.
[0202] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.
Claims
1. A learning evaluation method, characterized in that: The method includes: Obtain students' activity data on the teaching platform system according to the needs of the preset learning evaluation framework; Processing the activity data according to preset rules to obtain processed data; A learning situation and evaluation for the student is determined based on the processed data.
2. A learning evaluation method according to claim 1, characterized in that: The acquisition of student activity data on the teaching platform system specifically includes: Data on students’ operation of digital resources, participation in classroom activities, independent learning and homework completion, and practical training.
3. A learning evaluation method according to claim 1 or 2, characterized in that: The activity data is processed according to a preset rule to obtain the processed data, which specifically includes: Preprocessing the collected activity data to obtain preprocessed data; Performing quantization processing on the preprocessed data to obtain quantized data; Determine the weight distribution of the evaluation indicators in the learning evaluation framework to obtain the weight of each evaluation indicator; The comprehensive score of the corresponding evaluation index is determined according to the quantitative data and the weight of each evaluation index.
4. A learning evaluation method according to claim 3, characterized in that: After obtaining the quantitative data, the method further includes: Visualization processing is performed on data belonging to evaluation indicators in the quantitative data.
5. A learning evaluation method according to claim 3, characterized in that: The collected activity data is preprocessed, and the preprocessed data includes: The collected data are subjected to format conversion, data completion and useless information deletion operations to obtain preprocessed data.
6. A learning evaluation method according to any one of claims 3 to 5, characterized in that: The quantizing the preprocessed data to obtain quantized data comprises: The pre-processed data is subjected to absolute evaluation and / or relative evaluation processing to obtain quantitative data.
7. A learning evaluation method according to any one of claims 3 to 5, characterized in that: Determining the weight distribution of the evaluation indicators in the learning evaluation framework to obtain the weight of each evaluation indicator includes: Determine the dominance relationship of each evaluation indicator in the evaluation framework; Constructing a judgment matrix of the next layer of indicators to the previous layer of indicators based on the dominance relationship; The weight of each indicator in the next layer of indicators to the previous layer of indicators is determined according to the judgment matrix.
8. A learning evaluation method according to claim 7, characterized in that: Determining the comprehensive score of the corresponding evaluation index according to the quantitative data and the weight of each evaluation index includes: The comprehensive scores of the evaluation indicators of the result evaluation and the decision evaluation are determined according to the weight values and the quantitative data.
9. A learning evaluation method according to claim 7, characterized in that: Determining the learning situation and evaluation of the student based on the processed data includes: Convert the comprehensive score of the result evaluation and the comprehensive score of each evaluation index of the decision evaluation into corresponding grade representation; A grade evaluation in a preset grade evaluation set is retrieved according to the grade representation, and the grade evaluation is displayed.
10. A learning evaluation device, characterized in that: The device includes: Data acquisition module: used to obtain students' activity data on the teaching platform system according to the needs of the preset learning evaluation framework; Data processing module: used to process the activity data according to preset rules to obtain processed data; Evaluation module: used for determining the learning situation and evaluation of the student based on the processed data.
11. A learning evaluation device according to claim 10, characterized in that: The data processing module includes: Preprocessing module: used to preprocess the collected activity data to obtain preprocessed data; Data quantization module: used for quantizing the preprocessed data to obtain quantized data; A weight calculation module is used to determine the weight distribution of the evaluation indicators in the learning evaluation framework and obtain the weight of each evaluation indicator; The comprehensive score determination module is used to determine the comprehensive score of the corresponding evaluation index according to the quantitative data and the weight of each evaluation index.
12. A learning evaluation device according to claim 10 or 11, characterized in that: The evaluation modules include: Grade expression determination module: used to convert the comprehensive score of the result evaluation and the comprehensive score of each evaluation index of the decision evaluation into the corresponding grade expression; The evaluation determination and display module is used to retrieve the grade evaluation in the preset grade evaluation set according to the grade representation, and display the grade evaluation.
13. A computer device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores computer instructions that can be executed by the at least one processor, and the computer instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 9.
14. A computer-readable storage medium having computer instructions stored thereon, characterized in that: The computer instructions are used to cause a computer to execute the method according to any one of claims 1 to 9.