Teaching ability evaluation method, device and equipment

Abstract

The invention relates to the technical field of data processing, and discloses a teaching ability evaluation method, device and equipment, and the method comprises the steps: carrying out the keyword extraction of teaching ability data, and obtaining a keyword vector corresponding to the teaching ability data; inputting the keyword vector into a pre-trained index classification model to obtain an index set corresponding to the teaching ability data; based on the keyword vector and a subdivision data set corresponding to the index set, respectively determining a subdivision vector value corresponding to each evaluation index in the index set; and based on the subdivision vector value corresponding to each evaluation index, determining an evaluation result corresponding to the teaching ability data. Therefore, the keyword vectors corresponding to the teaching ability data are input into the index classification model to obtain the index set, so that the evaluation indexes corresponding to the teaching ability data can be determined without manual data processing, and meanwhile, the teaching ability can be evaluated according to the corresponding evaluation indexes for different teaching ability data; and personalized and accurate teaching ability evaluation is realized.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a teaching ability evaluation method, device and equipment. Background Art

[0002] In order to evaluate the professional ability of teachers and drive them to improve their abilities, the current method is usually to build a teacher professional ability evaluation task library and analysis model library, collect teacher teaching data in a targeted manner, and perform data analysis, and drive teachers to provide ability prompts based on the evaluation results obtained from the analysis. However, this method has a single analysis model, and it is difficult to expand or upgrade the model, and it is impossible to implement personalized ability drive. At the same time, the collected data needs to be processed in an artificial data format before being collected, which is time-consuming and laborious. Summary of the invention

[0003] In view of this, the present invention provides a teaching ability evaluation method, device and equipment to solve the problems of lack of personalization and low processing efficiency in current teacher ability evaluation.

[0004] In a first aspect, the present invention provides a teaching ability evaluation method, the method comprising:

[0005] Acquire teaching ability data, and perform keyword extraction on the teaching ability data to obtain keyword vectors corresponding to the teaching ability data;

[0006] Input the keyword vector into the pre-trained indicator classification model to obtain the indicator set corresponding to the teaching ability data;

[0007] Based on the keyword vector and the segmented data set corresponding to the indicator set, respectively determine the segmented vector value corresponding to each evaluation indicator in the indicator set;

[0008] Based on the segmented vector values ​​corresponding to each evaluation indicator, the evaluation results corresponding to the teaching ability data are determined.

[0009] In an optional implementation, keyword extraction is performed on the teaching ability data to obtain a keyword vector corresponding to the teaching ability data, including:

[0010] Perform word segmentation on the teaching ability data to obtain the keywords contained in the teaching ability data;

[0011] Based on the keywords, search in the keyword vector library to obtain the vector data corresponding to each keyword;

[0012] Data processing is performed on the vector data corresponding to the keyword to obtain the keyword vector.

[0013] In an optional implementation, if the teaching ability data includes multiple pieces of data, the teaching ability data is segmented to obtain keywords included in the teaching ability data, including:

[0014] According to the identity information corresponding to the multiple pieces of data, the teaching ability data is divided into multiple data rows;

[0015] Perform word segmentation on multiple data rows respectively to obtain keywords contained in each data row.

[0016] In an optional implementation, the keyword vector library is obtained in the following manner:

[0017] Obtain a keyword sample data set, and perform word segmentation processing on each sample data in the keyword sample data set to obtain a keyword library;

[0018] Based on the number of occurrences of each keyword in the keyword library in each evaluation indicator, a keyword vector library is determined.

[0019] In an optional implementation, based on the keyword vector and the segmented data set corresponding to the indicator set, determining the segmented vector value corresponding to each evaluation indicator in the indicator set respectively includes:

[0020] Obtain the segmented data set corresponding to each evaluation indicator in the indicator set;

[0021] The number of occurrences of the keyword vector in the segmented data set corresponding to each evaluation indicator is determined respectively, and the segmented vector value corresponding to each evaluation indicator is obtained.

[0022] In an optional implementation, based on the segmented vector values ​​corresponding to each evaluation indicator, determining the evaluation result corresponding to the teaching ability data includes:

[0023] Based on the segmented vector values ​​corresponding to each evaluation indicator, query in the KD tree respectively to obtain the score points corresponding to each evaluation indicator;

[0024] The scoring points are processed to obtain the evaluation results corresponding to the teaching ability data.

[0025] In an optional implementation, the indicator classification model is trained in the following manner:

[0026] Obtain a keyword vector library and a training data set; classify the training data in the training data set according to the evaluation indicators;

[0027] Based on the keyword vector library, the training data in the training data set is segmented to obtain the feature vectors corresponding to each evaluation index;

[0028] Based on the feature vectors corresponding to each evaluation index, the initial classification model is trained to obtain an index classification model.

[0029] In an optional implementation, the segmented data set is obtained in the following manner:

[0030] According to the preset evaluation index, an initial segmentation data set is constructed; the initial segmentation data set includes multiple segmentation points, each segmentation point corresponds to an evaluation index;

[0031] Obtain a keyword sample data set, and based on the keyword sample data set, construct a secondary segmentation data set corresponding to each segmentation point;

[0032] Each secondary segmented data set is segmented to obtain a segmented data set.

[0033] In a second aspect, the present invention provides a teaching ability evaluation device, the device comprising:

[0034] A keyword extraction module is used to obtain teaching ability data and extract keywords from the teaching ability data to obtain keyword vectors corresponding to the teaching ability data;

[0035] An indicator set determination module is used to input the keyword vector into a pre-trained indicator classification model to obtain an indicator set corresponding to the teaching ability data;

[0036] A segmentation vector value determination module is used to determine the segmentation vector value corresponding to each evaluation indicator in the indicator set based on the keyword vector and the segmentation data set corresponding to the indicator set;

[0037] The evaluation result determination module is used to determine the evaluation result corresponding to the teaching ability data based on the segmented vector value corresponding to each evaluation indicator.

[0038] In a third aspect, the present invention provides a computer device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the teaching ability evaluation method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0039] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the teaching ability evaluation method of the first aspect or any corresponding embodiment thereof.

[0040] In a fifth aspect, the present invention provides a computer program product, comprising computer instructions for causing a computer to execute the teaching ability evaluation method of the first aspect or any corresponding embodiment thereof.

[0041] The teaching ability evaluation method provided by the present invention performs keyword extraction on teaching ability data to obtain keyword vectors, and inputs the keyword vectors into an indicator classification model to obtain an indicator set, thereby determining the evaluation indicators corresponding to the teaching ability data without manual data processing, and at the same time separately determining the subdivided vector values ​​corresponding to each evaluation indicator in the indicator set, and thereby determining the evaluation results, so that for different teaching ability data, the teaching ability can be evaluated according to the corresponding evaluation indicators, thereby realizing personalized and precise teaching ability evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 is a flow chart of a teaching ability evaluation method according to an embodiment of the present invention;

[0044] Figure 2 is a flow chart of another teaching ability evaluation method according to an embodiment of the present invention;

[0045] Figure 3 is a flow chart of another teaching ability evaluation method according to an embodiment of the present invention;

[0046] Figure 4 is a flow chart of yet another teaching ability evaluation method according to an embodiment of the present invention;

[0047] Figure 5 is a structural block diagram of a teaching ability evaluation device according to an embodiment of the present invention;

[0048] Figure 6 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0050] In order to evaluate the professional ability of teachers and drive them to improve their abilities, the current method is usually to build a teacher professional ability evaluation task library and analysis model library, collect teacher teaching data in a targeted manner, and perform data analysis, and drive teachers to provide ability prompts based on the evaluation results obtained from the analysis. However, this method has a single analysis model, and it is difficult to expand or upgrade the model, and it is impossible to implement personalized ability drive. At the same time, the collected data needs to be processed in an artificial data format before being collected, which is time-consuming and laborious.

[0051] Based on this, the present invention provides a teaching ability evaluation method, which obtains teaching ability data, and extracts keywords from the teaching ability data to obtain keyword vectors corresponding to the teaching ability data; inputs the keyword vectors into a pre-trained indicator classification model to obtain an indicator set corresponding to the teaching ability data; based on the keyword vectors and the segmented data sets corresponding to the indicator set, the segmented vector values ​​corresponding to each evaluation indicator in the indicator set are respectively determined; based on the segmented vector values ​​corresponding to each evaluation indicator, the evaluation result corresponding to the teaching ability data is determined. In this way, keyword extraction is performed on the teaching ability data to obtain keyword vectors, and the keyword vectors are input into the indicator classification model to obtain an indicator set, so that the evaluation indicators corresponding to the teaching ability data can be determined without manual data processing, and the segmented vector values ​​corresponding to each evaluation indicator in the indicator set are respectively determined, and the evaluation results are determined thereby, so that for different teaching ability data, the teaching ability can be evaluated according to the corresponding evaluation indicators, and personalized and precise teaching ability evaluation can be achieved.

[0052] According to an embodiment of the present invention, an embodiment of a teaching ability evaluation method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0053] In this embodiment, a teaching ability evaluation method is provided, which can be used to evaluate the teaching ability of teachers. Figure 1 is a flow chart of a teaching ability evaluation method according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0054] Step S101, obtaining teaching ability data, and performing keyword extraction on the teaching ability data to obtain a keyword vector corresponding to the teaching ability data.

[0055] In the embodiment of the present invention, the latest updated teaching ability data is obtained, and the teaching ability data includes professional ability data of at least one teacher, such as professional ability data in terms of ability, evaluation, knowledge, and emotion. The teaching ability data is segmented and keywords are extracted to obtain keyword vectors corresponding to the teaching ability data.

[0056] In an optional implementation, if the teaching ability data contains multiple data or data of multiple teachers, word segmentation and keyword extraction are performed on the data of different teachers respectively to obtain a keyword vector corresponding to each teacher.

[0057] Step S102, input the keyword vector into a pre-trained indicator classification model to obtain an indicator set corresponding to the teaching ability data.

[0058] In an embodiment of the present invention, the indicator classification model is pre-trained based on sample data, and the sample data may be historical teaching ability data. The input of the indicator classification model is the keyword vector of the teaching ability data, and the output of the indicator classification model is the evaluation index corresponding to the teaching ability data and the confidence score of the evaluation index. The higher the confidence score of the evaluation index, the more likely the teaching ability data is the data under the evaluation index. The keyword vector is input into the indicator classification model, and a preset number of evaluation indicators with the highest confidence scores in the output of the indicator classification model are obtained to obtain an indicator set corresponding to the teaching ability data.

[0059] In an optional implementation, the indicator classification model is trained in the following manner:

[0060] Step a1, obtain the keyword vector library and training data set.

[0061] Among them, the keyword vector library is obtained by processing the keyword sample data set, which is the teaching ability data of all teachers in history. The training data in the training data set are classified according to the evaluation indicators to form multiple dimensions; the evaluation indicators in the training data set can be classified into multiple categories such as applying for teacher qualifications, publishing papers, publishing topics, scientific research projects, teacher ethics assessment, literary works and academic part-time jobs, thus covering the three-dimensional professional ability of teachers in many aspects such as professional foundation, teaching ability, belief and emotion.

[0062] Step a2: Based on the keyword vector library, the training data in the training data set are segmented to obtain the feature vectors corresponding to each evaluation index.

[0063] Among them, each dimension in the training data set, that is, the training data corresponding to each evaluation indicator, is segmented to obtain the segmentation of each dimension; then the segmentation of each dimension is searched in the keyword vector library to obtain the keyword vector corresponding to each segmentation, and for each dimension, the keyword vector corresponding to the segmentation contained therein is processed to obtain the feature vector corresponding to each dimension, that is, the feature vector corresponding to each evaluation indicator.

[0064] The data processing for the keyword vector may be performed by taking an average value or the like, and no specific limitation is made here.

[0065] Step a3: Based on the feature vectors corresponding to the various evaluation indicators, the initial classification model is trained to obtain an indicator classification model.

[0066] Among them, the initial classification model can be built based on the fastText algorithm, and the feature vectors corresponding to each evaluation index are input into the initial classification model. According to the difference between the classification result output by the initial classification model and the evaluation index corresponding to the input feature vector, the initial classification model is adjusted and optimized to obtain the index classification model.

[0067] Step S103: based on the keyword vector and the segmented data set corresponding to the indicator set, respectively determine the segmented vector value corresponding to each evaluation indicator in the indicator set.

[0068] In the embodiment of the present invention, a corresponding subdivided data set is constructed corresponding to each evaluation indicator; the subdivided data set contains various keywords related to the corresponding evaluation indicator.

[0069] In the embodiment of the present invention, the keyword vector is compared with the segmented data set corresponding to each evaluation indicator in the indicator set, the number of occurrences of the keywords involved in the segmented data set in the keyword vector is determined, and the segmented vector value corresponding to each evaluation indicator is obtained.

[0070] Step S104, determining the evaluation result corresponding to the teaching ability data based on the segmented vector value corresponding to each evaluation indicator.

[0071] In the embodiment of the present invention, the subdivided vector values ​​corresponding to each evaluation index are aggregated and processed to obtain the evaluation result corresponding to the teaching ability data. The specific numerical value of the evaluation result represents the ability score of the teacher corresponding to the teaching ability data. If the teaching ability data contains multiple data, that is, contains data of multiple teachers, the evaluation result contains ability scores equal to the number of teachers.

[0072] In this embodiment, a teaching ability evaluation method is provided, which can be used to evaluate the teaching ability of teachers. Figure 2is a flow chart of another teaching ability evaluation method according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps:

[0073] Step S201, obtaining teaching ability data, and performing keyword extraction on the teaching ability data to obtain a keyword vector corresponding to the teaching ability data.

[0074] Specifically, step S201 includes:

[0075] Step S2011, performing word segmentation processing on the teaching ability data to obtain keywords contained in the teaching ability data.

[0076] In the embodiment of the present invention, the teaching ability data is segmented to obtain a plurality of keywords, and repeated keywords are removed from the obtained keywords to obtain the keywords included in the teaching ability.

[0077] In an optional implementation, if the teaching ability data includes multiple data, when the teaching ability data is segmented, the teaching ability data is firstly divided into multiple data rows according to the identity information corresponding to the multiple data; wherein, one row of data in the teaching ability data forms one data row, each data row corresponds to one teacher, and the specific corresponding teacher can be determined by using his identity information, and the data with the same identity information is integrated to form multiple data rows, and one data row corresponds to one identity information. Then, the multiple data rows are segmented respectively to obtain the keywords contained in each data row.

[0078] Step S2012: Based on the keywords, search in the keyword vector library to obtain vector data corresponding to each keyword.

[0079] In the embodiment of the present invention, the keyword vector library contains vector data corresponding to all possible keywords in the teaching ability data, and the vector data represents the frequency of occurrence of the corresponding keywords in each evaluation. Based on the keywords obtained by the above word segmentation, the keyword vector library is searched to obtain the vector data corresponding to each keyword.

[0080] In an optional implementation, if the teaching ability data is divided into multiple data rows, the keywords contained in the multiple data rows are searched in the keyword vector library to obtain the vector data corresponding to the keywords in each data row.

[0081] In an optional implementation, the keyword vector library is obtained in the following manner:

[0082] Step b1, obtaining a keyword sample data set, and performing word segmentation processing on each sample data in the keyword sample data set to obtain a keyword library.

[0083] The keyword sample data set is the historical teaching ability data. Each sample data in the keyword sample data set is segmented and repeated keywords are removed. The remaining keywords form a keyword library.

[0084] Step b2: determining a keyword vector library based on the number of occurrences of each keyword in the keyword library in each evaluation index.

[0085] Among them, the number of occurrences of each keyword in the keyword library in each evaluation indicator is determined respectively, and the keyword vector corresponding to each keyword is obtained. Each specific value in the keyword vector corresponds to the number of occurrences of the keyword in an evaluation indicator. In different keyword vectors, the values ​​corresponding to the same position are the number of occurrences of different keywords in the same evaluation indicator. The keyword vectors corresponding to each keyword in the keyword library are determined respectively, and summarized to obtain the keyword vector library.

[0086] Step S2013, performing data processing on the vector data corresponding to the keyword to obtain the keyword vector.

[0087] In the embodiment of the present invention, data processing is performed on the vector data corresponding to each keyword, and the data processing may be mean processing to obtain the keyword vector corresponding to the teaching ability data.

[0088] In an optional implementation, if the teaching ability data is divided into multiple data rows, data processing is performed on the vector data of the keywords corresponding to each data row to obtain the keyword vector corresponding to each data row.

[0089] Step S202: Input the keyword vector into the pre-trained index classification model to obtain the index set corresponding to the teaching ability data. Figure 1 Step S102 of the illustrated embodiment will not be described in detail here.

[0090] Step S203: Based on the keyword vector and the segmented data set corresponding to the indicator set, determine the segmented vector value corresponding to each evaluation indicator in the indicator set. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0091] Step S204: Determine the evaluation result corresponding to the teaching ability data based on the segmented vector value corresponding to each evaluation indicator. Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0092] In this embodiment, a teaching ability evaluation method is provided, which can be used to evaluate the teaching ability of teachers. Figure 3 is a flow chart of another teaching ability evaluation method according to an embodiment of the present invention. Figure 3 As shown, the process includes the following steps:

[0093] Step S301, obtain teaching ability data, and extract keywords from the teaching ability data to obtain keyword vectors corresponding to the teaching ability data. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0094] Step S302: Input the keyword vector into the pre-trained index classification model to obtain the index set corresponding to the teaching ability data. Figure 1 Step S102 of the illustrated embodiment will not be described in detail here.

[0095] Step S303: Based on the keyword vector and the segmented data set corresponding to the indicator set, the segmented vector value corresponding to each evaluation indicator in the indicator set is determined respectively.

[0096] Specifically, step S303 includes:

[0097] Step S3031, obtaining the segmented data set corresponding to each evaluation indicator in the indicator set.

[0098] In the embodiment of the present invention, each evaluation indicator has a corresponding subdivided data set, and the subdivided data set includes all keywords involved in the corresponding evaluation indicator.

[0099] In an optional implementation, the segmented data set is obtained in the following manner:

[0100] Step c1: construct an initial segmentation data set based on preset evaluation indicators.

[0101] The preset evaluation index refers to all evaluation indexes, which may be all evaluation indexes involved in the training data set when the index classification model is trained through the training data set. The initial segmentation data set includes multiple segmentation points, each of which corresponds to an evaluation index.

[0102] Step c2: obtaining a keyword sample data set, and constructing a secondary segmentation data set corresponding to each segmentation point based on the keyword sample data set.

[0103] The sample data in the keyword sample data set are classified, the evaluation index corresponding to the sample data is determined, and the sample data is put into the secondary segmentation data set corresponding to the corresponding evaluation index, that is, the sample data is put into the secondary segmentation data set corresponding to the corresponding segmentation point.

[0104] Step c3, performing word segmentation processing on each secondary segmented data set to obtain a segmented data set.

[0105] The data contained in each secondary segmented data set is segmented and repeated keywords are removed to obtain a third-level segmented data set, which is also the segmented data set corresponding to the evaluation index.

[0106] Step S3032, determining the number of occurrences of the keyword vector in the segmented data set corresponding to each evaluation indicator, and obtaining the segmented vector value corresponding to each evaluation indicator.

[0107] In an embodiment of the present invention, for the segmented data set corresponding to each evaluation indicator in the indicator set, the keyword vector corresponding to the teaching ability data is determined respectively, the number of occurrences of each keyword in the teaching ability data in each evaluation indicator is determined, and the segmented vector value corresponding to each evaluation indicator is obtained.

[0108] Step S304: Determine the evaluation result corresponding to the teaching ability data based on the segmented vector value corresponding to each evaluation indicator. Figure 1 Step S104 of the illustrated embodiment will not be described in detail here.

[0109] In this embodiment, a teaching ability evaluation method is provided, which can be used to evaluate the teaching ability of teachers. Figure 4 is a flow chart of another teaching ability evaluation method according to an embodiment of the present invention. Figure 4 As shown, the process includes the following steps:

[0110] Step S401, obtain teaching ability data, and extract keywords from the teaching ability data to obtain keyword vectors corresponding to the teaching ability data. Figure 1 Step S101 of the illustrated embodiment will not be described in detail here.

[0111] Step S402: Input the keyword vector into the pre-trained index classification model to obtain the index set corresponding to the teaching ability data. Figure 1 Step S102 of the illustrated embodiment will not be described in detail here.

[0112] Step S403: Based on the keyword vector and the segmented data set corresponding to the indicator set, determine the segmented vector value corresponding to each evaluation indicator in the indicator set. Figure 1 Step S103 of the illustrated embodiment will not be described in detail here.

[0113] Step S404, determining the evaluation result corresponding to the teaching ability data based on the segmented vector value corresponding to each evaluation indicator.

[0114] Specifically, step S404 includes:

[0115] Step S4041, based on the segmented vector value corresponding to each evaluation indicator, query in the KD tree respectively to obtain the score point corresponding to each evaluation indicator.

[0116] In the embodiment of the present invention, the segmented vector values ​​corresponding to the respective evaluation indicators are respectively put into the constructed KD tree as points, and a query search is performed at the same time to find out the least similar preset number of points as the scoring points.

[0117] Step S4042, performing data processing on the scoring points to obtain evaluation results corresponding to the teaching ability data.

[0118] In the embodiment of the present invention, data processing is performed on the scoring points, such as weighted processing, average processing, etc., and the obtained value is the evaluation result.

[0119] In an optional implementation, if the evaluation result is within the first interval, it can be considered that the teacher corresponding to the teaching ability data is a teacher with higher ability. At this time, the evaluation indicator with the highest confidence in the indicator set can be used as a driving target to supervise and drive the teacher corresponding to the teaching ability data.

[0120] In an optional implementation, if the evaluation result is within the second interval, it can be considered that the teacher corresponding to the teaching ability data is a teacher with lower ability. At this time, all evaluation indicators in the indicator set can be used as driving targets to supervise and drive the teacher corresponding to the teaching ability data.

[0121] A teaching ability evaluation method provided by an embodiment of the present invention performs keyword extraction on teaching ability data to obtain a keyword vector, and inputs the keyword vector into an indicator classification model to obtain an indicator set, thereby determining the evaluation indicators corresponding to the teaching ability data without manual data processing, and simultaneously determining the subdivided vector values ​​corresponding to each evaluation indicator in the indicator set, and thereby determining the evaluation result, so that for different teaching ability data, the teaching ability can be evaluated according to the corresponding evaluation indicators, thereby realizing personalized and precise teaching ability evaluation.

[0122] In the present embodiment, a teaching ability evaluation device is also provided, and the device is used to realize the above-mentioned embodiment and preferred implementation mode, and the description that has been made is not repeated. As used below, the term "module" can realize the combination of software and / or hardware of predetermined functions. Although the device described in the following embodiments is preferably realized with software, the realization of hardware, or the combination of software and hardware is also possible and conceived.

[0123] This embodiment provides a teaching ability evaluation device, such as Figure 5 As shown, including:

[0124] The keyword extraction module 501 is used to obtain teaching ability data and perform keyword extraction on the teaching ability data to obtain a keyword vector corresponding to the teaching ability data;

[0125] An indicator set determination module 502 is used to input the keyword vector into a pre-trained indicator classification model to obtain an indicator set corresponding to the teaching ability data;

[0126] A segmentation vector value determination module 503 is used to determine the segmentation vector value corresponding to each evaluation indicator in the indicator set based on the keyword vector and the segmentation data set corresponding to the indicator set;

[0127] The evaluation result determination module 504 is used to determine the evaluation result corresponding to the teaching ability data based on the segmented vector value corresponding to each evaluation indicator.

[0128] In an optional implementation, the keyword extraction module 501 includes:

[0129] A word segmentation processing unit, used to perform word segmentation processing on the teaching ability data to obtain keywords contained in the teaching ability data;

[0130] A vector data determination unit, used to search in a keyword vector library based on the keyword to obtain vector data corresponding to each keyword;

[0131] The data processing unit is used to process the vector data corresponding to the keyword to obtain the keyword vector.

[0132] In an optional implementation, if the teaching ability data includes multiple pieces of data, the word segmentation processing unit includes:

[0133] A data division subunit is used to divide the teaching ability data into multiple data rows according to the identity information corresponding to the multiple data;

[0134] The word segmentation processing subunit is used to perform word segmentation processing on multiple data rows respectively to obtain keywords contained in each data row.

[0135] In an optional embodiment, the device further comprises:

[0136] The keyword library acquisition module is used to acquire a keyword sample data set and perform word segmentation processing on each sample data in the keyword sample data set to obtain a keyword library;

[0137] The keyword vector library determination module is used to determine the keyword vector library based on the number of occurrences of each keyword in the keyword library in each evaluation indicator.

[0138] In an optional implementation, the subdivision vector value determination module 503 includes:

[0139] A segmented data set acquisition unit is used to acquire a segmented data set corresponding to each evaluation indicator in the indicator set;

[0140] The segmentation vector value determination unit is used to determine the number of occurrences of the keyword vector in the segmentation data set corresponding to each evaluation indicator, and obtain the segmentation vector value corresponding to each evaluation indicator.

[0141] In an optional implementation, the evaluation result determination module 504 includes:

[0142] A scoring point determination unit is used to query in the KD tree based on the segmentation vector value corresponding to each evaluation indicator to obtain the scoring point corresponding to each evaluation indicator;

[0143] The evaluation result determination unit is used to process the scoring points to obtain the evaluation results corresponding to the teaching ability data.

[0144] In an optional embodiment, the device further comprises:

[0145] The data acquisition module is used to obtain the keyword vector library and the training data set; the training data in the training data set is classified according to the evaluation index;

[0146] The feature vector determination module is used to perform word segmentation processing on the training data in the training data set based on the keyword vector library to obtain the feature vector corresponding to each evaluation index;

[0147] The model training module is used to train the initial classification model based on the feature vectors corresponding to each evaluation index to obtain an index classification model.

[0148] In an optional embodiment, the device further comprises:

[0149] The initial segmentation data set construction module is used to construct an initial segmentation data set according to a preset evaluation index; the initial segmentation data set includes a plurality of segmentation points, each of which corresponds to an evaluation index;

[0150] A secondary segmentation data set construction module is used to obtain a keyword sample data set and construct a secondary segmentation data set corresponding to each segmentation point based on the keyword sample data set;

[0151] The segmented data set construction module is used to perform word segmentation processing on each secondary segmented data set to obtain a segmented data set.

[0152] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0153] The teaching ability evaluation device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0154] The embodiment of the present invention also provides a computer device having the above Figure 5 The teaching ability evaluation device shown.

[0155] See also Figure 6 , Figure 6 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 6 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 6 A processor 10 is taken as an example.

[0156] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0157] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0158] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0159] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0160] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0161] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0162] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0163] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the present invention.

Claims

1. A teaching ability evaluation method, characterized in that: The method comprises: Acquire teaching ability data, and perform keyword extraction on the teaching ability data to obtain a keyword vector corresponding to the teaching ability data; Inputting the keyword vector into a pre-trained indicator classification model to obtain an indicator set corresponding to the teaching ability data; Based on the keyword vector and the segmented data set corresponding to the indicator set, respectively determine the segmented vector value corresponding to each evaluation indicator in the indicator set; Based on the subdivided vector values ​​corresponding to each of the evaluation indicators, the evaluation results corresponding to the teaching ability data are determined.

2. The method according to claim 1, characterized in that The step of extracting keywords from the teaching ability data to obtain keyword vectors corresponding to the teaching ability data includes: Performing word segmentation processing on the teaching ability data to obtain keywords contained in the teaching ability data; Based on the keywords, searching in a keyword vector library to obtain vector data corresponding to each of the keywords; Data processing is performed on the vector data corresponding to the keyword to obtain the keyword vector.

3. The method according to claim 2, characterized in that If the teaching ability data includes multiple pieces of data, the teaching ability data is segmented to obtain keywords included in the teaching ability data, including: According to the identity information corresponding to the plurality of pieces of data, the teaching ability data is divided into a plurality of data rows; The multiple data rows are respectively segmented to obtain keywords contained in each data row.

4. The method according to claim 2, characterized in that: The keyword vector library is obtained in the following way: Acquire a keyword sample data set, and perform word segmentation processing on each sample data in the keyword sample data set to obtain a keyword library; The keyword vector library is determined based on the number of occurrences of each keyword in the keyword library in each evaluation indicator.

5. The method according to claim 1, characterized in that The determining, based on the keyword vector and the segmented data set corresponding to the indicator set, the segmented vector value corresponding to each evaluation indicator in the indicator set includes: Obtaining a subdivided data set corresponding to each evaluation indicator in the indicator set; The number of occurrences of the keyword vector in each segmented data set corresponding to the evaluation index is determined respectively, and the segmented vector value corresponding to each evaluation index is obtained.

6. The method according to claim 1, characterized in that Determining the evaluation result corresponding to the teaching ability data based on the segmented vector value corresponding to each of the evaluation indicators includes: Based on the segmented vector values ​​corresponding to each of the evaluation indicators, queries are performed in the KD tree to obtain the scoring points corresponding to each of the evaluation indicators; Data processing is performed on the scoring points to obtain evaluation results corresponding to the teaching ability data.

7. The method according to claim 1, characterized in that The indicator classification model is trained in the following way: Obtaining a keyword vector library and a training data set; classifying the training data in the training data set according to the evaluation index; Based on the keyword vector library, the training data in the training data set are segmented to obtain feature vectors corresponding to each of the evaluation indicators; Based on the feature vectors corresponding to the evaluation indicators, the initial classification model is trained to obtain the indicator classification model.

8. The method according to claim 1, characterized in that The segmented data set is obtained in the following way: According to the preset evaluation index, an initial segmentation data set is constructed; the initial segmentation data set includes a plurality of segmentation points, each of which corresponds to an evaluation index; Acquire a keyword sample data set, and construct a secondary segmentation data set corresponding to each segmentation point based on the keyword sample data set; Word segmentation is performed on each of the secondary segmented data sets to obtain the segmented data sets.

9. A teaching ability evaluation device, characterized in that: The device comprises: A keyword extraction module, used to obtain teaching ability data, and perform keyword extraction on the teaching ability data to obtain a keyword vector corresponding to the teaching ability data; An indicator set determination module, used for inputting the keyword vector into a pre-trained indicator classification model to obtain an indicator set corresponding to the teaching ability data; A segmentation vector value determination module, used to determine the segmentation vector value corresponding to each evaluation indicator in the indicator set based on the keyword vector and the segmentation data set corresponding to the indicator set; An evaluation result determination module is used to determine the evaluation result corresponding to the teaching ability data based on the segmented vector value corresponding to each of the evaluation indicators.

10. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the teaching ability evaluation method according to any one of claims 1 to 8 by executing the computer instructions.

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