CN116258398A - Intelligent assessment method and device for competence of teacher based on interactive teaching situation - Google Patents
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Abstract
The application belongs to the technical field of education, and particularly relates to an intelligent assessment method and device for competence of teachers based on interactive teaching situations. The intelligent evaluation method for the competence of the teacher based on the interactive teaching situation comprises the following steps: displaying a preset teaching scene; acquiring interaction information provided by a teacher for the teaching scene; inputting the interaction information into a preset scoring model to obtain an evaluation score output by the scoring model; wherein the scoring model is a pre-trained model that determines an evaluation score for characterizing teacher ability based on interaction information. So set up, the scheme that this application provided can objective quantization teacher ability.
Description
Technical Field
The application belongs to the technical field of education, and particularly relates to an intelligent assessment method and device for competence of teachers based on interactive teaching situations.
Background
Teacher competence research is paid attention to in education evaluation development in the last twenty years, and accurate evaluation of competence of teacher individuals is significant for recruitment, cultivation, performance, management and the like of teacher teams. Different from the prior teacher qualification test, whether the knowledge or skill possessed by the teacher is qualified or not, the teacher is more qualified or not, whether the capability and the characteristics possessed by an individual are suitable for the occupation of the teacher or not, whether the potential of the teacher to be excellent in the position of the teacher or not is provided, and the teacher qualification test relates to multiple aspects such as personal quality, role positioning, behavior motivation and the like. Thus, teacher competence can be seen as a potential trait of an individual, requiring quantification by a specific measurement method.
But now lacks an objective way to quantify teacher capabilities.
Disclosure of Invention
Therefore, the application provides a teacher competence intelligent assessment method and device based on an interactive teaching situation, and solves the problem that the current method for quantifying the teacher capability lacks objectively at least to a certain extent.
In order to achieve the above purpose, the present application adopts the following technical scheme:
in a first aspect, the present application provides a teacher competence intelligent assessment method based on an interactive teaching situation, including:
displaying a preset teaching scene;
acquiring interaction information provided by a teacher for the teaching scene;
inputting the interaction information into a preset scoring model to obtain an evaluation score output by the scoring model;
wherein the scoring model is a pre-trained model that determines an evaluation score for characterizing teacher ability based on interaction information.
In some embodiments, the interactive information includes audio information, video information, and/or text information when the teacher replies to the teaching scenario.
In some embodiments, obtaining interaction information provided by a teacher for the teaching scenario includes:
shooting video information replied by a teacher aiming at the teaching scene based on a preset camera device;
based on a preset audio acquisition device, acquiring audio information of shooting teachers replying to the teaching scene;
and acquiring text information input by a teacher when replying to the teaching scene.
In some embodiments, the preset teaching scenario includes a plurality of scenarios.
In some embodiments, the evaluation score comprises a score of multiple dimensions;
the dimensions include: at least one of a learning force dimension, a mood intelligence dimension, a problem solving dimension, and a student guiding dimension.
In some embodiments, inputting the interaction information into a preset scoring model to obtain an evaluation score output by the scoring model, including:
performing data representation on the interaction information to obtain feature information corresponding to the interaction information;
and inputting the characteristic information into a preset scoring model to obtain the evaluation score output by the scoring model.
In some embodiments, the scoring model includes: the dimension matching module and the automatic scoring module;
the dimension matching module is used for screening the characteristic information to obtain characteristic information corresponding to each dimension;
and the automatic scoring module scores based on the characteristic information corresponding to each dimension to obtain the score corresponding to each dimension.
In some embodiments, the pre-training process of the scoring model includes:
acquiring interaction information of a preset quantity and expert annotation information corresponding to each interaction information as training samples;
and training the pre-built deep learning model based on the training sample to obtain a scoring model. In a second aspect, the present application provides an intelligent assessment device for competence of a teacher based on an interactive teaching context, including:
the display module is used for displaying preset teaching scenes;
the acquisition module is used for acquiring interaction information provided by a teacher for the teaching scene;
the evaluation module is used for inputting the interaction information into a preset scoring model to obtain evaluation scores output by the scoring model;
wherein the scoring model is a pre-trained model that determines an evaluation score for characterizing teacher ability based on interaction information.
In a third aspect, the present application provides a computer readable storage medium storing a computer program for executing the above-described intelligent assessment method of teacher competence based on interactive teaching context.
In a fourth aspect, the present application provides an electronic device, comprising:
a processor;
a memory for storing the processor-executable instructions;
the processor is used for executing the intelligent assessment method for the competence of the teacher based on the interactive teaching situation.
In the scheme provided by the application, a preset teaching scenario is displayed for a teacher to answer, and then interaction information provided by the teacher for the teaching scenario is obtained; the method comprises the steps that answers given by teachers aiming at teaching scenes are obtained, and then the interaction information is input into a preset scoring model to obtain evaluation scores output by the scoring model; the scoring model is a model which is trained in advance and used for determining the evaluating score used for representing the teacher capability based on the interaction information. So configured, the present application provides a method that can objectively quantify teacher's ability.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart illustrating a teacher competency intelligent assessment method based on an interactive teaching context, according to an example embodiment.
FIG. 2 is a flow chart illustrating a teacher competency intelligent assessment method based on an interactive teaching context, according to an example embodiment.
FIG. 3 is a flow chart illustrating a teacher competency intelligent assessment method based on an interactive teaching context, according to an example embodiment.
FIG. 4 is a flow chart illustrating a teacher competency intelligent assessment device based on an interactive teaching context, according to an example embodiment.
Fig. 5 is a schematic diagram of an electronic device according to an exemplary embodiment.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail below. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, based on the examples herein, which are within the scope of the protection sought by those of ordinary skill in the art without undue effort, are intended to be encompassed by the present application.
Referring to fig. 1, fig. 1 is a flowchart illustrating a method for intelligently evaluating teacher competence based on an interactive teaching context according to an exemplary embodiment, the method for intelligently evaluating teacher competence based on an interactive teaching context includes the following steps:
step S110, displaying a preset teaching scene;
specifically, the preset teaching scenario is a scenario set manually and used for detecting the capability of a teacher, and specifically comprises the following steps:
for example: the preset teaching scenes comprise: the students learn to slide down quickly in the near term, the homework is very serious, and you can feel that she thinks about learning in the period of time obviously. Beautiful parents are doing catering business, and are busy at ordinary times and have no time to manage her. You clearly have some problems with the beautiful home teaching mode, preparing for her home visit, he says when reserving visit time with her father: "I don't have time to call you't you's bar" for me week. "how do you do in this case?
In practical applications, a teacher may be presented with teaching scenarios similar to the examples described above. The teacher can freely answer. After the teacher finishes the reply of one preset teaching scene, another preset teaching scene can be displayed for the teacher to reply. And circulating in this way, the teacher continuously replies to a plurality of teaching scenes.
Step S120, obtaining interaction information provided by a teacher for the teaching scene;
it should be noted that, the scheme provided in the present application adopts an open evaluation, and the open evaluation is different from the traditional evaluation mode in that a specified option is required to be provided for a testee to select, but the testee freely describes own behavior mode, and in the system, a testee needs to face a camera to dictate a coping strategy, and convert the coping strategy into video, audio and text data to perform multi-mode analysis).
Specifically, the interactive information includes audio information, video information and/or text information when the teacher replies to the teaching scene. In the scheme provided by the application, any interactive information which can be acquired as much as possible, so that the teacher can be evaluated more comprehensively.
For example, in a specific application scenario, a teacher may dictate a reply to the teaching scenario, and then a preset camera device or an audio acquisition device acquires audio information and video information of the teacher reply. The acquired audio information can be converted into text information through the technical scheme of voice recognition, and the text information input by a teacher can be acquired through a preset man-machine interaction device.
Step S130, inputting the interaction information into a preset scoring model to obtain an evaluation score output by the scoring model;
wherein the scoring model is a pre-trained model that determines an evaluation score for characterizing teacher ability based on interaction information.
It should be noted that the scoring model is trained in advance based on the existing scoring strategy, and the obtained scoring model can score teachers effectively, objectively and fairly.
At present, the number of effective teacher competence assessment tools in China is relatively small, mainly based on a Chen Shiliang table and a multi-item selection situation judgment test, such as teacher competence feature test, college teacher competence feature questionnaire, junior middle school office competence investigation questionnaire, infant teacher competence feature self-assessment questionnaire and the like. The evaluation dimension based on the self-aging scale lacks unified standards, and social acceptance is difficult to avoid, so that the reliability of the evaluation is in dispute. The situation judgment test enables the testee to sort the listed behavior options or select the most effective reaction mode by describing the problem situation related to the work to the testee. With greater resistance to fraud, the context judgment test is considered more suitable for evaluating the competence performance of an individual in operation. However, it is difficult for a situation judgment test to measure a single concept, and unreasonable settings in behavior option programming and scoring logic determination can make the result susceptible to guessing, often with low credibility and difficult guarantee of effectiveness. Meanwhile, the situation judgment test based on the multiple selection forms does not completely avoid social competence, and a tested person can easily obtain prompts from options so as to preferentially answer in a high-interest scene, so that high-competence individuals are difficult to distinguish effectively.
In the scheme provided by the application, based on the teacher competence assessment of the open type situation test, the situation test questions of the open type answer are set based on the unified teacher competence dimension. The tested teacher answers the questions in text form, and researchers study the evaluation of the answers by text automated scoring techniques. Specifically, a preset teaching scenario is displayed for a teacher to answer, and then interaction information provided by the teacher for the teaching scenario is obtained; the method comprises the steps that answers given by teachers aiming at teaching scenes are obtained, and then the interaction information is input into a preset scoring model to obtain evaluation scores output by the scoring model; wherein the scoring model is a pre-trained model that determines an evaluation score for characterizing teacher ability based on interaction information. So set up, the scheme that this application provided can objective quantization teacher ability.
In practical application, in order to evaluate all the capabilities of a teacher more comprehensively, the evaluation score in the scheme provided by the application comprises scores of multiple dimensions; the dimensions include: at least one of a learning force dimension, a mood intelligence dimension, a problem solving dimension, and a student guiding dimension. When the evaluation is specifically performed, the capability of the teacher can be evaluated according to each dimension, namely, the capability of the teacher is scored according to each dimension, so that the capability of the teacher is determined according to each dimension, and the capability of the teacher is evaluated more comprehensively.
Specifically, referring to fig. 2, inputting the interaction information into a preset scoring model to obtain an evaluation score output by the scoring model, including:
step S210, performing data representation on the interaction information to obtain feature information corresponding to the interaction information;
it should be noted that the purpose of the feature information is mainly to convert the information into data which is convenient for computer processing, extract the feature of the interaction information, and pave for the subsequent data processing.
And S220, inputting the characteristic information into a preset scoring model to obtain the evaluation score output by the scoring model.
It should be noted that, the specific scoring process is performed by a scoring model, and in the process, the participation of people is reduced, so that the scoring process is more objective and fair.
Specifically, the scoring model includes: the dimension matching module and the automatic scoring module;
the dimension matching module is used for screening the characteristic information to obtain characteristic information corresponding to each dimension; and the automatic scoring module scores based on the characteristic information corresponding to each dimension to obtain the score corresponding to each dimension.
Specifically, the evaluation score includes a score of a plurality of dimensions; the dimensions include: at least one of a learning force dimension, a mood intelligence dimension, a problem solving dimension, and a student guiding dimension. By the arrangement, the scheme provided by the application can evaluate the capability of a teacher more comprehensively.
The matching of dimensions should be based on the principle of consistency and complementarity, i.e. feature data closely related to a certain dimension is matched with the dimension in interaction data of a plurality of kinds in a plurality of scenes. If the fundamental frequency fluctuation of the voice and the use of the text word have consistency in evaluating the emotion intelligence dimension of the tested person, the micro expression change characteristics and the text emotion have complementarity in evaluating the emotion intelligence dimension of the tested person, and the like; based on the preset principles, dimension matching is performed.
Specifically, referring to fig. 3, the pre-training process of the scoring model includes:
step S301, acquiring preset quantity of interaction information and expert annotation information corresponding to each interaction information as training samples;
it should be noted that, the expert annotation information is obtained by manually scoring the interaction information of a preset number by related staff based on a preset scoring rule and scoring logic.
Step S302, training the pre-built deep learning model based on the training sample to obtain a scoring model.
It should be noted that, in the scheme provided by the application, the pre-built deep learning model is mainly divided into two parts, one part is a dimension matching module constructed based on a preset rule, and the other part is an automatic scoring module. The part needing training in the scheme provided by the application is mainly an automatic scoring module.
By means of the setting, the scoring model obtained through training can score the ability of the teacher in different measurement dimensions so as to accurately and objectively reflect the ability of the teacher.
Specifically, the scheme provided by the application can be realized by adopting the hardware of the B/S architecture, so that the application can be realized on any computer equipment provided with browser software. The software layer, the front end adopts a Vue frame, the rear end adopts a flash frame of Python language, and the database adopts MySQL software technology; supporting a hardware layer of software operation, wherein system operation codes and multi-mode data storage are respectively based on an elastic calculation server and an object storage server; the system can call video acquisition equipment and electronic recording equipment which are built in or integrated with a computer to store video, voice and text data information of a measured person in a response process.
Referring to fig. 4, the present application provides an evaluation device, including:
the display module 41 is used for displaying preset teaching scenes;
an obtaining module 42, configured to obtain interaction information provided by a teacher for the teaching scenario;
the evaluation module 43 is configured to input the interaction information into a preset scoring model, and obtain an evaluation score output by the scoring model;
wherein the scoring model is a pre-trained model that determines an evaluation score for characterizing teacher ability based on interaction information.
In some embodiments, the interactive information includes audio information, video information, and/or text information when the teacher replies to the teaching scenario.
In some embodiments, the acquisition module 42 is specifically configured to:
shooting video information replied by a teacher aiming at the teaching scene based on a preset camera device;
based on a preset audio acquisition device, acquiring audio information of shooting teachers replying to the teaching scene;
and acquiring text information input by a teacher when replying to the teaching scene.
In some embodiments, the preset teaching scenario includes a plurality of scenarios.
In some embodiments, the evaluation score comprises a score of multiple dimensions;
the dimensions include: at least one of a learning force dimension, a mood intelligence dimension, a problem solving dimension, and a student guiding dimension.
In some embodiments, the evaluation module is specifically configured to:
performing data representation on the interaction information to obtain feature information corresponding to the interaction information;
and inputting the characteristic information into a preset scoring model to obtain the evaluation score output by the scoring model.
In some embodiments, the scored model includes: the dimension matching module and the automatic scoring module;
the dimension matching module is used for screening the characteristic information to obtain characteristic information corresponding to each dimension;
and the automatic scoring module scores based on the characteristic information corresponding to each dimension to obtain the score corresponding to each dimension.
In some embodiments, the pre-training process of the scoring model includes:
acquiring interaction information of a preset quantity and expert annotation information corresponding to each interaction information as training samples; and training the pre-built deep learning model based on the training sample to obtain a scoring model.
The application also provides a computer readable storage medium storing a computer program for executing the intelligent assessment method of teacher competence based on the interactive teaching context.
Referring to fig. 5, an electronic device of the present application includes: a processor 51 and a memory 52 for storing the processor-executable instructions; the processor 51 is configured to execute the intelligent assessment method for competence of a teacher based on the interactive teaching situation.
Furthermore, the present application provides a computer readable storage medium storing computer instructions for implementing the above-described related methods when executed.
It is to be understood that the same or similar parts in the above embodiments may be referred to each other, and that in some embodiments, the same or similar parts in other embodiments may be referred to.
It should be noted that in the description of the present application, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Furthermore, in the description of the present application, unless otherwise indicated, the meaning of "plurality", "multiple" means at least two.
It will be understood that when an element is referred to as being "mounted" or "disposed" on another element, it can be directly on the other element or intervening elements may also be present; when an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may be present, and further, as used herein, connection may comprise a wireless connection; the use of the term "and/or" includes any and all combinations of one or more of the associated listed items.
Any process or method description in a flowchart or otherwise described herein may be understood as: means, segments, or portions of code representing executable instructions including one or more steps for implementing specific logical functions or processes are included in the preferred embodiments of the present application, in which functions may be executed out of order from that shown or discussed, including in a substantially simultaneous manner or in an inverse order, depending upon the functionality involved, as would be understood by those skilled in the art to which the embodiments of the present application pertains.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.
Claims (10)
1. Intelligent measurement of teacher's competence based on interactive teaching situation, characterized in that includes:
displaying a preset teaching scene;
acquiring interaction information provided by a teacher for the teaching scene;
inputting the interaction information into a preset scoring model to obtain an evaluation score output by the scoring model;
wherein the scoring model is a pre-trained model that determines an evaluation score for characterizing teacher ability based on interaction information.
2. The method for intelligent assessment of teacher competency based on interactive teaching context according to claim 1, characterized in that the interactive information includes audio information, video information and/or text information when teacher replies to the teaching context.
3. The intelligent assessment method for competence of a teacher based on an interactive teaching context according to claim 2, wherein obtaining interaction information provided by the teacher for the teaching context comprises:
shooting video information replied by a teacher aiming at the teaching scene based on a preset camera device;
based on a preset audio acquisition device, acquiring audio information of shooting teachers replying to the teaching scene;
and acquiring text information input by a teacher when replying to the teaching scene.
4. The intelligent assessment method for teacher competence based on interactive teaching context according to claim 1, wherein the preset teaching context comprises a plurality of contexts.
5. The interactive teaching context-based teacher competence intelligent assessment method according to claim 1, characterized in that the assessment score comprises a multi-dimensional score;
the dimensions include: at least one of a learning force dimension, a mood intelligence dimension, a problem solving dimension, and a student guiding dimension.
6. The intelligent assessment method for the competence of a teacher based on an interactive teaching situation according to claim 5, wherein inputting the interactive information into a preset scoring model to obtain an evaluation score output by the scoring model comprises the following steps:
performing data representation on the interaction information to obtain feature information corresponding to the interaction information;
and inputting the characteristic information into a preset scoring model to obtain the evaluation score output by the scoring model.
7. The interactive teaching context-based teacher competence intelligent assessment method according to claim 6, wherein the scored model includes: the dimension matching module and the automatic scoring module;
the dimension matching module is used for screening the characteristic information to obtain characteristic information corresponding to each dimension;
and the automatic scoring module scores based on the characteristic information corresponding to each dimension to obtain the score corresponding to each dimension.
8. The intelligent assessment method for teacher competence based on interactive teaching context according to claim 1, characterized in that the pre-training process of the scoring model comprises:
acquiring interaction information of a preset quantity and expert annotation information corresponding to each interaction information as training samples;
and training the pre-built deep learning model based on the training sample to obtain a scoring model.
9. Intelligent assessment device of teacher's competence based on interactive teaching situation, its characterized in that includes:
the display module is used for displaying preset teaching scenes;
the acquisition module is used for acquiring interaction information provided by a teacher for the teaching scene;
the evaluation module is used for inputting the interaction information into a preset scoring model to obtain evaluation scores output by the scoring model;
the scoring model is a model which is trained in advance and used for determining the evaluating score used for representing the teacher capability based on the interaction information.
10. An electronic device, comprising:
a processor;
a memory for storing the processor-executable instructions;
the processor configured to perform the intelligent assessment method for competence of a teacher based on an interactive teaching context as claimed in any one of claims 1 to 8.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117633587A (en) * | 2023-11-20 | 2024-03-01 | 北京理工大学珠海学院 | Audio and text wide-time category emotion recognition method based on transfer learning |
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| CN117633587A (en) * | 2023-11-20 | 2024-03-01 | 北京理工大学珠海学院 | Audio and text wide-time category emotion recognition method based on transfer learning |
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