CN114064739B - Marking optimization method and device, electronic equipment and readable storage medium - Google Patents
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Abstract
The application discloses a marking optimization method, a marking optimization device, electronic equipment and a readable storage medium, which are applied to the technical field of computers, wherein the marking optimization method comprises the following steps: when detecting that a user is answering, acquiring answering operation data, and storing the answering operation data into a preset metadata file; and when the answer of the user is detected to be finished, matching the answer operation data with a preset marking rule by scanning the preset metadata file to obtain a matching result and outputting the examination result corresponding to the user according to the matching result. The technical problem that examination and examination paper reading efficiency is low in the prior art is solved.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for optimizing scoring, an electronic device, and a readable storage medium.
Background
With the rapid development of science and technology, the development of enterprise resource planning is more and more mature, at present, the enterprise training examination and reading adopts a method of combining machine examination and manual examination, objective questions are automatically read by a machine, and subjective questions are manually read, when subjective questions are manually read, the condition that examination rules are not uniform due to subjective judgment of examiners can occur, and the training content and the examination questions are not fixed.
Disclosure of Invention
The application mainly aims to provide an examination paper marking optimization method, an examination paper marking optimization device, electronic equipment and a readable storage medium, and aims to solve the technical problem that examination paper marking efficiency is low in the prior art.
In order to achieve the above object, the present application provides an examination paper scoring optimization method, including:
when detecting that a user is answering, acquiring answering operation data, and storing the answering operation data into a preset metadata file;
when the answer of the user is detected to be finished, matching the answer operation data with a preset marking rule by scanning the preset metadata file to obtain a matching result;
and outputting the assessment result corresponding to the user according to the matching result.
Optionally, the preset metadata file includes a rule metadata file, a list metadata file, and a table unit data file, the answer operation data is stored as table unit data in the table unit data file,
the step of scanning the preset metadata file to match the answer operation data with a preset marking rule to obtain a matching result comprises the following steps:
scanning the table unit data file to obtain the table unit data;
according to a first mapping relation between table unit data and list metadata, inquiring the list metadata corresponding to the table unit data in the list metadata file;
according to a second mapping relation between the list metadata and the marking rules, inquiring preset marking rules corresponding to the list metadata in the rule metadata file;
and matching the form unit data with the preset marking rule to obtain a matching result.
Optionally, the matching result comprises a first matching result and a second matching result,
the step of matching the form unit data with the preset marking rule to obtain a matching result comprises the following steps:
when detecting that the question corresponding to the list metadata is an objective question, judging whether answer operation in the list unit data is consistent with preset answer operation corresponding to the preset examination rule or not according to the preset examination rule;
if the answer is consistent with the first matching result, judging that the answer of the objective questions is correct, and obtaining a first matching result;
and if the answer is inconsistent, judging that the answer of the objective questions is wrong, and obtaining a second matching result.
Optionally, the step of matching the form unit data with the preset scoring rule to obtain a matching result further includes:
when detecting that the question corresponding to the list metadata is a subjective question, acquiring an answer field in the list unit data;
and calculating semantic similarity between the answer field and a preset field corresponding to the preset marking rule, and generating the matching result according to the semantic similarity.
Optionally, the preset metadata file includes a table unit data file, a list metadata file,
when detecting that the user is answering, acquiring answer operation data and storing the answer operation data into a preset metadata file, wherein the step of acquiring the answer operation data comprises the following steps:
when detecting that a user is answering, acquiring the answering operation data and question data corresponding to the answering operation data, and generating corresponding attribute labels according to the association relationship between the question data and the answering operation data;
and storing the question data and the attribute labels into a list unit data file together, and storing the answer operation data and the attribute labels into the list metadata file together.
Optionally, the preset metadata file comprises a rule metadata file,
before the step of obtaining a matching result by scanning the preset metadata file and matching the answer operation data with a preset scoring rule, the scoring optimization method comprises the following steps:
obtaining assessment content and a user role to which the user belongs;
and configuring corresponding preset marking rules according to the assessment contents and the user roles, and storing the marking rules into the rule metadata file.
Optionally, after the step of configuring a corresponding preset scoring rule according to the assessment content and the user role and storing the preset scoring rule in the rule metadata file, the scoring optimization method further includes:
acquiring current assessment content and a current user role to which the user belongs;
judging whether the current checking content or the current user role is changed or not;
if the change occurs, returning to the execution step: and configuring corresponding preset marking rules according to the assessment contents and the user roles, and storing the preset marking rules into the rule metadata file.
In order to achieve the above object, the present application further provides an examination paper reading optimization device, including:
the data acquisition module is used for acquiring answer operation data when detecting that a user is answering and storing the answer operation data into a preset metadata file;
the data matching module is used for matching the answer operation data with a preset marking rule by scanning the preset metadata file after the answer of the user is detected to be finished, so as to obtain a matching result;
and the result output module is used for outputting the assessment result corresponding to the user according to the matching result.
Optionally, the data matching module is further configured to:
scanning the table unit data file to obtain the table unit data;
according to a first mapping relation between table unit data and list metadata, inquiring the list metadata corresponding to the table unit data in the list metadata file;
according to a second mapping relation between the list metadata and the marking rules, inquiring preset marking rules corresponding to the list metadata in the rule metadata file;
and matching the form unit data with the preset marking rule to obtain a matching result.
Optionally, the data matching module is further configured to:
when detecting that the question corresponding to the list metadata is an objective question, judging whether answer operation in the list unit data is consistent with preset answer operation corresponding to the preset examination rule or not according to the preset examination rule;
if the answer is consistent with the first matching result, judging that the answer of the objective questions is correct, and obtaining a first matching result;
and if the answer is inconsistent, judging that the answer of the objective questions is wrong, and obtaining a second matching result.
Optionally, the data matching module is further configured to:
when detecting that the question corresponding to the list metadata is a subjective question, acquiring an answer field in the list unit data;
and calculating semantic similarity between the answer field and a preset field corresponding to the preset marking rule, and generating the matching result according to the semantic similarity.
Optionally, the data obtaining module is further configured to:
when detecting that a user is answering, acquiring the answering operation data and question data corresponding to the answering operation data, and generating corresponding attribute labels according to the association relationship between the question data and the answering operation data;
and storing the question data and the attribute labels into a list unit data file together, and storing the answer operation data and the attribute labels into the list metadata file together.
Optionally, the data obtaining module is further configured to:
obtaining assessment content and a user role to which the user belongs;
and configuring corresponding preset marking rules according to the assessment contents and the user roles, and storing the marking rules into the rule metadata file.
Optionally, the data obtaining module is further configured to:
acquiring current assessment content and a current user role to which the user belongs;
judging whether the current checking content or the current user role is changed or not;
if the change occurs, returning to the execution step: and configuring corresponding preset marking rules according to the assessment contents and the user roles, and storing the preset marking rules into the rule metadata file.
The present application further provides an electronic device, the electronic device including: the scoring optimization method comprises a memory, a processor and a program of the scoring optimization method stored on the memory and capable of running on the processor, wherein the program of the scoring optimization method can realize the steps of the scoring optimization method when being executed by the processor.
The present application also provides a computer-readable storage medium having stored thereon a program for implementing the scoring optimization method, which when executed by a processor, implements the steps of the scoring optimization method as described above.
The present application also provides a computer program product comprising a computer program which, when executed by a processor, performs the steps of the scoring optimization method as described above.
Compared with the method which combines intelligent system marking and manual marking and is adopted in the prior art, the method obtains answer operation data when detecting that a user answers and stores the answer operation data to a preset metadata file; when the answer of the user is detected to be finished, matching the answer operation data with a preset marking rule by scanning the preset metadata file to obtain a matching result; and outputting the assessment result corresponding to the user according to the matching result. The examination paper marking method has the advantages that the examination paper marking is carried out by adopting the intelligent system in the whole process, the examination paper marking person does not need to be trained, the examination paper marking is carried out through objective judgment of the intelligent system, the condition that examination paper marking rules are inconsistent when the examination paper marking person carries out subjective examination paper marking through subjective judgment is avoided, the technical defect that the examination paper marking time is long due to the fact that the examination paper marking person cannot adapt to the examination rules quickly due to the self limitation of the examination paper marking person when a method combining machine examination paper marking and manual examination paper marking is adopted is overcome, and the examination paper marking efficiency is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic flow chart diagram illustrating a first embodiment of the scoring optimization method of the present application;
fig. 2 is a schematic device structure diagram of a hardware operating environment related to the scoring optimization method in the embodiment of the present application.
The objectives, features, and advantages of the present application will be further described with reference to the accompanying drawings.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
In a first embodiment of the scoring optimization method, referring to fig. 1, the scoring optimization method includes:
step S10, when detecting that the user is answering, acquiring answering operation data, and storing the answering operation data into a preset metadata file;
in this embodiment, it should be noted that the preset metadata file is a preset file for storing metadata, and the preset metadata file includes a list metadata file, a table unit data file, and a rule metadata file, and the preset metadata file may be an XML file, where the XML file is a subset of an extensible markup language and a standard universal markup language. The answer operation data is operation data for users to answer different question types, and the answer operation data comprises answer operation and answer answers. The different question types are different types of questions set according to different assessment purposes, the question types can be subjective questions, objective questions and brief answers, and further the subjective questions can be discussion questions; the objective questions can be selection questions or judgment questions. The answer operation is the operation of answering the objective questions by the user, and the answer operation can be the operation of clicking options and can also be the operation of typing options. The answer is the content of the subjective question answered by the user, and the answer can be the data of the content typed by the user.
Exemplarily, the step S10 includes: when detecting that the user is answering, acquiring the answering operation and answering answer of the user, and when detecting that the user is answering completely, storing the answering operation and answering answer of the user into the table unit data file.
In step S10, when it is detected that the user is answering, the step of obtaining answer operation data and storing the answer operation data in a preset metadata file includes:
step A10, when detecting that a user is answering, acquiring the answering operation data and question data corresponding to the answering operation data, and generating corresponding attribute labels according to the association relationship between the question data and the answering operation data;
step a20, storing the question data and the attribute tag together in a table unit data file, and storing the answer operation data and the attribute tag together in the list metadata file.
In this embodiment, it should be noted that the attribute tags are marks made for the answer operation data and the question data corresponding to the answer operation data, and may generate the same attribute tag for the answer operation data and the question data corresponding to the answer operation data, or generate different attribute tags of the same type for the answer operation data and the question data corresponding to the answer operation data.
As an example, the steps a10 to a20 include: when detecting that a user is answering, judging the question type of the user answering, and when the question is a selection question or a judgment question, acquiring click option operation data of the user and question data corresponding to the click option operation data to generate a first attribute label; storing the click option operation data and the first attribute tag to the table unit data file together; and jointly storing the title data corresponding to the click option operation data and the first attribute tag into the list metadata file.
As an example, the steps a10 to a20 include: when detecting that a user is answering, judging the question type of the user answering, and when the question is a blank filling question or a subjective question, acquiring the user input content data and question data corresponding to the user input content data to generate a second attribute label; storing the user-entered content data and the second attribute tag together in the table unit data file; and jointly storing the title data corresponding to the user-typed content data and the second attribute tag into the list metadata file.
Step S20, when the answer of the user is detected to be finished, matching the answer operation data with a preset marking rule by scanning the preset metadata file to obtain a matching result;
in this embodiment, it should be noted that the rule metadata file includes a preset scoring rule, the preset scoring rule is a rule preset according to a correspondence between table unit data and list unit data, the preset scoring rule may be that one list unit data corresponds to one list unit data, and may also be that one list unit data corresponds to multiple list unit data, when the preset metadata file format is an XML file, the preset scoring rule may be queried through an XPath locator, where XPath is an XML path language and may be used to determine a language of a certain position of the XML.
Exemplarily, the step S20 includes: when the answer of the user is detected to be finished, the answer operation data is obtained by scanning the table unit data file; obtaining question data corresponding to the answer operation data by scanning the list metadata file; inquiring the rule metadata file to obtain a preset marking rule corresponding to the question data; and matching the answer operation data with the preset marking rule according to the preset marking rule and the answer operation data to obtain a matching result.
In step S20, the step of matching the answer operation data with a preset marking rule by scanning the preset metadata file to obtain a matching result includes:
step S21, scanning the table cell data file to obtain the table cell data;
step S22, according to the first mapping relation between the table unit data and the list metadata, inquiring the list metadata corresponding to the table unit data in the list metadata file;
step S23, according to a second mapping relation between the list metadata and the marking rules, inquiring the preset marking rules corresponding to the list metadata in the rule metadata file;
and step S24, matching the form unit data with the preset marking rule to obtain a matching result.
In this embodiment, it should be noted that the answer operation data is stored in the table unit data file as the table unit data. The first mapping relation is a mapping relation between preset list metadata and table unit data and is embodied by attribute labels of the list metadata and the table unit data; the second mapping relation is a mapping relation between preset list metadata and the marking rule.
As one example, steps S21 to S24 include: when the answer of the user is detected to be finished, scanning the table unit data file, and acquiring click option operation data of the user to obtain the table unit data; checking a first attribute label of the table unit data according to the table unit data; querying the list metadata file for list metadata having the first attribute tag; according to a second mapping relation between the list metadata and an examination paper marking rule, inquiring a preset examination paper marking rule corresponding to the list metadata in the rule metadata file; and matching the form unit data with the preset marking rule to obtain a matching result.
As one example, steps S21 to S24 include: when the user answers the questions, scanning the table unit data file, and acquiring the keying-in content data of the user to obtain the table unit data; checking a second attribute label of the table unit data according to the table unit data; querying the list metadata file for list metadata having the second attribute tag; according to a second mapping relation between the list metadata and an examination paper marking rule, inquiring a preset examination paper marking rule corresponding to the list metadata in the rule metadata file; and matching the form unit data with the preset marking rule to obtain a matching result.
In step S24, the step of matching the form unit data with the preset scoring rule to obtain a matching result includes:
step B10, when detecting that the question corresponding to the list metadata is an objective question, judging whether the answer operation in the list unit data is consistent with the preset answer operation corresponding to the preset marking rule according to the preset marking rule;
step B20, if the answer is consistent, judging that the answer of the objective questions is correct, and obtaining a first matching result;
and step B30, if the answer is inconsistent, judging that the answer of the objective questions is wrong, and obtaining a second matching result.
In this embodiment, it should be noted that the matching result is a score value of a question corresponding to the list metadata obtained according to whether the answer operation is consistent with the preset answer operation.
Exemplarily, the step B10 to the step B30 include: when detecting that the question corresponding to the list metadata is an objective question, determining a preset answer operation corresponding to the list metadata according to the preset marking rule; judging whether answer operation in the form unit data is consistent with preset answer operation corresponding to the preset marking rule or not; if the answer operation in the form unit data is consistent with the preset answer operation corresponding to the preset marking rule, judging that the objective question is correctly answered, and counting the full score; and if the answer operation in the table unit data is inconsistent with the preset answer operation corresponding to the preset marking rule, judging that the objective question is answered incorrectly and not scoring.
In step S24, the step of matching the form unit data with the preset scoring rule to obtain a matching result further includes:
step C10, when detecting that the question corresponding to the list metadata is a subjective question, acquiring an answer field in the list unit data;
and step C20, calculating semantic similarity between the answer field and a preset field corresponding to the preset marking rule, and generating the matching result according to the semantic similarity.
In this embodiment, it should be noted that the matching result is a score of a question corresponding to the list metadata obtained according to semantic similarity between the answer field and a preset field corresponding to the preset marking rule.
As an example, the step C10 to the step C20 include: when detecting that the question corresponding to the list metadata is a subjective question, acquiring an answer field in the list unit data; converting the answer field into a first semantic feature according to a preset semantic model; converting a preset field corresponding to the preset marking rule into a second semantic feature according to a preset semantic model; calculating the semantic similarity of the first semantic feature and the second semantic feature, and generating a score value of the subjective question according to the product of the semantic similarity and the score occupied by the subjective question. For example, when the preset main topic occupation score is 4 minutes, and the similarity between the first semantic feature and the second semantic feature is 100%, the score of the main topic is 4; when the similarity between the first semantic feature and the second semantic feature is 50%, calculating the score value of the main topic to be 2; and when the similarity between the first semantic feature and the second semantic feature is 0%, calculating the score value of the main topic as 0. The preset semantic model is a preset language model and is used for extracting semantic features of the text, wherein the semantic features can be feature vectors obtained by converting text features.
As an example, the step C10 to the step C20 include: when detecting that the question corresponding to the list metadata is a subjective question, acquiring an answer field in the list unit data, and determining each similar field set corresponding to a preset field corresponding to the preset marking rule, wherein different semantic similarities exist between the fields in different similar field sets and the preset field; querying the answer fields in each similar field set to determine the similar field set to which the answer fields belong; taking semantic similarity corresponding to a similar field set to which the answer field belongs as target semantic similarity between the answer field and a preset field; and generating a score value of the subjective question according to the product of the target semantic similarity and the score occupied by the subjective question. For example, when the score of the preset subjective question is 4 minutes, and the answer field belongs to the preset first similar field set, the score of the subjective question is 4; when the answer field belongs to a preset second similar field set, calculating the score value of the main question to be 3; when the answer field belongs to a preset third similar field set, calculating the score value of the main question to be 2; when the answer field belongs to a preset fourth similar field set, calculating the score value of the main question as 1; and when the answer field does not belong to the preset similar field set, calculating the score value of the subjective question as 0.
In step S20, the step of obtaining a matching result by scanning the preset metadata file and matching the answer operation data with a preset marking rule includes:
step D10, obtaining assessment content and the user role to which the user belongs;
and D20, configuring corresponding preset marking rules according to the assessment contents and the user roles, and storing the preset marking rules into the rule metadata file.
Exemplarily, the steps D10 to D20 include: and acquiring assessment content and the basic information of the user. Judging the user role to which the user belongs according to the basic information of the user, wherein the examination content can be a question for examining the training quality after enterprise training, and can also be used for filling related information after a client buys a house so as to facilitate after-sale service; the basic information is information for helping to judge the role of the user, and the basic information can be a name or an identity card number; the user role can be a client or an employee, and further, the employee can be a trial employee or a formal employee. And establishing a mapping relation between list metadata and list unit data as the preset marking rule according to the assessment content and the user role, and storing the mapping relation into the rule metadata file.
In step D20, after the step of configuring a corresponding preset scoring rule according to the assessment content and the user role, and storing the rule into the rule metadata file, the method includes:
step E10, obtaining the current assessment content and the current user role to which the user belongs;
step E20, judging whether the current examination content or the current user role is changed;
step E30, if the change occurs, returning to the step: and configuring corresponding preset marking rules according to the assessment contents and the user roles, and storing the preset marking rules into the rule metadata file.
Exemplarily, the step E10 to the step E30 include: acquiring current assessment content and current basic information of the user; judging the current user role to which the user belongs according to the current basic information of the user; and judging whether the current examination content or the current user role is changed or not, if the current examination content or the current user role is changed, establishing a mapping relation between list metadata and list unit data as the preset marking rule according to the current examination content and the current user role, and storing the mapping relation into the rule metadata file.
And step S30, outputting the assessment result corresponding to the user according to the matching result.
Exemplarily, the step S30 includes: and obtaining scores of the subjective questions and the objective questions according to the matching result, integrating the scores of the subjective questions and the objective questions to obtain total scores, and outputting the assessment result corresponding to the user.
Compared with the method which combines intelligent system marking and manual marking and is adopted in the prior art, the method comprises the steps of obtaining answer operation data when a user is detected answering and storing the answer operation data to a preset metadata file; when the answer of the user is detected to be finished, matching the answer operation data with a preset marking rule by scanning the preset metadata file to obtain a matching result; and outputting the assessment result corresponding to the user according to the matching result. The examination paper marking method has the advantages that the examination paper marking is carried out by adopting the intelligent system in the whole process, examination paper marking is carried out through objective judgment of the intelligent system, training on examination paper marking persons is not needed, the condition that examination paper marking rules are inconsistent when the examination paper marking persons carry out subjective examination paper marking through subjective judgment is avoided, the technical defect that the examination paper marking time is long due to the fact that the examination paper marking persons cannot adapt to the examination rules quickly due to the self limitation of the examination paper marking persons when a method of combining machine examination paper marking and manual examination paper marking is adopted is overcome, and examination paper marking efficiency is improved.
Example two
The embodiment of the present application further provides an examination paper reading optimization device, where the examination paper reading optimization device includes:
the data acquisition module is used for acquiring answer operation data when detecting that a user is answering and storing the answer operation data into a preset metadata file;
the data matching module is used for matching the answer operation data with a preset marking rule by scanning the preset metadata file after the answer of the user is detected to be finished, so as to obtain a matching result;
and the result output module is used for outputting the assessment result corresponding to the user according to the matching result.
Optionally, the data matching module is further configured to:
scanning the table unit data file to obtain the table unit data;
according to a first mapping relation between table unit data and list metadata, inquiring the list metadata corresponding to the table unit data in the list metadata file;
according to a second mapping relation between the list metadata and the marking rules, inquiring preset marking rules corresponding to the list metadata in the rule metadata file;
and matching the form unit data with the preset marking rule to obtain a matching result.
Optionally, the data matching module is further configured to:
when detecting that the question corresponding to the list metadata is an objective question, judging whether answer operation in the list unit data is consistent with preset answer operation corresponding to the preset examination rule or not according to the preset examination rule;
if the answer is consistent with the first matching result, judging that the answer of the objective questions is correct, and obtaining a first matching result;
and if the answer is inconsistent, judging that the answer of the objective questions is wrong, and obtaining a second matching result.
Optionally, the data matching module is further configured to:
when detecting that the question corresponding to the list metadata is a subjective question, acquiring an answer field in the list unit data;
and calculating semantic similarity between the answer field and a preset field corresponding to the preset marking rule, and generating the matching result according to the semantic similarity.
Optionally, the data obtaining module is further configured to:
when detecting that a user is answering, acquiring the answering operation data and question data corresponding to the answering operation data, and generating corresponding attribute labels according to the association relationship between the question data and the answering operation data;
and storing the question data and the attribute labels into a list unit data file together, and storing the answer operation data and the attribute labels into the list metadata file together.
Optionally, the data obtaining module is further configured to:
obtaining assessment content and a user role to which the user belongs;
and configuring corresponding preset marking rules according to the assessment contents and the user roles, and storing the marking rules into the rule metadata file.
Optionally, the data obtaining module is further configured to:
acquiring current assessment content and a current user role to which the user belongs;
judging whether the current checking content or the current user role is changed or not;
if the change occurs, returning to the execution step: and configuring corresponding preset marking rules according to the assessment contents and the user roles, and storing the preset marking rules into the rule metadata file.
By adopting the examination paper marking optimization device provided by the invention, the technical problem of low examination paper marking efficiency is solved by adopting the examination paper marking optimization method in the first embodiment. Compared with the prior art, the beneficial effects of the scoring optimization device provided by the embodiment of the invention are the same as the beneficial effects of the scoring optimization method provided by the embodiment, and other technical features of the scoring optimization device are the same as those disclosed by the embodiment method, which are not described herein again.
EXAMPLE III
An embodiment of the present invention provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the scoring optimization method in the first embodiment.
Referring now to FIG. 2, shown is a schematic diagram of an electronic device suitable for use in implementing embodiments of the present disclosure. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., car navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 2 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 2, the electronic device may include a processing apparatus (e.g., a central processing unit, a graphic processor, etc.) that may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) or a program loaded from a storage apparatus into a Random Access Memory (RAM). In the RAM, various programs and data necessary for the operation of the electronic apparatus are also stored. The processing device, the ROM, and the RAM are connected to each other by a bus. An input/output (I/O) interface is also connected to the bus.
Generally, the following systems may be connected to the I/O interface: input devices including, for example, touch screens, touch pads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, and the like; output devices including, for example, Liquid Crystal Displays (LCDs), speakers, vibrators, and the like; storage devices including, for example, magnetic tape, hard disk, etc.; and a communication device. The communication means may allow the electronic device to communicate wirelessly or by wire with other devices to exchange data. While the figures illustrate an electronic device with various systems, it is to be understood that not all illustrated systems are required to be implemented or provided. More or fewer systems may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means, or installed from a storage means, or installed from a ROM. The computer program, when executed by a processing device, performs the above-described functions defined in the methods of the embodiments of the present disclosure.
By adopting the examination paper marking optimization method in the first embodiment, the electronic equipment provided by the invention solves the technical problem of low examination paper marking efficiency. Compared with the prior art, the electronic device provided by the embodiment of the invention has the same beneficial effects as the examination paper optimizing method provided by the first embodiment, and other technical features in the electronic device are the same as those disclosed in the embodiment method, which are not repeated herein.
It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the foregoing description of embodiments, the particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Example four
The present embodiment provides a computer-readable storage medium having computer-readable program instructions stored thereon for performing the method of scoring optimization in the first embodiment.
The computer readable storage medium provided by the embodiments of the present invention may be, for example, a USB flash disk, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or any combination thereof. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present embodiment, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device. Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer-readable storage medium may be embodied in an electronic device; or may be present alone without being incorporated into the electronic device.
The computer readable storage medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: when detecting that a user is answering, acquiring answering operation data, and storing the answering operation data into a preset metadata file; when the answer of the user is detected to be finished, matching the answer operation data with a preset marking rule by scanning the preset metadata file to obtain a matching result; and outputting the assessment result corresponding to the user according to the matching result.
Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present disclosure may be implemented by software or hardware. Wherein the names of the modules do not in some cases constitute a limitation of the unit itself.
The computer-readable storage medium provided by the invention stores computer-readable program instructions for executing the scoring optimization method, and solves the technical problem of low examination scoring efficiency. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the embodiment of the present invention are the same as the beneficial effects of the scoring optimization method provided by the first embodiment, and are not described herein again.
EXAMPLE five
The present application also provides a computer program product comprising a computer program which, when executed by a processor, performs the steps of the scoring optimization method as described above.
The computer program product provided by the application solves the technical problem of low examination and examination efficiency. Compared with the prior art, the beneficial effects of the computer program product provided by the embodiment of the present invention are the same as the beneficial effects of the scoring optimization method provided by the first embodiment, and are not described herein again.
The above description is only a preferred embodiment of the present application, and not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings, or which are directly or indirectly applied to other related technical fields, are included in the scope of the present application.
Claims (9)
1. A scoring optimization method is characterized by comprising the following steps:
when detecting that a user is answering, acquiring answering operation data, and storing the answering operation data into a preset metadata file, wherein the answering operation data comprises answering operation and answering answers;
when the answer of the user is detected to be finished, matching the answer operation data with a preset marking rule by scanning the preset metadata file to obtain a matching result;
outputting the assessment result corresponding to the user according to the matching result;
wherein the preset metadata file comprises a rule metadata file, a list metadata file and a table unit data file, the answer operation data is stored in the table unit data file as table unit data,
the step of scanning the preset metadata file to match the answer operation data with a preset marking rule to obtain a matching result comprises the following steps:
scanning the table unit data file to obtain the table unit data;
according to a first mapping relation between table unit data and list metadata, inquiring the list metadata corresponding to the table unit data in the list metadata file;
according to a second mapping relation between the list metadata and the marking rules, inquiring preset marking rules corresponding to the list metadata in the rule metadata file;
and matching the form unit data with the preset marking rule to obtain a matching result.
2. The scoring optimization method of claim 1, wherein the matching results include a first matching result and a second matching result,
the step of matching the form unit data with the preset marking rule to obtain a matching result comprises the following steps:
when detecting that the question corresponding to the list metadata is an objective question, judging whether answer operation in the list unit data is consistent with preset answer operation corresponding to the preset examination rule or not according to the preset examination rule;
if the answer is consistent with the first matching result, judging that the answer of the objective questions is correct, and obtaining a first matching result;
and if the answer is inconsistent, judging that the answer of the objective questions is wrong, and obtaining a second matching result.
3. The scoring optimization method of claim 1, wherein the step of matching the form unit data with the preset scoring rule to obtain a matching result further comprises:
when detecting that the question corresponding to the list metadata is a subjective question, acquiring an answer field in the list unit data;
and calculating semantic similarity between the answer field and a preset field corresponding to the preset marking rule, and generating the matching result according to the semantic similarity.
4. The scoring optimization method of claim 1, wherein the preset metadata files comprise a form unit data file, a list metadata file,
when detecting that the user is answering, acquiring answer operation data and storing the answer operation data into a preset metadata file, wherein the step of acquiring the answer operation data comprises the following steps:
when detecting that a user is answering, acquiring the answering operation data and question data corresponding to the answering operation data, and generating corresponding attribute labels according to the association relationship between the question data and the answering operation data;
and storing the question data and the attribute labels into a list unit data file together, and storing the answer operation data and the attribute labels into the list metadata file together.
5. The scoring optimization method of claim 1, wherein the preset metadata file comprises a rule metadata file,
before the step of obtaining a matching result by scanning the preset metadata file and matching the answer operation data with a preset scoring rule, the scoring optimization method comprises the following steps:
obtaining assessment content and a user role to which the user belongs;
and configuring corresponding preset marking rules according to the assessment contents and the user roles, and storing the marking rules into the rule metadata file.
6. The scoring optimization method of claim 5, wherein after the step of configuring the corresponding preset scoring rules according to the assessment content and the user roles and storing the corresponding preset scoring rules in the rule metadata file, the scoring optimization method further comprises:
acquiring current assessment content and a current user role to which the user belongs;
judging whether the current checking content or the current user role is changed or not;
if the change occurs, returning to the execution step: and configuring corresponding preset marking rules according to the assessment contents and the user roles, and storing the preset marking rules into the rule metadata file.
7. An scoring optimization device, comprising:
the data acquisition module is used for acquiring answer operation data when detecting that a user is answering, and storing the answer operation data into a preset metadata file, wherein the answer operation data comprises answer operation and answer;
the data matching module is used for matching the answer operation data with a preset marking rule by scanning the preset metadata file after the answer of the user is detected to be finished, so as to obtain a matching result;
the result output module is used for outputting the assessment result corresponding to the user according to the matching result;
wherein the preset metadata file comprises a rule metadata file, a list metadata file and a table unit data file, the answer operation data is stored in the table unit data file as table unit data,
the step of scanning the preset metadata file to match the answer operation data with a preset marking rule to obtain a matching result comprises the following steps:
the form acquisition module is used for scanning the form unit data file to acquire the form unit data;
the list query module is used for querying the list metadata corresponding to the list unit data in the list metadata file according to a first mapping relation between the list unit data and the list metadata;
the rule query module is used for querying a preset marking rule corresponding to the list metadata in the rule metadata file according to a second mapping relation between the list metadata and the marking rule;
and the rule matching module is used for matching the form unit data with the preset marking rule to obtain a matching result.
8. An electronic device, characterized in that the electronic device comprises: a memory, a processor, and a program stored on the memory for implementing a scoring optimization method:
the memory is used for storing a program for realizing the scoring optimization method;
the processor is configured to execute a program implementing the scoring optimization method to implement the steps of the scoring optimization method according to any one of claims 1 to 6.
9. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a program for implementing an optimization method for scoring, the program being executed by a processor for implementing the steps of the optimization method for scoring as recited in any one of claims 1 to 6.
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