Automatic scoring method for information technology training experiments

Abstract

The present invention relates to the field of data processing technology, and specifically to an automatic scoring method for information technology training experiments, comprising the following steps: Step 1: constructing a data interaction platform, student users registering on the data interaction platform through identity information, and student users sending experimental data on the data interaction platform through a wireless network housing data interaction platform; Step 2: monitoring the computer operation records of student users during training or experimental development. The present invention uses the capture and identification of student user programming experimental operation record data, and cooperates with the experimental result data reading, and further performs weight analysis on the experimental operation record data, which provides basic data support for the automatic scoring of the experiment for the execution of the steps of the method, and finally, through the user end, the experimental operation record data and the scoring record and weight configuration of the experimental data, the weight analysis of the experimental operation record data corresponding to each score is accurately obtained.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to an automatic scoring method for information technology training experiments. Background Art

[0002] The IT industry generally refers to the information technology industry, also known as the information industry. It is an industry that uses information means and technologies to collect, organize, store, and transmit information intelligence, provide information services, and provide corresponding information means, information technology and other services. The information technology industry includes: industrial sectors engaged in the production, circulation and sale of information, and the use of information to provide services;

[0003] The IT industry mainly includes system analysts, computer programmers, software testers, software project managers, and system architects.

[0004] When learning relevant knowledge of IT technology, in addition to mastering theoretical knowledge, you also need to have practical hands-on skills. Therefore, hands-on experiments are an indispensable part.

[0005] In traditional experimental classes, teachers usually ask students to complete given experimental tasks, and students submit experimental reports. Teachers review and finally give evaluations of learning operational capabilities. This results in teachers having to do a lot of approval and grading work, which is very inefficient and untimely, especially in the case of a large number of students in online distance education who attend classes at different times, which brings a huge burden to teachers' approval and grading work. Summary of the invention

[0006] Technical issues solved

[0007] In view of the above-mentioned shortcomings of the prior art, the present invention provides an automatic scoring method for information technology training experiments, which solves the technical problems raised in the above-mentioned background technology.

[0008] Technical Solution

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0010] The automatic scoring method for information technology training experiment includes the following steps:

[0011] Step 1: Build a data interaction platform. Student users register on the data interaction platform with their identity information. Student users send experimental data on the data interaction platform through the wireless network.

[0012] Step 2: Monitor the computer operation records of student users during practical training or experiments:

[0013] Step 3: Obtain computer operation record data according to the execution result data of step 2 and its lower sub-steps, and package the computer operation record data with the experimental data;

[0014] Step 4: Analyze the timestamp of the data source data interaction platform corresponding to the computer operation record data of the data packet, and generate a queue of timing logic for the data packet according to the timestamp;

[0015] Step 5: Feedback the queued data packets to the data interaction platform constructed in step 1. The user end accesses the data interaction platform through the mobile device or computer device held by the user, and downloads the data packets in the data interaction platform.

[0016] Step 6: The user terminal decompresses the data packet and manually scores the experimental data and computer operation record data in the decompressed data packet;

[0017] Step 7: Obtain the manual scoring results of the experimental data and computer operation record data by the user in step 6, analyze the measurement results and configuration weights of the experimental data and computer operation record data in the data packet corresponding to the manual scoring, and after the user holds a mobile device or computer and downloads and decompresses the data packet, determine the data packet score based on the analysis of the measurement results and configuration weights of the experimental data and computer operation record data in the data packet corresponding to the manual scoring.

[0018] Furthermore, when the step 1 is executed, after the data interaction platform is constructed synchronously, a database is set up in the data interaction platform, and the experimental data received in real time by the data interaction platform is stored in the database.

[0019] Furthermore, the step 2 is provided with steps at a lower level, including the following steps:

[0020] Step 21: Select computer operation keys as capture targets, and measure the operation records corresponding to the capture targets in the monitored computer operation records of the student users when the student users carry out practical training or experiments;

[0021] Step 22: Design the capture target weight, and select the capture target corresponding computer operation record data from the capture target corresponding operation record measurement result data according to the capture target weight.

[0022] Furthermore, when the computer operation key position is selected as the capture target in step 21, it is manually edited and set by the user end.

[0023] Furthermore, the step 22 applies any random algorithm in the prior art when selecting the computer operation record data corresponding to the capture target.

[0024] Furthermore, when step 3 is executed, the computer operation record data in the packaged data is synchronously marked. When the user terminal receives the packaged data, after decompressing the packaged data, it distinguishes the computer operation record data and the experimental data according to the mark in the packaged data.

[0025] Furthermore, in step 5, when the data packet is fed back to the data interaction platform, the data interaction platform synchronously deletes the experimental data and computer operation record data corresponding to the data packet.

[0026] Furthermore, the step 5 is provided with sub-steps, including the following steps:

[0027] Step 51: After the user downloads the data packet by holding a mobile device or a computer device, the received data packet is synchronously decompressed in real time, and the data in each data packet is distinguished according to the marked data in the decompressed data packet;

[0028] After the data in each data packet is distinguished in step 51, each data packet is divided into two groups, one of which is computer operation record data and the other is experimental data.

[0029] Furthermore, when manually scoring the experimental data and the computer operation record data in step 6, the user's score and the experimental data and the computer operation record data corresponding to the score are stored synchronously.

[0030] Furthermore, when determining the data packet score, if the measurement results and configuration weights of the experimental data and computer operation record data are not analyzed, step 8 jumps to step 6 for execution.

[0031] Beneficial Effects

[0032] Compared with the known public technology, the technical solution provided by the present invention has the following beneficial effects:

[0033] 1. The present invention provides an automatic scoring method for information technology training experiments. The method is applied to programming learning in the IT industry. During the execution of the steps of the method, the student user programming experiment operation record data is captured and identified, and the experimental result data is read. The experimental operation record data is further subjected to weight analysis, which provides basic data support for the automatic scoring of the experiment for the execution of the steps of the method. Finally, the user end records the experimental operation record data and the scoring record and weight configuration of the experimental data, and accurately obtains the weight analysis of the experimental operation record data corresponding to each score, so that the experimental operation record data and the experimental data can be automatically scored according to the corresponding weight analysis;

[0034] 2. The method of the present invention can further select a certain amount of experimental operation record data based on the weight analysis of the experimental operation record data during the execution of the steps. When the user end scores the experimental operation record data and experimental data of the student user, the amount of experimental operation record data required for manual scoring is reduced, thereby making the method more efficient in the environment where the user manually scores the experimental operation record data and experimental data during the execution of its steps. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0036] Figure 1 This is a flow chart of the automatic scoring method for information technology training experiments. DETAILED DESCRIPTION

[0037] 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] The present invention will be further described below in conjunction with the embodiments. Example

[0039] Example 1

[0040] The automatic scoring method for information technology training experiments in this embodiment is as follows: Figure 1 As shown, the following steps are included:

[0041] Step 1: Build a data interaction platform. Student users register on the data interaction platform with their identity information. Student users send experimental data on the data interaction platform through the wireless network.

[0042] Step 2: Monitor the computer operation records of student users during practical training or experiments:

[0043] Step 3: Obtain computer operation record data according to the execution result data of step 2 and its lower sub-steps, and package the computer operation record data with the experimental data;

[0044] Step 4: Analyze the timestamp of the data source data interaction platform corresponding to the computer operation record data of the data packet, and generate a queue of timing logic for the data packet according to the timestamp;

[0045] Step 5: Feedback the queued data packets to the data interaction platform constructed in step 1. The user end accesses the data interaction platform through the mobile device or computer device held by the user, and downloads the data packets in the data interaction platform.

[0046] Step 6: The user terminal decompresses the data packet and manually scores the experimental data and computer operation record data in the decompressed data packet;

[0047] Step 7: Obtain the manual scoring results of the experimental data and computer operation record data by the user in step 6, analyze the measurement results and configuration weights of the experimental data and computer operation record data in the data packet corresponding to the manual scoring, and after the user holds a mobile device or computer and downloads and decompresses the data packet, determine the data packet score based on the analysis of the measurement results and configuration weights of the experimental data and computer operation record data in the data packet corresponding to the manual scoring.

[0048] Example 2

[0049] In terms of specific implementation, based on Example 1, this example refers to Figure 1 The automatic scoring method for information technology training experiment in Example 1 is further described in detail as shown below:

[0050] When step 1 is executed, after the data interaction platform is synchronously constructed, a database is set up in the data interaction platform, and the experimental data received in real time by the data interaction platform is stored through the database.

[0051] like Figure 1 As shown, step 2 is provided at the lower level with steps, including the following steps:

[0052] Step 21: Select computer operation keys as capture targets, and measure the operation records corresponding to the capture targets in the monitored computer operation records of the student users when the student users carry out practical training or experiments;

[0053] Step 22: Design the capture target weight, and select the capture target corresponding computer operation record data from the capture target corresponding operation record measurement result data according to the capture target weight.

[0054] like Figure 1 As shown, when the computer operation key position is selected as the capture target in step 21, it is manually edited and set by the user.

[0055] like Figure 1As shown, step 22 applies any random algorithm in the prior art when selecting the computer operation record data corresponding to the capture target.

[0056] Example 3

[0057] In terms of specific implementation, based on Example 1, this example refers to Figure 1 The automatic scoring method for information technology training experiment in Example 1 is further described in detail as shown below:

[0058] When step 3 is executed, the computer operation record data in the packaged data is synchronously marked. When the user terminal receives the packaged data, after decompressing the packaged data, it distinguishes the computer operation record data and the experimental data according to the mark in the packaged data.

[0059] like Figure 1 As shown, in step 5, when the data packet is fed back to the data interaction platform, the data interaction platform synchronously deletes the experimental data and computer operation record data corresponding to the data packet.

[0060] like Figure 1 As shown, step 5 is provided with sub-steps at the lower level, including the following steps:

[0061] Step 51: After the user downloads the data packet by holding a mobile device or a computer device, the received data packet is synchronously decompressed in real time, and the data in each data packet is distinguished according to the marked data in the decompressed data packet;

[0062] After the data in each data packet is distinguished in step 51, each data packet is divided into two groups, one of which is computer operation record data and the other is experimental data.

[0063] like Figure 1 As shown, in step 6, when the experimental data and computer operation record data are manually scored, the user's score and the experimental data and computer operation record data corresponding to the score are stored synchronously.

[0064] like Figure 1 As shown, when determining the data packet score in step 8, if the measurement results and configuration weights of the experimental data and computer operation record data are not analyzed, the process jumps to step 6 for execution.

[0065] In summary, the method provided in the above embodiment is used for programming learning in the IT industry. During the execution of the steps of the method, the student user's programming experiment operation record data is captured and identified, and the experimental result data is read, and the experimental operation record data is further weighted analyzed, which provides basic data support for the automatic scoring of the experiment for the execution of the method steps. Finally, through the user end, the scoring record and weight configuration of the experimental operation record data and the experimental data, the weight analysis of the experimental operation record data corresponding to each score is accurately obtained, so that the experimental operation record data and the experimental data can be automatically scored according to the corresponding weight analysis; in addition, during the execution of the steps, the method can further select a certain amount of experimental operation record data according to the weight analysis of the experimental operation record data. When the user end scores the student user's experimental operation record data and experimental data, the amount of experimental operation record data required for manual scoring is reduced, thereby making the method more efficient in the environment where the user manually scores the experimental operation record data and the experimental data during the execution of its steps.

[0066] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An automatic scoring method for information technology training experiments, characterized in that: The following steps are involved: Step 1: Build a data interaction platform. Student users register on the data interaction platform with their identity information. Student users send experimental data on the data interaction platform through the wireless network. Step 2: Monitor the computer operation records of student users during practical training or experiments: Step 3: Obtain computer operation record data according to the execution result data of step 2 and its lower-level sub-steps, and package the computer operation record data with the experimental data; Step 4: Analyze the timestamp of the data source data interaction platform corresponding to the computer operation record data of the data packet, and generate a queue of timing logic for the data packet according to the timestamp; Step 5: Feedback the queued data packets to the data interaction platform constructed in step 1. The user end accesses the data interaction platform through the mobile device or computer device held by the user, and downloads the data packets in the data interaction platform. Step 6: The user terminal decompresses the data packet and manually scores the experimental data and computer operation record data in the decompressed data packet; Step 7: Obtain the manual scoring results of the experimental data and computer operation record data by the user in step 6, analyze the measurement results and configuration weights of the experimental data and computer operation record data in the data package corresponding to the manual scoring, and after the user holds a mobile device or computer and downloads the data package and decompresses the data package, determine the data package score according to the measurement results and configuration weights of the experimental data and computer operation record data in the data package corresponding to the manual scoring. Wherein, when the step 1 is executed, after the data interaction platform is built, a database is set up in the data interaction platform, and the experimental data received in real time by the data interaction platform is stored in the database. Wherein, the step 2 is provided with steps at the lower level, including the following steps: Step 21: Select computer operation keys as capture targets, and measure the operation records corresponding to the capture targets in the monitored computer operation records of the student users when the student users carry out practical training or experiments; Step 22: Design the capture target weight, and select the computer operation record data corresponding to the capture target from the measurement result data of the operation record corresponding to the capture target according to the capture target weight. Wherein, when selecting the computer operation key as the capture target in step 21, the user terminal manually edits and sets it. Wherein, said step 22 applies any random algorithm in the prior art when selecting the computer operation record data corresponding to the capture target, Wherein, when the step 3 is executed, the computer operation record data in the packaged data is synchronously marked. When the user terminal receives the packaged data, after decompressing the packaged data, the computer operation record data and the experimental data are distinguished according to the mark in the packaged data. Wherein, in step 5, when the data packet is fed back to the data interaction platform, the data interaction platform synchronously deletes the experimental data and computer operation record data corresponding to the data packet. Wherein, the step 5 is provided with sub-steps at the lower level, including the following steps: Step 51: After the user downloads the data packet by holding a mobile device or a computer device, the received data packet is synchronously decompressed in real time, and the data in each data packet is distinguished according to the marked data in the decompressed data packet; After step 51 distinguishes the data in each data packet, each data packet is divided into two groups, one of which is computer operation record data and the other is experimental data. Wherein, in step 6, when manually scoring the experimental data and the computer operation record data, the user's score and the experimental data and the computer operation record data corresponding to the score are stored synchronously. Wherein, when determining the data packet score, if the measurement results and configuration weights of the experimental data and the computer operation record data are not analyzed, step 7 jumps to step 6 for execution.

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