Big data driving-based Japanese education precise learning condition analysis implementation method and platform

Abstract

The invention discloses a Japanese education precise learning condition analysis implementation method and platform based on big data driving, and the method comprises the following steps: S1, enabling students and teachers to log in a system, enabling the teachers to input examination questions, storing the examination questions in a database, and enabling the students to carry out an online examination from the system; s2, inputting various historical question types in advance by a teacher, and inputting standard spoken language answers and non-standard spoken language answers; s3, training the acquired question types and answers to obtain a standard spoken language training model and a non-standard spoken language training model; s4, analyzing the obtained spoken language features of the students by using the training model, and judging whether the spoken language is standard or not; s5, obtaining score characteristics of each test score of each student, classifying the score characteristics of multiple times, and generating a report and a curve graph; through the system, the oral ability of students after Japanese learning can be assessed online, and the score of each assessment of each student can be analyzed and counted.

Description

Technical Field

[0001] The present invention relates to the technical field of Japanese language learning and assessment, and specifically to a method and platform for implementing accurate learning situation analysis of Japanese language education based on big data drive. Background Art

[0002] In the Japanese major, the ability to master Japanese is more inclined to oral communication. In Japanese learning, Japanese can be learned through the online system, and the assessment of learning results can only be done offline. Moreover, offline can only assess the scholars' vocabulary mastery. As for the scholars' oral ability, it is difficult to assess and analyze.

[0003] Therefore, in order to correct the above-mentioned defects, we proposed a method and platform for accurate learning situation analysis of Japanese language education driven by big data. Summary of the invention

[0004] The technical problem solved by the present invention is to propose a method and platform for implementing accurate learning situation analysis of Japanese language education based on big data drive.

[0005] To achieve the above purpose, the present invention provides the following technical solution: a method for implementing accurate learning situation analysis of Japanese language education based on big data drive, comprising the following steps: S1. Students and teachers log into the system, teachers input test questions and store them in the database, and students take online tests from the system; S2. The teacher inputs various types of history questions in advance and inputs the standard oral answers and non-standard oral answers; S3, training the obtained question types and answers to obtain a standard oral training model and a non-standard oral training model; S4, using the above training model to analyze the acquired spoken language features of the students to determine whether the spoken language is standard; S5. Obtain the score characteristics of each student's test results for each time, classify the score characteristics for multiple times, and generate reports and curve charts.

[0006] The invention discloses a method and platform for realizing accurate learning situation analysis of Japanese education based on big data drive, comprising the following steps: S1, students and teachers log in to the system, the teacher inputs the test questions and stores them in a database, and the students take an online test from the system; S2, the teacher inputs various types of history questions in advance, and inputs standard oral answers and non-standard oral answers; S3, the obtained question types and answers are trained to obtain standard oral training models and non-standard oral training models; S4, the obtained oral features of the students are analyzed using the above training model to determine whether the oral language is standard; S5, the score features of each test result of each student are obtained, multiple score features are classified, and reports and curve graphs are generated; the invention can assess the oral ability of students after Japanese learning on the Internet through this system, and can analyze and count the results of each assessment of each student.

[0007] The examination system includes a teacher login module, a student login module, a storage library, a training model, an examination module, a score query module, a test question entry module, a teacher scoring module, an automatic scoring module, a learning situation analysis module, and a history question type entry module. The student login module is respectively connected to the examination module and the score query module in communication, and the examination module and the score query module are respectively connected to the storage library in communication; The teacher login module is respectively communicated with the test question entry module, the teacher scoring module, the automatic scoring module, the learning situation analysis module, and the history question type entry module; the test question entry module, the teacher scoring module, and the learning situation analysis module are respectively communicated with the storage repository; the history question type entry module and the automatic scoring module are respectively communicated with the training model; and the training model is communicated with the storage repository.

[0008] Furthermore, the examination module includes a vocabulary examination module and an oral examination module. The vocabulary examination module includes a writing module, a text display module and a writing storage module. The writing module is communicatively connected to the writing storage module. After the test questions are displayed by the text display module, the students enter the answers from the writing module. The oral examination module includes a voice broadcast module, a voice recognition module, a noise elimination module, and a voice transfer module. The voice recognition module, the noise elimination module, and the voice transfer module are communicatively connected in sequence. The voice broadcast module plays out the test questions. When the students answer the questions orally, the voice recognition module recognizes the spoken language.

[0009] Compared with the prior art, the beneficial effects of the present invention are: the present invention can assess the oral ability of students after learning Japanese online through the system, and can analyze and count the results of each student's assessment each time. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a schematic diagram of the process structure of the present invention; Figure 2 It is a schematic diagram of the system structure of the present invention.

[0011] Numbers in the figure: 1. Student login module; 2. Teacher login module; 3. Repository; 4. Training model; 5. Examination module; 6. Score query module; 7. Question entry module; 8. Teacher scoring module; 9. Learning situation analysis module; 10. History question type entry module; 11. Automatic scoring module. DETAILED DESCRIPTION

[0012] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0013] See also Figure 1-2 A method for implementing accurate learning situation analysis of Japanese language education based on big data is as follows: both students and teachers log in to the system, the teacher enters the test questions and stores them in the database, the students take the online test from the system, and before the students take the test, the teacher inputs various types of history questions in advance, inputs the standard oral answers and non-standard oral answers, and then trains the obtained question types and answers to obtain standard oral training models and non-standard oral training models, and uses the above training models to analyze the obtained students' oral features to determine whether the oral is standard, and finally obtains the score features of each student's test results each time, classifies the multiple score features, and generates reports and curves.

[0014] The above implementation method relies on a platform, a carrier and an examination system, and is carried out from the carrier to the examination system, wherein the carrier can be a computer, a mobile phone, etc.

[0015] The examination system includes a teacher login module 2, a student login module 1, a repository 3, a training model 4, an examination module 5, a score query module 6, a test question entry module 7, a teacher scoring module 8, an automatic scoring module 11, a learning situation analysis module 9, and a history question type entry module 10. The examination module 5 includes a vocabulary examination module and an oral examination module. The vocabulary examination module includes a writing module, a text display module, and a writing preservation module. The oral examination module includes a voice broadcast module, a voice recognition module, a noise elimination module, and a voice transfer module. Specifically: The teacher enters the examination system from the teacher login module 2 and clicks on the test question entry module 7. The test question entry module 7 can access the Internet, query questions and answers from the Internet and import them into the storage repository 3. At the same time, previous test questions and the standard answers and non-standard answers to the test questions are entered into the storage repository, and the features of the standard answers and non-standard answers to the test questions are obtained and distinguished, and then continuous training is carried out to obtain standard oral training models and non-standard oral training models.

[0016] After the student logs in, he clicks on the test module 5, and then selects one of the vocabulary test module and the oral test module. If the oral test module is selected, the test questions in the storage library are retrieved, and then the test questions are broadcasted using the voice broadcast module. The student gives an oral answer based on the broadcasted voice. The voice recognition module begins to recognize the voice uttered by the student, and uses the noise elimination module to eliminate the noise in the student's voice, and then stores it in the storage library.

[0017] The automatic scoring module 11 and the training model are used to judge the results of this test, that is, each spoken voice package is compared with the standard spoken training model and the non-standard spoken training model respectively, and feature analysis is performed to determine whether the spoken language is standard. Finally, the score of each student's test result is obtained. The more the voice features in the voice package deviate from the standard spoken language and the closer it is to the non-standard spoken language, the lower the score. Conversely, the higher the score, the score is stored in the storage library.

[0018] Students can check their scores from the score query module 6.

[0019] Teachers can enter from the teacher scoring module 8, retrieve the voice packages of each student in this exam from the storage repository, and perform manual scoring. At the same time, after entering from the learning situation analysis module 9, multiple scoring features are classified, reports and graphs are generated, and students' learning situations are analyzed from the reports and graphs.

[0020] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for implementing accurate learning situation analysis of Japanese language education based on big data, characterized in that: The following steps are involved: S1. Students and teachers log into the system, teachers input test questions and store them in the database, and students take online tests from the system; S2. The teacher inputs various types of history questions in advance and inputs the standard oral answers and non-standard oral answers; S3, training the obtained question types and answers to obtain a standard oral training model and a non-standard oral training model; S4, using the above training model to analyze the acquired spoken language features of the students to determine whether the spoken language is standard; S5. Obtain the score characteristics of each student's test results for each time, classify the score characteristics for multiple times, and generate reports and curve charts.

2. A platform for implementing the method for accurate learning situation analysis of Japanese language education based on big data drive as claimed in claim 1, characterized in that: Including carrier and examination system, from the carrier to the examination system, The examination system comprises a teacher login module (2), a student login module (1), a storage library (3), a training model (4), an examination module (5), a score query module (6), a test question entry module (7), a teacher scoring module (8), an automatic scoring module (11), a learning situation analysis module (9), and a history question type entry module (10). The student login module (1) is respectively connected to the examination module (5) and the score query module (6) in communication, and the examination module (5) and the score query module (6) are respectively connected to the storage library (3); The teacher login module (2) is respectively connected to the test question entry module (7), the teacher scoring module (8), the automatic scoring module (11), the learning situation analysis module (9), and the history question type entry module (10); the test question entry module (7), the teacher scoring module (8), and the learning situation analysis module (9) are respectively connected to the storage repository (3); the history question type entry module (10) and the automatic scoring module (11) are respectively connected to the training model (4); and the training model (4) is connected to the storage repository (3).

3. The platform for implementing the method for accurate Japanese language education learning situation analysis based on big data drive according to claim 2 is characterized by: The test module (5) comprises a vocabulary test module and a spoken test module.

4. The platform for implementing the method for accurate Japanese language education learning situation analysis based on big data drive according to claim 3 is characterized by: The vocabulary test module includes a writing module, a text display module and a writing storage module. The writing module is in communication connection with the writing storage module. After the test questions are displayed by the text display module, the students enter the answers from the writing module.

5. The platform for implementing the method for accurate Japanese language education learning situation analysis based on big data drive according to claim 3 is characterized by: The oral examination module includes a voice broadcast module, a voice recognition module, a noise elimination module, and a voice transfer module. The voice recognition module, the noise elimination module, and the voice transfer module are connected in communication in sequence. The voice broadcast module plays the test questions, and when the students answer the questions orally, the voice recognition module recognizes the spoken language.

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