CN110610630B - Intelligent English teaching system based on error dispersion checking - Google Patents
Intelligent English teaching system based on error dispersion checking Download PDFInfo
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- G09B7/04—Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student characterised by modifying the teaching programme in response to a wrong answer, e.g. repeating the question, supplying a further explanation
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Abstract
An intelligent english teaching system based on error dispersion check, includes: the data module is used for recording all the standard answers and the wrong answers of the test questions; the answer module is used for solving the test questions by the students; the first decomposition module is used for decomposing sentences into words; the analysis module is used for analyzing words obtained by the first decomposition module decomposing sentences; the second decomposition module is used for decomposing the sentence into phrases; a first recognition module for recognizing a context of a sentence; the second recognition module is used for recognizing the language state of the sentence; the third recognition module is used for carrying out word recognition; the first assignment module is used for carrying out differential assignment; a detection module to detect an error condition; the second assignment module is used for assigning the error degree; a classification module to classify the errors; the processing module is used for calculating discrete values according to the assignment results of the first assignment module and the second assignment module; and the teaching module is used for performing classified teaching according to the discrete values calculated by the processing module.
Description
Technical Field
The invention relates to the field of intelligent teaching, in particular to an intelligent English teaching system based on error dispersion checking.
Background
Internet education is a new education form combining internet science and technology with the education field along with the continuous development of current science and technology. The informatization technology has permeated all aspects of society, in the field of education, a subversion of informatization is occurring quietly, in the modern information society, the internet has the characteristics of high efficiency, rapidness and convenient transmission, plays an irreplaceable important role in the study and the life of middle and small students, and becomes a good helper for the middle and small students to study, which is not only beneficial to improving the ability of the middle and primary school students to study and communicate on the internet, but also helps the children to increase knowledge, widen the visual field and enlighten the intelligence, but also can more effectively stimulate the learning desire and curiosity of children, can more effectively develop good behavior habits of independent thinking and courage of students in middle and primary schools, comprehensively educate and cultivate future builders and commuters in China, has incomplete current education system, and cannot well provide good learning environment for users and guarantee learning efficiency. Meanwhile, some students cannot formulate good learning schemes and learning contents suitable for themselves according to their own conditions, which can cause the occurrence of white work.
Disclosure of Invention
The purpose of the invention is as follows:
the invention provides an intelligent English teaching system based on error dispersion check, which aims at solving the problem that the condition of idle work can be caused because some students cannot formulate good learning schemes and learning contents which are more suitable for the students according to the condition of the students.
The technical scheme is as follows:
an intelligent english teaching system based on error dispersion check, includes:
the data module is used for recording all the standard answers and the wrong answers of the test questions;
the answer module is used for solving the test questions by the students;
the first decomposition module is used for decomposing sentences into words;
the analysis module is used for analyzing words obtained by decomposing sentences by the first decomposition module;
the second decomposition module is used for decomposing the sentence into phrases;
a first recognition module for recognizing a context of a sentence;
the second recognition module is used for recognizing the language state of the sentence;
the third recognition module is used for carrying out word recognition according to the sentence context recognized by the first recognition module and the language state recognized by the second recognition module;
the first assignment module is used for carrying out difference assignment according to the sentence context recognized by the first recognition module, the sentence morphism recognized by the second recognition module and the words recognized by the third recognition module;
a detection module to detect an error condition;
the second assignment module is used for assigning the error degree according to the error condition detected by the detection module;
a classification module for classifying errors according to the error conditions detected by the detection module;
the processing module is used for calculating discrete values according to the assignment results of the first assignment module and the second assignment module in the classification results of the classification module;
and the teaching module is used for performing classified teaching according to the discrete values calculated by the processing module.
As a preferred mode of the present invention, the first decomposition module decomposes the sentence into words according to intervals between words of each sentence.
As a preferred embodiment of the present invention, the analysis module analyzes words one by one, the analysis module determines part-of-speech ranges of the words according to the spelling and form of the words, and the analysis module finally confirms the part-of-speech of the words according to the parts-of-speech of the words before and after the words.
As a preferred mode of the present invention, the second decomposition module determines a connection relationship between preceding and following words according to the part of speech of the word analyzed by the analysis module, and the second decomposition module decomposes the sentence into a plurality of phrases in the form of a main phrase, a subordinate phrase, and a partial phrase according to the connection relationship between the preceding and following words.
As a preferred mode of the present invention, since the words in the main-predicate phrase, the verb-object phrase, and the bias phrase repeatedly appear, when analyzing the phrases decomposed by the second decomposition module, all the phrases are included in the analysis range, that is, the phrases with overlapping portions are included in the analysis range.
As a preferred mode of the present invention, the second recognition module determines the language state of the sentence according to the verb form analyzed and obtained by the analysis module and the phrase form decomposed by the second decomposition module.
As a preferred mode of the present invention, when performing a multiple sentence analysis, the first recognition module determines a context of a current sentence according to a context sentence; the first recognition module does not recognize context when performing single sentence analysis.
As a preferred mode of the present invention, the first assignment module performs a first assignment according to the sequence of the context, the semantics, and the language state of the sentence in english learning, and the first assignment module performs a modification of sentence assignment according to the length of the word and the confusion degree of the related word, and the result is used as a second assignment.
As a preferred mode of the present invention, the second assignment module performs a first assignment of the degree of error according to the degree of association between the word meaning of the erroneous word and the sentence; the second assignment module carries out secondary assignment of the error degree according to the difference degree between the tense of the error word and the morpheme of the sentence; the second assignment module carries out three-time assignment of the error degree according to the context of the current sentence; the second assignment module carries out four-time assignment of the error degree according to the phrase structure; and the second assignment module calculates a final assignment result according to the first assignment, the second assignment, the third assignment and the fourth assignment.
As a preferred mode of the present invention, when a plurality of errors occur during the error classification, the classification module classifies sentences into the respective errors according to the error condition, i.e. a single sentence can be classified into a plurality of types at the same time.
The invention realizes the following beneficial effects:
according to the personalized learning scheme and the customized learning content which are embodied by students and used for the discrete degree of wrong answers obtained by knowledge, the problem that the condition of idle work can be caused because some students cannot make good learning scheme and learning content which are more suitable for the students according to the condition of the students is solved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
FIG. 1 is a system framework diagram of the present invention;
FIG. 2 is a diagram of the working steps of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Example one
The reference figures are figure 1 and figure 2. An intelligent english teaching system based on error dispersion check, includes:
the data module 1 is used for recording all standard answers and wrong answers of the test questions;
the answer module 2 is used for solving the test questions by the students;
a first decomposition module 3, for decomposing sentences into words;
an analysis module 13, configured to analyze words obtained by the first decomposition module 3 decomposing sentences;
a second decomposition module 4, configured to decompose the sentence into phrases;
a first recognition module 5 for recognizing a context of a sentence;
the second recognition module 6 is used for recognizing the language state of the sentence;
a third recognition module 7, configured to perform word recognition according to the sentence context recognized by the first recognition module 5 and the language state recognized by the second recognition module 6;
a first assignment module 8, configured to perform difference assignment according to the sentence context recognized by the first recognition module 5, the sentence morphism recognized by the second recognition module 6, and the word recognized by the third recognition module 7;
a detection module 14 for detecting an error condition;
the second assignment module 9 is configured to assign a value of an error degree according to the error condition detected by the detection module 14;
a classification module 10 for classifying errors based on the error conditions detected by the detection module 14;
a processing module 11, configured to calculate a discrete value according to the assignment results of the first assignment module 8 and the second assignment module 9 in the classification result of the classification module 10;
and the teaching module 12 is configured to perform classification teaching according to the discrete values calculated by the processing module 11.
As a preferred mode of the present invention, the first decomposition module 3 decomposes the sentence into words according to the intervals between the words of each sentence.
As a preferred embodiment of the present invention, the analysis module 13 analyzes words one by one, the analysis module 13 determines part-of-speech ranges of words according to the spelling and form of the words, and the analysis module 13 finally confirms the part-of-speech of the words according to the parts-of-speech of the preceding and following words.
As a preferred mode of the present invention, the second decomposition module 4 determines a connection relationship between the preceding and following words according to the part of speech of the word analyzed by the analysis module 13, and the second decomposition module 4 decomposes the sentence into a plurality of phrases in the form of a main phrase, a subordinate phrase, and a bias phrase according to the connection relationship between the preceding and following words.
As a preferred mode of the present invention, since the words in the main-predicate phrase, the verb-object phrase, and the bias phrase repeatedly appear, when analyzing the phrases decomposed by the second decomposition module 4, all the phrases are included in the analysis range, that is, the phrases with overlapping portions are included in the analysis range.
As a preferred embodiment of the present invention, the second recognition module 6 determines the language state of the sentence according to the verb form analyzed by the analysis module 13 and the phrase form decomposed by the second decomposition module 4.
As a preferred mode of the present invention, when performing a multiple sentence analysis, the first recognition module 5 determines the context of the current sentence according to the context sentence; when performing a single sentence analysis, the first recognition module 5 does not recognize the context.
As a preferred mode of the present invention, the first assignment module 8 performs a first assignment according to the sequence of the contexts, semantics, and morphemes of the sentences in english learning, and the first assignment module 8 corrects the assignment of the sentences according to the lengths of the words and the confusion degrees of the related words, and the result is used as a second assignment.
As a preferred mode of the present invention, the second assigning module 9 performs a first assignment of the error degree according to the association degree between the incorrect word meaning and the sentence; the second assignment module 9 performs secondary assignment of the error degree according to the difference degree between the tense of the error word and the morpheme of the sentence; the second assignment module 9 assigns the error degree three times according to the context of the current sentence; the second assignment module 9 performs four assignments of error degrees according to the phrase structure; and the second assignment module 9 calculates a final assignment result according to the first assignment, the second assignment, the third assignment and the fourth assignment.
As a preferred mode of the present invention, when a plurality of errors occur during the error classification by the classification module 10, the classification module 10 classifies sentences into the respective errors according to the error condition, i.e. a single sentence can be classified into a plurality of types at the same time.
In the specific implementation process, when a student answers, the answering module 2 obtains the answer of the student to the current test question, assuming that the answer is a translated sentence, after the student answers, the sentence is decomposed into a plurality of words by the first decomposition module 3, the words obtained by the decomposition of the first decomposition module 3 are analyzed by the analysis module 13, the meaning and the part of speech of the words are checked by the analysis module 13 through butting with a network dictionary, and the part of speech is simply classified into a verb, a noun, an adjective, an adverb and an auxiliary word, wherein the auxiliary word has a linkage relationship with the verb; when the second decomposition module 4 decomposes sentences into phrases, the reference analysis module 13 analyzes words, and the sentences are divided into major and subordinate phrases, verb phrases and partial phrases according to the part of speech and the classification of the phrases, namely, the preceding nouns and the following verbs, auxiliary words and adverbs form major and subordinate phrases; the preceding auxiliary words, verbs, adverbs and the following nouns form an animated guest phrase; the nouns and the adjectives form a partial positive phrase, and the second decomposition module 4 decomposes the sentence into a plurality of phrases through the formation mode.
The first recognition module 5 performs context analysis according to context, such as phone conversation, interview, etc., in which some words often have specific meanings or some words or phrases have specific usages, and performs the most preferential analysis of wrong answers according to the context when the first recognition module 5 recognizes the corresponding context; the second recognition module 6 determines the morphism of the current sentence, e.g. when it is done now, when it is in the past, etc., based on verbs or phrases with obvious characteristics.
It is worth mentioning that, when multi-sentence analysis is performed, the first recognition module 5 judges the context of the current sentence according to the context sentence; when performing single sentence analysis, the first recognition module 5 does not recognize the context; when the second recognition module 6 determines the language state of the sentence, the language state analysis is performed according to the specific sentence usage or the noun representing the specific time, for example, the word representing the future or appearing tomorrow when the current sentence usage is performed; after the first recognition module 5 and the second recognition module 6 recognize the results, the third recognition module 7 judges each word in the sentence according to the recognition results of the first recognition module 5 and the second recognition module 6, and judges spelling, tense, and the like; for the verb, the judgment of word tense is carried out according to specific language state after the spelling is confirmed, and in the case of correct spelling, the tense of the verb is in a main investigation position, and the like.
When assigning, an English teacher can assign values according to the difficulty of knowledge points, the difficulty of understanding degree of corresponding culture background and the difficulty of word spelling, and can also assign values according to the sequence of knowledge points arranged in a textbook, and the assignment results are restored by the knowledge points and the knowledge points.
The second assignment module 9 performs the first assignment of the error degree according to the association degree between the incorrect word meaning and the sentence; the second assignment module 9 performs secondary assignment of the error degree according to the difference degree between the tense of the error word and the morpheme of the sentence; the second assignment module 9 performs three assignments of error degrees according to the context of the current sentence; the second assignment module 9 assigns the error degree four times according to the phrase structure; the second assignment module 9 calculates a final assignment result according to the first assignment, the second assignment, the third assignment, and the fourth assignment.
Finally, according to the adjustment between the first assignment module 8 and the second assignment module 9, a value is assigned to the wrong answer, the processing module 11 integrates the answers of all the questions, integrates the assignments of all the wrong answers, calculates the dispersion of the values, classifies the dispersion, and provides corresponding guidance for students in the same class.
The above embodiments are merely illustrative of the technical ideas and features of the present invention, and are intended to enable those skilled in the art to understand the contents of the present invention and implement the present invention, and not to limit the scope of the present invention. All equivalent changes or modifications made according to the spirit of the present invention should be covered within the protection scope of the present invention.
Claims (8)
1. An intelligent English teaching system based on error dispersion check is characterized by comprising:
the data module is used for recording all the standard answers and the wrong answers of the test questions;
the answer module is used for solving the test questions by the students;
the first decomposition module is used for decomposing sentences into words;
the analysis module is used for analyzing words obtained by decomposing sentences by the first decomposition module;
the second decomposition module is used for decomposing the sentence into phrases;
a first recognition module for recognizing a context of a sentence;
the second recognition module is used for recognizing the language state of the sentence;
the third recognition module is used for carrying out word recognition according to the sentence context recognized by the first recognition module and the language state recognized by the second recognition module;
the first assignment module is used for carrying out difference assignment according to the sentence context recognized by the first recognition module, the sentence morphism recognized by the second recognition module and the words recognized by the third recognition module;
a detection module to detect an error condition;
the second assignment module is used for assigning the error degree according to the error condition detected by the detection module;
a classification module for classifying errors according to the error conditions detected by the detection module;
the processing module is used for calculating discrete values according to the assignment results of the first assignment module and the second assignment module in the classification results of the classification module;
the teaching module is used for classified teaching according to the discrete values calculated by the processing module;
when multi-sentence analysis is carried out, the first recognition module judges the context of the current sentence according to the context sentence; when single sentence analysis is carried out, the first recognition module does not recognize the context; the first assignment module carries out primary assignment according to the sequence of the context, the semantics and the language state of the sentence in English learning respectively, the first assignment module carries out modification of sentence assignment according to the length of the word and the confusion degree of the related word, and the result is used as secondary assignment.
2. The intelligent English teaching system based on error dispersion degree check of claim 1, wherein: the first decomposition module decomposes the sentence into a plurality of words according to intervals between words of each sentence.
3. The intelligent English teaching system based on error dispersion degree check of claim 2, wherein: the analysis module analyzes words one by one, the analysis module judges the part-of-speech range of the words according to the spelling and the form of the words, and the analysis module finally confirms the part-of-speech of the words through the parts-of-speech of the words before and after.
4. The intelligent English teaching system based on error dispersion degree check of claim 3, wherein: the second decomposition module judges the connection relation of the front word and the rear word according to the word part of speech obtained by the analysis module, and decomposes the sentence into a plurality of phrases in the form of a main phrase, a subordinate phrase, a moving phrase and a partial phrase according to the connection relation of the front word and the rear word.
5. The intelligent English teaching system based on error dispersion degree check of claim 4, wherein: due to repeated occurrences of the words in the main phrase, the verb phrase and the bias phrase, when the phrases decomposed by the second decomposition module are analyzed, all the phrases are included in the analysis range, that is, the phrases with the overlapped parts are included in the analysis range.
6. The intelligent English teaching system based on error dispersion degree check of claim 5, wherein: and the second recognition module judges the language state of the sentence according to the verb form obtained by the analysis of the analysis module and the phrase form decomposed by the second decomposition module.
7. The intelligent English teaching system based on error dispersion degree check of claim 6, wherein: the second assignment module carries out primary assignment of the error degree according to the association degree between the word meaning of the error word and the sentence; the second assignment module carries out secondary assignment of the error degree according to the difference degree between the tense of the error word and the morpheme of the sentence; the second assignment module carries out three-time assignment of the error degree according to the context of the current sentence; the second assignment module carries out four-time assignment of the error degree according to the phrase structure; and the second assignment module calculates a final assignment result according to the first assignment, the second assignment, the third assignment and the fourth assignment.
8. The intelligent English teaching system based on error dispersion degree check of claim 7, wherein: when the classification module performs error classification, and when various errors occur, the classification module classifies sentences into the errors respectively according to the error conditions, namely, a single sentence can be simultaneously classified into a plurality of types.
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