English word test method and system, storage medium and electronic equipment

Abstract

The invention provides an English word test method and system, a storage medium and electronic equipment. The method comprises the following steps: obtaining word knowledge items to be tested; the word knowledge items comprise one or more combinations of word recognition, word spelling and fixed matching complete form filling; generating test example sentences and test answers containing a preset number of word knowledge items; and feedback information of a user for the test example sentence is obtained, and whether the word knowledge item is mastered or not is determined according to the feedback information and the test answer. According to the English word testing method and system, the storage medium and the electronic equipment, whether the English words are mastered or not is accurately tested based on the specific context, and the learning efficiency and learning effect of the English words are effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular relates to an English word testing method, system, storage medium and electronic equipment. Background Art

[0002] Memorizing English vocabulary plays a crucial role in language learning and practical application. First, English words are the fundamental units of language, and mastering a sufficient vocabulary is fundamental to listening, speaking, reading, and writing. A rich vocabulary improves language comprehension and enables more accurate and fluent expression. Second, in the context of globalization, English is an international language, and vocabulary size directly impacts cross-cultural communication. Whether in daily communication, academic research, or career development, a sufficient vocabulary is crucial for effective communication. Furthermore, words are the carriers of culture. By learning English vocabulary, we can better understand the history, literature, and lifestyle of English-speaking countries and broaden our horizons.

[0003] Insufficient English vocabulary is a common problem faced by many English learners. In order to improve English vocabulary learning, people use repetition memory method, association memory method, root and affix memory method to memorize English vocabulary. At the same time, testing whether a word is mastered is a necessary means and process for word memorization. In the prior art, the methods for testing whether a word is mastered mainly include the following methods:

[0004] (1) Display the spelling of the word and let the user choose whether to know it or not.

[0005] (2) Display the spelling of a word and display multiple groups of word meanings (one group covers all the meanings of the word) for the user to choose; or display Chinese and display multiple spellings for the user to choose.

[0006] (3) Display the spelling of a word, if there is any missing, and ask the user to fill it in; or display Chinese and ask the user to write the spelling from memory.

[0007] However, the above method has the following shortcomings:

[0008] (1) The test is not conducted in a specific context, resulting in inaccurate detection results. This is because the meaning of a word in a dictionary is often different from the expression in a specific context, resulting in the user not truly mastering the meaning of the word in the test.

[0009] (2) The detection is general and multiple word meanings are detected at once. Users are likely to overlook some word meanings, resulting in unrealized meanings for familiar words.

[0010] (3) The test is not comprehensive enough. Word deformation and fixed word collocation are the key points of word learning and are also test points, but they are not included in the test scope. Summary of the Invention

[0011] The present invention provides an English word testing method, system, storage medium and electronic device, which accurately test whether English words are mastered based on specific contexts, effectively improving the learning efficiency and learning effect of English words.

[0012] In a first aspect, the present invention provides an English word testing method, which includes the following steps: obtaining word knowledge items to be tested; the word knowledge items include one or more combinations of word recognition, word spelling and fixed collocation cloze; generating test sentences and test answers containing a preset number of the word knowledge items; obtaining user feedback information on the test sentences, so as to determine whether the word knowledge items are mastered based on the feedback information and the test answers.

[0013] In an implementation of the first aspect, the word recognition includes one or more combinations of word meaning recognition, word deformation recognition, and word fixed collocation meaning recognition; the word spelling includes one or a combination of the spelling of the word prototype and the spelling of the word deformation; the word deformation includes the comparative form, superlative form, noun plural form, third person singular form, past tense, past participle, and present participle of the word; the fixed collocation cloze test includes one or more combinations of habitual collocation, word grammatical structure, idioms, and phrasal verbs.

[0014] In an implementation of the first aspect, the method further includes:

[0015] When the word knowledge item is word recognition, the word corresponding to the word knowledge item in the test sentence is marked with a specific symbol; the test answer includes the Chinese translation of the test sentence and the meaning of the word corresponding to the word knowledge item; when the word knowledge item is used in the test sentence in the form of word deformation, the test answer also includes the original spelling of the word;

[0016] When the word knowledge item is a word spelling, the word corresponding to the word knowledge item in the test sentence is replaced by a special symbol, and the corresponding word meaning is provided; the test answer includes the Chinese translation of the test sentence and the correct spelling of the word in the test sentence; when the word knowledge item is used in the test sentence in the form of word deformation, the test answer also includes the original spelling of the word;

[0017] When the word knowledge item is a fixed collocation cloze test, the word corresponding to the word knowledge item in the test sentence is marked with a specific symbol; the test answer includes the Chinese translation of the test sentence, the corresponding fixed collocation meaning, and the spelling of the word in the corresponding cloze test;

[0018] The test answers are displayed or not displayed according to the user's customized settings.

[0019] In an implementation of the first aspect, generating a test sentence containing a preset number of word knowledge items includes the following steps:

[0020] Get a preset number of word knowledge items;

[0021] Inputting the word knowledge item into the AIGC macro model;

[0022] Obtain the test example sentences output by the AIGC large model.

[0023] In an implementation of the first aspect, generating a test sentence containing a preset number of word knowledge items includes the following steps:

[0024] Build big data of test sentences;

[0025] Get a preset number of word knowledge items;

[0026] Generate a search key value based on the preset number of word knowledge items;

[0027] Obtaining a plurality of corresponding test sentences from the test sentence big data by searching the key value;

[0028] Randomly select a test sentence;

[0029] The construction of test sentence big data includes:

[0030] Get a test sentence;

[0031] Extracting a word set from the test sentence based on a large language model;

[0032] Extracting fixed collocations in the test sentences based on a large language model;

[0033] removing the words included in the idioms and phrasal verbs in the fixed collocation from the word set to obtain an updated word set;

[0034] Acquire the updated word set and the corresponding meanings of the fixed collocations in the test sentence based on the large language model;

[0035] Based on a preset number of words and / or fixed collocations to be tested in the test sentence, generating search key values for the words and / or fixed collocations to be tested according to corresponding word meanings and / or spellings;

[0036] The test sentence big data is constructed based on the search key value and the test sentence.

[0037] In an implementation of the first aspect, obtaining user feedback information regarding the test example sentence adopts any of the following methods:

[0038] Acquiring a feedback image of the user regarding the test example sentence, wherein the feedback image is a photographed image of a paper text containing a feedback mark of the test example sentence; recognizing the feedback mark in the feedback image based on an OCR algorithm to obtain the feedback information; wherein the user prints the test example sentence to obtain the paper text, and adds the feedback mark of the test example sentence to the printed text;

[0039] Acquire feedback information input by the user for the test sentence, where the feedback information is input by the user through an input device.

[0040] In an implementation of the first aspect, it also includes generating a word recitation table for printing and recitation or generating a word recitation table and generating new test sentences based on the word recitation table based on the feedback information; wherein the feedback information includes word knowledge items that have been mastered and word knowledge items that have not been mastered.

[0041] In a second aspect, the present invention provides an English vocabulary testing system, the system comprising a knowledge item acquisition module, a test case generation module, and a test result analysis module;

[0042] The knowledge item acquisition module is used to acquire word knowledge items to be tested; the word knowledge items include one or more combinations of word recognition, word spelling and fixed collocation cloze test;

[0043] The test case generation module is used to generate test sentences and test answers containing a preset number of word knowledge items;

[0044] The test result analysis module is used to obtain user feedback information on the test sentence, so as to determine whether the user has mastered the word knowledge item based on the feedback information and the test answer.

[0045] In a third aspect, the present invention provides an electronic device, comprising: a processor and a memory;

[0046] The memory is used to store computer programs;

[0047] The processor is used to execute the computer program stored in the memory, so that the electronic device performs the above-mentioned English word testing method.

[0048] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the above-mentioned English word testing method when executed by an electronic device.

[0049] As described above, the English word testing method, system, storage medium, and electronic device of the present invention have the following beneficial effects:

[0050] (1) Accurately test whether English words are mastered based on specific contexts, effectively improving the learning efficiency and effect of English words;

[0051] (2) It can test two or more vocabulary knowledge items at the same time, which effectively improves the efficiency of vocabulary testing;

[0052] (3) It can realize multi-dimensional testing of English words such as meaning, deformation, and fixed collocation, which improves the granularity of English word testing.

[0053] (4) The test sentences used each time are rich and varied, which effectively expands the learning content and improves the efficiency and effect of English learning.

[0054] (5) Using printed paper for testing is consistent with students’ usual learning habits and prevents students from being disturbed by electronic products. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 Shown is a schematic diagram of an application scenario of an English word testing method according to an embodiment of the present invention;

[0056] Figure 2 Shown is a flow chart of an English word testing method according to one embodiment of the present invention;

[0057] Figure 3 Shown is a schematic diagram of an embodiment of the present invention for generating an auxiliary recitation example based on two English words;

[0058] FIG4( a ) is a schematic diagram showing the display status of a test sentence and a test answer in one embodiment of the present invention;

[0059] FIG4( b ) is a schematic diagram showing the display status of a test sentence and a test answer in one embodiment of the present invention;

[0060] Figure 5 Shown is a schematic diagram of the layout of a knowledge file containing test examples and test answers in one embodiment of the present invention;

[0061] FIG6( a ) is a schematic diagram showing a test sentence in the first embodiment of the present invention;

[0062] FIG6( b ) is a schematic diagram showing a test sentence in the first embodiment of the present invention;

[0063] FIG7( a ) is a schematic diagram showing a test sentence in the second embodiment of the present invention;

[0064] FIG7( b ) is a schematic diagram showing a test sentence in the second embodiment of the present invention;

[0065] FIG8( a ) is a schematic diagram showing a test sentence in a third embodiment of the present invention;

[0066] FIG8( b ) is a schematic diagram showing a test sentence in the third embodiment of the present invention;

[0067] Figure 9 Shown is a schematic diagram of a feedback image including a feedback mark in one embodiment of the present invention;

[0068] FIG10( a ) is a front view of a word memorization table according to an embodiment of the present invention;

[0069] FIG10( b ) is a schematic diagram showing the back side of a word memorization table according to an embodiment of the present invention;

[0070] Figure 11 Shown is a schematic structural diagram of an English vocabulary testing system according to an embodiment of the present invention;

[0071] Figure 12 FIG. 1 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0072] The following describes the embodiments of the present invention through specific examples. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other unless they conflict.

[0073] It should be noted that the illustrations provided in the following embodiments are merely schematic illustrations of the basic concept of the present invention. Therefore, the illustrations only show components related to the present invention and are not drawn according to the number, shape, and size of components in actual implementation. In actual implementation, the type, quantity, and proportion of each component may be changed arbitrarily, and the component layout may also be more complex.

[0074] The following embodiments of the present invention provide an English word testing method, which can be applied to electronic devices. The electronic devices described in the present invention may include mobile phones 11, tablet computers 12, laptop computers 13, wearable devices, vehicle-mounted devices, augmented reality (AR) / virtual reality (VR) devices, ultra-mobile personal computers (UMPCs), netbooks, personal digital assistants (PDAs), etc. with wireless charging capabilities. The embodiments of the present invention do not impose any restrictions on the specific types of electronic devices.

[0075] The English word test method of the present invention can be implemented by combining a cloud server with a web page or an APP. The English word test method of the present invention can also be implemented by combining a local server with a web page or an APP.

[0076] The technical solutions in the embodiments of the present invention will be described in detail below with reference to the accompanying drawings in the embodiments of the present invention.

[0077] like Figure 2 As shown, in one embodiment, the English word testing method of the present invention includes steps S1 to S3.

[0078] Step S1: Obtain word knowledge items to be tested; the word knowledge items include one or more combinations of word recognition, word spelling and fixed collocation cloze.

[0079] Specifically, based on the user's actual English word learning needs, word knowledge items to be tested are obtained to meet the needs of different word test scenarios. It should be noted that the word knowledge items include word recognition, word spelling, and fixed collocation cloze. In other words, the recognition of a certain meaning of a word, the recognition of a word's deformation, the spelling of a word's prototype, the spelling of a word's deformation, or the cloze of a word's fixed collocation can be regarded as a word knowledge item. Among them, a word is the smallest language unit in a language that can be used independently. It is the basic element that constitutes a sentence. A word can be a letter (such as "a"), a syllable (such as "cat"), or even multiple syllables (such as "unbelievable"). Words have fixed spelling and pronunciation, and usually have a clear part of speech (such as noun, verb, adjective, etc.). For example, "apple", "run", "beautiful", etc. in English are all English words. For some words, they correspond to word prototypes and word deformations. The word deformation includes the comparative, superlative, plural, third person singular, past tense, past participle and present participle of the word. For some words, there are some fixed collocations, that is, they are often or always used with specific other words to form a conventional language structure. The fixed word collocations include habitual collocations, idioms, phrasal verbs and word grammatical structures. Among them, habitual collocations are high-frequency habitual combinations between words, such as "make a mistake". Phrasal verbs are overall meaning phrases composed of verbs + prepositions / adverbs, such as "look after". Idioms refer to fixed phrases with metaphorical meanings that cannot be interpreted literally, such as "rain cats and dogs". Word grammatical structures refer to phrases composed according to grammatical structures, such as "require somebody / something todo something". The word recognition includes word meaning recognition, word deformation recognition and word fixed collocation meaning recognition. The spelling includes word prototype spelling and word deformation spelling.

[0080] In one embodiment, appropriate vocabulary knowledge items can be selected based on the user's vocabulary test needs. For example, when a user needs to take a test, corresponding vocabulary knowledge items for CET-4 / CET-6, high school entrance examination syllabus vocabulary, college entrance examination syllabus vocabulary, and TOEFL / GRE / IELTS syllabus vocabulary can be obtained.

[0081] Step S2: Generate test sentences and test answers containing a preset number of the word knowledge items.

[0082] Specifically, for the word knowledge items, test sentences and test answers are generated. The test sentences contain a preset number of word knowledge items, thereby accurately testing whether a certain English word knowledge item has been mastered based on a specific context. Preferably, each test sentence contains two word knowledge items.

[0083] In one embodiment, generating a test sentence containing a predetermined number of word knowledge items includes the following steps:

[0084] 11) Obtain a preset number of word knowledge items.

[0085] The preset number of word knowledge items may be obtained according to a preset number set by the user. For example, when the test example sentence contains two word knowledge items, two word knowledge items are obtained.

[0086] 12) Input the word knowledge item into the AIGC macro model.

[0087] Among them, test sentences are generated based on the AIGC (Artificial Intelligence Generated Content) big model. The AIGC big model uses artificial intelligence technology to automatically generate text, images, audio, video, code and other content. Its core is to learn patterns in massive data through algorithm models, and generate new content that meets the needs based on user input (such as prompt words, parameters, etc.). Its essence is a data-driven innovation in content production methods, marking a paradigm shift from "manual creation" to "human-machine collaborative creation." Preferably, the AIGC big model can use a big model on the cloud, such as DeepSeek, or deploy a private AIGC big model, such as DeepSeek R1 14B version.

[0088] For the AIGC large model, call the API and enter the prompt word. For example, when a test sentence contains a single word knowledge item, enter the prompt word as follows:

[0089] For word knowledge items related to word prototypes, the prompt words are: Use word: long, Part of speech: intransitive verb, Meaning: desire, Generate a set of sentences, Return format English: content line break Chinese: content.

[0090] For word knowledge items related to word deformation, the prompt words are: use the deformed word: slept, part of speech: adjective, meaning: keep, generate a rule sentence, and return the format English: content line break Chinese: content.

[0091] For word knowledge items related to fixed word collocations, the prompt words are: Use phrase: pay attention to, word meaning: pay attention, generate a set of sentences, and return the format English: content line break Chinese: content.

[0092] For another example, when a test sentence contains two word knowledge items, the input prompt words are as follows:

[0093] For word knowledge items related to word prototypes, the prompt words are: use the word: long, part of speech: intransitive verb, meaning: desire, and the word: meeting, part of speech: noun, meaning: meeting, to generate a set of sentences, and return the format English: content line break Chinese: content.

[0094] For word knowledge items related to word transformation, the prompt words are: use the word: pepper, part of speech: noun, meaning: pepper, and the transformed word: slept, part of speech: adjective, meaning: keep, to generate a sentence, and return the format English: content line break Chinese: content.

[0095] For word knowledge items related to fixed word collocations, the prompt words are: use the word: football, part of speech: noun, meaning: football, and the phrase: pay attention to, meaning: pay attention, to generate a sentence, and return the format English: content line break Chinese: content.

[0096] 13) Obtain the test sentences output by the AIGC large model.

[0097] The AIGC model generates test sentences based on the input prompts. Since the test sentences generated each time are rich and diverse, it can effectively expand the learning content and improve learning efficiency and results.

[0098] In another embodiment, generating a test sentence containing a predetermined number of word knowledge items includes the following steps:

[0099] 21) Build big data of test sentences.

[0100] The test sentence big data is generated based on the big data, wherein the test sentence big data stores search key values corresponding to a preset number of word knowledge items and a plurality of test sentences corresponding to the search key values.

[0101] The construction of test sentence big data includes:

[0102] a) Get a test sentence.

[0103] The test sentences can be generated based on the AIGC large model, or obtained from sentences in existing English exercises, test questions, and essays.

[0104] b) extracting a word set A from the test sentence based on the large language model.

[0105] Large Language Models (LLMs) are AI models designed to understand and generate human language. They are trained on large amounts of text data and can perform a wide range of tasks, including text summarization, translation, sentiment analysis, and more. LLMs are characterized by their massive size, containing billions of parameters, which helps them learn complex patterns in language data. These models are often based on deep learning architectures such as Transformers, which contributes to their impressive performance on various NLP tasks.

[0106] For example, the prompt words input into the large language model are as follows:

[0107] Extract all the words in the sentence "It doesn't require a new type of technology that people aren't already familiar with." and output the words and word prototypes. If the words are similar to the people type, the prototype still uses people instead of person. For example, assuming the input sentence is "As long as there have been codes, people have tried to break them.", the output should be a Json array: [["As","as"],["long","long"],["as","as"],["there","there"],["have","have"],["been","be"],["codes","code"],["people","people"],["have","have"],["tried","try"],["to","to"],["break","break"],["them","them"]]

[0108] c) extracting fixed collocations in the test sentences based on the large language model.

[0109] First, all fixed collocations of words included in the word set A are obtained. These fixed collocations include, but are not limited to, idioms, phrasal verbs, customary collocations, word grammatical structures, prepositional phrases, etc. Then, all fixed collocations B in the test sentence are extracted based on the large language model.

[0110] For example, the prompt words of the large language model are as follows:

[0111] Does the sentence "It doesn't require a new type of technology that people aren't already familiar with" contain the following collocations: "1. be required to do sth.; 2. as required; 3. require sb. to do sth.; 4. require of; 5. require sb. to do sth.; 6. familiar with;"? Output the collocation numbers in JSON array format.

[0112] d) removing words included in idioms and phrasal verbs in the fixed collocations from the word set to obtain an updated word set.

[0113] Among them, since the words that constitute idioms and phrasal verbs are irrelevant to their specific meanings, they need to be excluded to obtain an updated word set.

[0114] e) obtaining the updated word set and the corresponding meanings of the fixed collocations in the test sentence based on the large language model.

[0115] For example, the prompt words of the large language model are:

[0116] In the sentence "It doesn't require a new type of technology that people aren't already familiar with.", which of the following dictionary definitions agrees with the word "require"? The dictionary definition is: "1. to need something; to depend on somebody / something; 2. to make somebody do or have something, especially because it is necessary according to particular law or set of rules." Output the definition number in JSON array format. No explanation required.

[0117] f) Based on a preset number of words to be tested and / or fixed collocations in the test sentence, generating search key values for the words to be tested and / or fixed collocations according to corresponding word meanings and / or spellings.

[0118] Among them, through a certain encoding method, the search key value of the word to be tested and / or fixed collocation is generated according to the corresponding word meaning and / or spelling. For example, in the alphabetical order of the words, "word knowledge item type 1 + word knowledge item ID1 + word knowledge item type 2 + word knowledge item ID2... + word knowledge item type n + word knowledge item IDn" are merged and hashed as the search key value. Among them, n represents the number of word knowledge items. Among them, the hash adopts algorithms such as MD5. The ID of the word knowledge item is the unique identifier corresponding to the word knowledge item. The word knowledge item type is the specific content of word recognition, word spelling and fixed collocation cloze corresponding to the word.

[0119] For example, when a sentence contains a word knowledge item, the search key value is obtained as follows:

[0120] Sentence: It doesn't require a new type of technology that people aren't quite familiar with.

[0121] Word knowledge item: require in the sentence meaning. The word knowledge item ID is: 12345, and the meaning type is: S.

[0122] Coding method: S12345.

[0123] Search key value after MD5: 192404bede5083e4b75dfe508acf61d8.

[0124] For another example, if a sentence contains a word knowledge item, the search key value is obtained as follows:

[0125] Word knowledge item: The word knowledge item ID of the phrase familiar with is: 84735, and the type of word meaning is: P.

[0126] Coding method: P84735.

[0127] Search key value after MD5: 0b4d912a2803b8e2b9fb704b4ab56628.

[0128] For example, if a sentence contains two consecutive words in a knowledge item test, the search key value is obtained as follows:

[0129] Sentence: It doesn't require a new type of technology that people aren't quite familiar with.

[0130] The word knowledge item ID of the word "require" in the sentence meaning is: 12345, and the word knowledge item ID of "familiar with" is: 84735, and the alphabetical order of "familiar with" is before "require".

[0131] Coding method: P84735S12345.

[0132] The key value after MD5 is: a9ff35d6aa57e56e487fae39273e2aeb.

[0133] g) constructing the test sentence big data based on the search key value and the test sentence.

[0134] In particular, test example sentence generation conditions can be pre-set based on the user's proficiency, i.e., preset example sentence generation conditions can be obtained. The preset example sentence generation conditions include test example sentence difficulty, the user's English proficiency level, and the like. Based on the word knowledge items corresponding to the search key value and the preset example sentence generation conditions, multiple matching test example sentences can be obtained based on the large language model. In one embodiment, the sentence key value of the test example sentence is associated with the corresponding search key value, thereby obtaining the test example sentence.

[0135] The test example sentences and corresponding search key values are stored in big data, such as MongoDB, to construct the test example sentence big data.

[0136] 22) Obtain a preset number of word knowledge items.

[0137] Among them, according to the needs of word memorization, a preset number of word knowledge items are selected for testing.

[0138] 23) Generate a search key value based on the preset number of word knowledge items.

[0139] 24) Obtaining a plurality of corresponding test sentences in the test sentence big data by using the search key value.

[0140] The search key value is input into the test example sentence big data to obtain the corresponding test example sentence. When a test example sentence is needed, the search key value is input to obtain the corresponding test example sentence. The search key value is associated with the sentence key value of the test example sentence, and the associated sentence key value is used to obtain the corresponding multiple test example sentences.

[0141] For example, based on the meaning of the word "require" and the phrase "familiar with," we find the knowledge item IDs 12345 and 84735, respectively. These are combined into P84735S12345, yielding the search key value a9ff35d6aa57e56e487fae39273e2aeb. Based on the search key value, we obtain the sentence key value 2b5a98ea39ab75dc5c4af24ed5f65ff8, which matches the test example sentence. Based on this sentence key value, we find the sentence "It doesn't require a new type of technology that people aren't already familiar with."

[0142] 25) Randomly select a test sentence.

[0143] It should be noted that the AIGC large-scale model method of generating test sentences is time-consuming and expensive. For example, generating 100 sentences containing two test items takes an average of 300 seconds and costs an average of 0.58 yuan. Therefore, using big data to pre-build test sentences is also a good solution and can achieve the same diversity of test sentences as AIGC.

[0144] In one embodiment, in the test sentence, if Figure 3 As shown, the word knowledge items need to be marked so that users can quickly locate the word knowledge items.

[0145] When the word knowledge item is a word recognition, the word corresponding to the word knowledge item in the test sentence is marked with a specific symbol; the test answer includes the Chinese translation of the test sentence and the meaning of the word corresponding to the word knowledge item; when the word knowledge item is used in the test sentence in the form of word deformation, the test answer also includes the word prototype. For example, She longs for adventure beyond the horizon.

[0146] When the word knowledge item is a word spelling, the word corresponding to the word knowledge item in the test sentence is replaced by a special symbol, and the corresponding word meaning is provided. The test answer includes the Chinese translation of the test sentence and the correct spelling of the word in the test sentence; when the word knowledge item is used in the test sentence in the form of word deformation, the test answer also includes the original spelling of the word. For example, She *******[longing] for adventure beyond the horizon.

[0147] When the word knowledge item is a fixed collocation cloze test, the word corresponding to the word knowledge item in the test sentence is marked with a specific symbol. The test answer includes the Chinese translation of the test sentence, the corresponding fixed collocation meaning, and the spelling of the word in the corresponding cloze test.

[0148] Preferably, the test answers are displayed or not displayed according to the user's custom settings. In the present invention, the test sentences and the test answers can be displayed in paper form, or in electronic form such as apps and mini-programs. In the scenario of electronic display, Figure 4 (a) shows the test answer non-display mode; Figure 4 (b) shows the test answer display mode. In the scenario of paper display, the test answers can be printed or not printed according to the user's custom selection. When printing the test answers, the test answers can be folded or not to accommodate different test scenarios.

[0149] Step S3: Obtain user feedback information regarding the test sentence, and determine whether the user has mastered the word knowledge item based on the feedback information and the test answer.

[0150] Specifically, the user's feedback information on the test example sentence is obtained in any of the following ways:

[0151] (1) Obtaining a feedback image of the user regarding the test example sentence, wherein the feedback image is a photographed image of a paper document containing a feedback mark of the test example sentence; recognizing the feedback mark in the feedback image based on an OCR algorithm to obtain the feedback information. The user prints the test example sentence to obtain the paper document, and adds the feedback mark of the test example sentence to the printed document.

[0152] In order to facilitate users to take English vocabulary tests anytime and anywhere, the test sentences and the test answers can be printed as paper files. Figure 5 The layout of the paper document is shown. The user can mark whether they have mastered each test sentence on the paper document, that is, draw a feedback mark on the paper document. For example, the feedback mark √ is used to indicate that the word knowledge item has been mastered; the feedback mark × is used to indicate that the word knowledge item has not been mastered. Then, the user captures an image of the marked paper document and provides the captured image to the electronic device. The electronic device performs image recognition on the image through an OCR algorithm, obtains the feedback mark, and then generates the feedback information by counting the feedback marks. Figure 6(a)-Figure 8(b) That is, it is displayed as the feedback mark of different types of word knowledge items on paper documents in the present invention. Figure 9 The feedback image also includes the answer sheet number, answer sheet A / B side identification, user code and other identifications, which facilitates answer sheet identification.

[0153] (2) Obtaining feedback information input by the user regarding the test sentence, wherein the feedback information is input by the user through an input device.

[0154] The user provides feedback information regarding the test sentence input in the electronic device by manually inputting the feedback information. For example, the user may input the feedback information into the system using a stylus pen, or input the feedback information into the electronic device using a touch screen input method. For example, inputting √ indicates that the English vocabulary has been mastered, while inputting × indicates that the English vocabulary has not been mastered.

[0155] In one embodiment, the English word testing method of the present invention further includes generating a word recitation table for printing and recitation or generating a word recitation table and generating new test sentences based on the word recitation table based on the feedback information. The feedback information includes whether the word knowledge items have been mastered and whether the word knowledge items have not been mastered. As shown in Figure 10(a), the front of the word recitation table is used for recitation. As shown in Figure 10(b), the back of the word recitation table is used for self-testing. The left half of the back of the word recitation table only has words, no part of speech, and no meaning. The right half only has part of speech and meaning, and no spelling of the word. The spelling, part of speech, and meaning of the same word are in the same row, and the user can quickly find them. In particular, the back of the word recitation table can be folded from the middle, thereby optimizing the detection effect.

[0156] In one embodiment, the English vocabulary test method of the present invention further includes outputting the feedback information to a smart terminal associated with the user. The smart terminal associated with the user can be a parent's smart terminal or a teacher's smart terminal. Outputting the feedback information to the smart terminal enables parents and / or teachers to promptly learn the user's English vocabulary test results, facilitating timely follow-up and answering questions about the corresponding vocabulary knowledge items.

[0157] The protection scope of the English word testing method described in the embodiment of the present invention is not limited to the execution order of the steps listed in this embodiment. All solutions implemented by adding, reducing, or replacing steps in the prior art based on the principles of the present invention are included in the protection scope of the present invention.

[0158] An embodiment of the present invention also provides an English word testing system, which can implement the English word testing method described in the present invention. However, the implementation device of the English word testing system described in the present invention includes but is not limited to the structure of the English word testing system listed in this embodiment. All structural deformations and replacements of the existing technology made according to the principles of the present invention are included in the protection scope of the present invention.

[0159] like Figure 11As shown, in one embodiment, the English vocabulary testing system of the present invention includes a knowledge item acquisition module 111 , a test case generation module 112 and a test result analysis module 113 .

[0160] The knowledge item acquisition module 111 is used to acquire word knowledge items to be tested; the word knowledge items include one or more combinations of word recognition, word spelling and fixed collocation cloze.

[0161] The test case generation module 112 is connected to the knowledge item acquisition module 111 and is used to generate test sentences and test answers containing a preset number of word knowledge items.

[0162] The test result analysis module 113 is connected to the test case generation module 112 and is used to obtain user feedback information on the test sentence, so as to determine whether the user has mastered the word knowledge item based on the feedback information and the test answer.

[0163] Among them, the structures and principles of the knowledge item acquisition module 111, the test case generation module 112 and the test result analysis module 113 correspond one-to-one to the steps in the above-mentioned English word testing method, so they are not repeated here.

[0164] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices or methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of modules / units is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules or units, which can be electrical, mechanical or other forms.

[0165] Modules / units described as separate components may or may not be physically separate, and components displayed as modules / units may or may not be physical modules, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules / units may be selected based on actual needs to achieve the objectives of the embodiments of the present invention. For example, the functional modules / units in various embodiments of the present invention may be integrated into a single processing module, each module / unit may exist physically separately, or two or more modules / units may be integrated into a single module / unit.

[0166] Those skilled in the art should further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the composition and steps of each example according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0167] The embodiment of the present invention also provides a computer-readable storage medium. A person skilled in the art will understand that all or part of the steps in the method for implementing the above embodiment can be completed by instructing a processor through a program, and the program can be stored in a computer-readable storage medium, and the storage medium is a non-transitory medium, such as a random access memory, a read-only memory, a flash memory, a hard disk, a solid-state drive, a magnetic tape, a floppy disk, an optical disc, and any combination thereof. The above storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a digital video disc (DVD)), or a semiconductor medium (for example, a solid-state drive (SSD)), etc.

[0168] An embodiment of the present invention further provides an electronic device comprising a processor and a memory.

[0169] The memory is used to store computer programs.

[0170] The memory includes various media that can store program codes, such as ROM, RAM, magnetic disk, USB flash drive, memory card or optical disk.

[0171] The processor is connected to the memory and is used to execute the computer program stored in the memory so that the electronic device executes the above-mentioned English word testing method.

[0172] Preferably, the processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0173] like Figure 12 As shown, the electronic device of the present invention is in the form of a general-purpose computing device. Components of the electronic device may include, but are not limited to: one or more processors or processing units 121, memory 122, and a bus 123 connecting different system components (including memory 122 and processing unit 121).

[0174] Bus 123 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. Examples of these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0175] Electronic devices typically include a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device, including volatile and non-volatile media, removable and non-removable media.

[0176] The memory 122 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 1221 and / or cache memory 1222. The electronic device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 1223 may be used to read and write non-removable, non-volatile magnetic media ( Figure 12 Not shown, often called a "hard drive"). Although Figure 12Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 123 via one or more data medium interfaces. Memory 122 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of various embodiments of the present invention.

[0177] A program / utility 1224 having a set (at least one) of program modules 12241 may be stored, for example, in memory 122. Such program modules 12241 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may include an implementation of a network environment. Program modules 12241 generally implement the functions and / or methods of the embodiments described herein.

[0178] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, displays, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., network cards, modems, etc.). Such communication may be performed through input / output (I / O) interface 124. Furthermore, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) through network adapter 125. Figure 12 As shown, the network adapter 125 communicates with other modules of the electronic device via the bus 123. It should be understood that, although not shown in the figures, other hardware and / or software modules may be used in conjunction with the electronic device, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0179] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the present invention. Anyone skilled in the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by one of ordinary skill in the art without departing from the spirit and technical principles disclosed herein are intended to be covered by the claims of the present invention.

Claims

1. An English word testing method, characterized in that: The method comprises the following steps: Obtaining word knowledge items to be tested; the word knowledge items include one or more combinations of word recognition, word spelling, and fixed collocation cloze; Generating test sentences and test answers containing a preset number of the word knowledge items; Acquire user feedback information on the test example sentence to determine whether the user has mastered the word knowledge item based on the feedback information and the test answer.

2. The English word testing method according to claim 1, wherein: The word recognition includes one or more combinations of word meaning recognition, word deformation recognition, and word fixed collocation meaning recognition; the word spelling includes one or a combination of the spelling of the word prototype and the spelling of the word deformation; the word deformation includes the comparative form, superlative form, noun plural form, third person singular form, past tense, past participle, and present participle of the word; the fixed collocation cloze test includes one or more combinations of habitual collocation, word grammatical structure, idioms, and phrasal verbs.

3. The English word testing method according to claim 2, wherein: Also includes: When the word knowledge item is word recognition, the word corresponding to the word knowledge item in the test sentence is marked with a specific symbol; The test answer includes the Chinese translation of the test sentence and the meaning of the word knowledge item; When the word knowledge item is used in the test sentence in the form of word deformation, the test answer further includes the word prototype spelling; When the word knowledge item is a word spelling, the word corresponding to the word knowledge item in the test sentence is replaced by a special symbol, and the corresponding word meaning is provided; the test answer includes the Chinese translation of the test sentence and the correct spelling of the word in the test sentence; When the word knowledge item is used in the test sentence in the form of word deformation, the test answer further includes the word prototype spelling; When the word knowledge item is a fixed collocation cloze test, the word corresponding to the word knowledge item in the test sentence is marked with a specific symbol; the test answer includes the Chinese translation of the test sentence, the corresponding fixed collocation meaning, and the spelling of the word in the corresponding cloze test; The test answers are displayed or not displayed according to the user's customized settings.

4. The English word testing method according to claim 1, wherein: Generating a test sentence containing a preset number of word knowledge items includes the following steps: Get a preset number of word knowledge items; Inputting the word knowledge item into the AIGC macro model; Obtain the test example sentences output by the AIGC large model.

5. The English word testing method according to claim 1, wherein: Generating a test sentence containing a preset number of word knowledge items includes the following steps: Build big data of test sentences; Get a preset number of word knowledge items; Generate a search key value based on the preset number of word knowledge items; Obtaining a plurality of corresponding test sentences from the test sentence big data by searching the key value; Randomly select a test sentence; The construction of test sentence big data includes: Get a test sentence; Extracting a word set from the test sentence based on a large language model; Extracting fixed collocations in the test sentences based on a large language model; removing the words included in the idioms and phrasal verbs in the fixed collocation from the word set to obtain an updated word set; Acquire the updated word set and the corresponding meanings of the fixed collocations in the test sentence based on the large language model; Based on a preset number of words and / or fixed collocations to be tested in the test sentence, generating search key values for the words and / or fixed collocations to be tested according to corresponding word meanings and / or spellings; The test sentence big data is constructed based on the search key value and the test sentence.

6. The English word testing method according to claim 1, wherein: Obtaining user feedback on the test example sentence may be done in any of the following ways: Acquiring a feedback image of the user regarding the test example sentence, wherein the feedback image is a photographed image of a paper text containing a feedback mark of the test example sentence; recognizing the feedback mark in the feedback image based on an OCR algorithm to obtain the feedback information; wherein the user prints the test example sentence to obtain the paper text, and adds the feedback mark of the test example sentence to the printed text; Acquire feedback information input by the user for the test sentence, where the feedback information is input by the user through an input device.

7. The English word testing method according to claim 1, wherein: The method further comprises generating a word recitation table for printing and recitation according to the feedback information or generating a word recitation table and generating a new test sentence according to the word recitation table; The feedback information includes whether the word knowledge item has been mastered and whether the word knowledge item has not been mastered.

8. An English word testing system, characterized in that: The system includes a knowledge item acquisition module, a test case generation module and a test result analysis module; The knowledge item acquisition module is used to acquire word knowledge items to be tested; the word knowledge items include one or more combinations of word recognition, word spelling and fixed collocation cloze test; The test case generation module is used to generate test sentences and test answers containing a preset number of word knowledge items; The test result analysis module is used to obtain user feedback information on the test sentence, so as to determine whether the user has mastered the word knowledge item based on the feedback information and the test answer.

9. An electronic device, characterized in that: The electronic device includes: a processor and a memory; The memory is used to store computer programs; The processor is configured to execute the computer program stored in the memory, so as to enable the electronic device to perform the English word testing method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by an electronic device, the English word testing method according to any one of claims 1 to 7 is implemented.

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