CN106951406B - Chinese reading ability grading method based on text language variables - Google Patents
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Abstract
The invention discloses a classification method of Chinese reading ability based on text linguistic variables, which comprises the following steps: 1) finding a text segment related to reading and understanding answers of the questions in the text, and determining a source text; 2) calculating a language variable of the source text in the step 1) as an independent variable; 3) the tested reading comprehension score is regarded as the reference of reading comprehension capacity and text difficulty and is used as a dependent variable; 4) calculating a Pearson product-difference correlation coefficient between the independent variable in the step 2) and the dependent variable in the step 3); 5) and 4) sorting the correlation coefficients in the step 4) from high to low, screening out independent variables at the first 5 of the sorting, determining the optimal independent variable of the prediction dependent variable by adopting a least square method, and deriving an optimal matching function. The method distinguishes the reading cognitive characteristics in the reading comprehension test and the non-reading comprehension test, has high fitting goodness and accords with the Chinese language characteristics; the calculation is simple and quick, and the method has expansibility and high accuracy.
Description
Technical Field
The invention belongs to the technical field of grading methods of reading ability, and particularly relates to a grading method of Chinese reading ability based on text language variables.
Background
Reading comprehension is a implicit ability, and judging the reading comprehension level requires finding observable references of physical properties, which are linguistic variables of text. In the english country, the common method for text classification is: and (3) taking the reading comprehension score of the student as the difficulty score of the reading text, and calculating and screening the optimal language variable for predicting the text difficulty score by adopting a least square method. The problems of the method are that: the reading understanding score of the student is affected by the reading understanding problem, namely the same text, different reading understanding problems, and the score of the student may be completely different, but researchers in English countries do not calculate how the reading understanding problem affects the reading understanding score, and the neglect is likely to make the reading understanding score difficult to truly reflect the difficulty of reading the text.
In the research of Chinese reading classification, the text classification method of traditional Chinese and simplified Chinese mostly refers to the method of English national researchers, and has certain rationality. However, English is a pinyin character and is in a coincidence state, Chinese is an ideogram and is in a coincidence state, and the optimal predictive variable of the difficulty of English text is not necessarily suitable for Chinese.
In a specific method, Chinese researchers calculate variables such as syllable number, stroke number, word length, word frequency, sentence length and the like contained in the text, take the variables as independent variables, take grade or reading comprehension score of a student as dependent variables, and find out a best matching function formula by adopting regression analysis. These studies neglected the difference between the reading method at the time of reading comprehension test and the reading method at the time of non-test. For example, if the reading comprehension problem involves a difficult word, the student's level of processing of the difficult word greatly affects his reading comprehension score (i.e., the text difficulty score as a dependent variable), whereas in non-test reading, the student may skip the word. In other words, students treated the same linguistic variable differently in both cases, and the students perceived difficulty differently, but prior studies did not distinguish.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a classification method of Chinese reading ability based on text language variables, the grade of the reading ability is obtained by calculating the difficulty grade of a target text, the fitting goodness is high, the classification method accords with the Chinese language characteristics, the calculation is simple and quick, and the classification method has the advantages of expansibility and high accuracy.
To achieve the above object, the present invention adopts the following method
A Chinese reading ability grading method based on text language variables comprises the following steps:
1) determining a tested text and a reading understanding question of a reading understanding test, and finding a text segment related to an answer corresponding to each reading understanding question in the text as a source text;
2) respectively calculating language variables of source texts corresponding to the reading and understanding problems in the step 1) as independent variables;
3) testing a subject by using the text of the step 1) and the reading understanding problem, and referring the reading understanding score of the subject to reading understanding capability as a dependent variable;
4) calculating a Pearson product-difference correlation coefficient between the independent variable in the step 2) and the dependent variable in the step 3);
5) sorting the correlation coefficients obtained in the step 4) from high to low, screening out independent variables sorted at the first 5 bits, determining the optimal linguistic variable of the prediction dependent variable by adopting a least square method, and deriving an optimal matching function.
The type of the reading and understanding problem in the step 1) is the whole process reflecting the tried reading and understanding psychology, including feeling, perception, memory, thinking and imagination.
The independent variables in the step 2) have 6 types, and the 6 types of independent variables are respectively connected with characters, words, sentences, paragraphs, chapters and words; the independent variable calculation method comprises the following steps: the method comprises the steps of calculating the repeated times of words by adopting a Chinese word frequency tool, calculating mature words by adopting Chinese Text Analyzer software and a modern Chinese language corpus word frequency table, and calculating word connection and stroke number respectively by adopting a Text readability index automatic analysis system.
The dependent variable of the step 3) adopts a T score, and the calculation step and definition of the T score are as follows:
firstly, reviewing a test paper to be tested to obtain the original average score of all the tests of each reading understanding problem; each of the above original average scores is then converted to a percentage, and the formula is: percent is original average score/full score value multiplied by 100 percent; the full score is the full score of the reading understanding problem corresponding to the original average score;
and (3) performing Z fraction processing on the percentage by taking the piece as a unit, wherein the formula is as follows: z-fraction (x-mu)/sigma,
wherein:
σ is the standard deviation; x is the percentage of the single reading comprehension problem of the text; μ is the mean of the percentages of all read understanding questions of this text; n is the number of reading understanding problems of the text;
and converting the obtained Z fraction into a T fraction, wherein the formula is as follows: t fraction is 500+10 × Z fraction.
The calculation formula of the Pearson product difference correlation coefficient in the step 4) is as follows:
in the formula:
p-Pearson product difference correlation coefficient
m: reading and understanding the number of questions
Yi: t-score of ith reading comprehension question;
Xi: the independent variable corresponding to the ith reading understanding problem;
The specific method for deriving the optimal matching function in the step 5) is as follows:
sorting the Pearson Product-difference correlation coefficients obtained in the step 4) from high to low, screening out independent variables sorted at the first 5 bits, inputting the screened independent variables and the dependent variables obtained in the step 3) into Statistical Product and Service Solutions software to perform least square method multiple linear regression analysis, and according to the output multiple judgment coefficient R2And (3) checking the goodness of fit, and outputting the best matching function as follows:
Yk＝β0+β1χ1+β2χ2+β3χ3+
in the above formula, Yk: the predicted dependent variable;
X1，X2，X3: the best argument to predict reading comprehension and text difficulty.
β1,β2,β3: is a partial regression coefficient
: random error
The best match function is a hierarchical formula.
The invention has the advantages that: the method distinguishes the cognitive characteristics of reading in a reading comprehension test and reading in a non-reading comprehension test from the visual angle of the information processing psychological theory, adopts a method for analyzing language variables in an answer source text of a reading comprehension problem, has high fitting goodness, achieves a composite correlation coefficient of 0.97 and fitting goodness of 94 percent, and accords with the language characteristics of Chinese; the calculation is simple and quick, and the method has expansibility and high accuracy.
Drawings
Fig. 1 is a flow chart of a method for grading chinese reading ability based on text linguistic variables according to the present invention.
Detailed Description
As shown in fig. 1, the method for grading chinese reading ability based on text linguistic variables of the present invention comprises the steps of:
1) determining that the tested: students who were tried in the first two years are determined. According to the 'standard of Chinese course of compulsory education in education department', students in the year stage require 3500 commonly used Chinese characters to be recognized in an accumulated mode, 99.48 percent of the 'modern commonly used word list' is covered in an accumulated mode, and a better test foundation is achieved;
2) the subject Chinese textbook subject is sorted: in view of the fact that the reading subject matters of students are factors influencing the reading comprehension capacity, for example, the application subject matters are generally more difficult than the literature subject matters, the text of the reading comprehension test is selected according to the subject matter distribution in the Chinese text book, so that the real level of the reading comprehension of the students is favorably reflected, and the subject matters in the Chinese text books of the eight-year level and the nine-year level of the human education version are analyzed;
3) selecting reading test materials according to the sorted subjects: in view of the fact that China has no standardized and large Chinese reading comprehension capability test paper aiming at native languages, standardized reading test texts and reading comprehension problems used by primary and secondary school students in Taiwan and hong Kong areas in China are adopted, and subject distribution is consistent with distribution of Chinese teaching materials in junior middle schools in continental areas in China.
4) And determining a reading problem by combining the cognitive psychological process of reading of a reader: reading is a process of psychologically processing and processing text information, the visual angle of information processing psychology is determined firstly, three types of reading comprehension problems are determined according to thinking processes (feeling, perception, memory, thinking and imagination) of students from low to high, namely entering and extracting, synthesizing and explaining, countering and evaluating, the tested reading comprehension capability is comprehensively examined, and the grading of the reading comprehension capability is realized.
5) The test subjects were tested for reading comprehension: after the tested subject is subjected to standardized test, the test paper is read in batches uniformly, and the reliability and the effectiveness of a marker are ensured. The read comprehension score to be tested refers to the read comprehension ability as a dependent variable.
6) And (3) carrying out reliability and validity check on the test paper: the overall reliability of the test paper is 0.9, the structural validity exceeds 0.8, the requirements are met, and a higher standard is achieved.
7) Based on the reading understanding problem, sorting the text independent variables:
when the reading in the reading comprehension test and the reading in the non-reading comprehension test are faced, the tested difficult words and difficult sentences and the paragraphs formed by the difficult words and the difficult sentences are different from the reading mode and the processing mode of the chapters, so that the tested difficult words and difficult sentences are different in difficulty feeling. In the reading comprehension test, the linguistic variables that make the difficulty of the test are those that affect their ability to solve the reading comprehension problem. Text segments of the text for each reading understanding answer to the question are determined, which are source texts for analyzing text language arguments.
Specific linguistic variables are as follows:
chapter: number of paragraphs in source text chapters; total word count and total word count; non-repeating words and non-repeating words.
Paragraph: the number of paragraphs in a paragraph of source text, the number of sentences in a paragraph, and the number of repeated and non-repeated words and words.
The sentence: the sentence numbers of compound sentences, simple sentences and clauses in the source text sentences and the percentage of the total number of the compound sentences, the simple sentences and the clauses, and the number of repeated and unrepeated words and characters in the three types of sentences.
Non-aligned word frequency and word frequency (own word frequency and own word frequency): repeating the self word frequency and the self word frequency for 1-10 times and the percentage of the self word frequency.
Word frequency (best-case) of comparison: the object of comparison is 'modern Chinese common word list'; firstly, dividing into vocabulary words (N is 14629) and non-vocabulary words; the vocabulary words are divided into easy words (N-300) and difficult words (N-11629); the easy words are divided into high frequency words (N ═ 1000), medium frequency words (N ═ 1001-.
Word connection: part of speech in the source text, syntactic structure.
And (3) introducing the variables in the source Text into a word frequency tool on an online website of the Chinese language database to calculate the repetition times of words, introducing Chinese Text Analyzer software and a word frequency table of the modern Chinese language database to calculate ripe words, and calculating word connection and stroke number by using a Text readability index automatic analysis system 2.3.
8) Sorting the text dependent variables:
(a) the reading comprehension test paper is reviewed according to the scoring standard, the original average score of each reading comprehension problem is respectively obtained, the formula is,
in the formula (I), X1···XnObserved value of original score;
n is the number of trials.
(b) The original average score of each reading understanding problem is converted into a percentage, and the formula is,
percent is original average score/full score value multiplied by 100 percent; ②
In the second expression, the full score is the full score of the reading comprehension problem corresponding to the original average score
(c) Converting the percentage into a standard fraction Z fraction by taking the piece as a unit, and obtaining the formula,
formula (III):
σ is the standard deviation; x is the percentage of the single reading comprehension problem of the text; μ is the mean of the percentages of all read understanding questions of this text; n is the number of reading comprehension questions of the text.
(d) In order to avoid negative values and decimal points, the Z fraction obtained in the step c) is converted into a T fraction, and the formula is as follows:
t fraction of 500+10 XZ
The standard mean value of the T scores meets 500 scores and is in normal distribution.
9) Screening independent variables:
calculating a Pearson product-difference correlation coefficient between the independent variable calculated in the step 7) and the dependent variable processed in the step 8).
The calculation formula of the correlation coefficient of the Pearson product difference is as follows:
in the formula:
p-Pearson product difference correlation coefficient
Yi: the T-score of the ith reading understanding question,
1 · m;
Xi: the ith reading understands the argument corresponding to the question,
m is the number of reading comprehension questions.
And respectively determining the independent variables with significant correlation with the dependent variables in the 6 classes of independent variables, namely P <0.05, sorting the independent variables from high to low according to the Pearson product-difference correlation coefficient, and screening out the independent variables with the correlation coefficient value listed as the top 5 from each class.
10) Fitting the best function
Inputting the dependent variable and the independent variable selected in the step 9) into Statistical Product and Service Solutions (SPSS) software, performing least square multiple linear regression analysis, and determining multiple coefficient R according to the output2And (3) checking the goodness of fit: satisfy goodness of fit R2Approaching 1; the variance expansion factor VIF is less than 10; the significance P satisfying the regression coefficient is less than 0.05. Determining the output best match function is
Yk＝β0+β1χ1+β2χ2+β3χ3+ ⑥
In the formula, YkThe prediction dependent variable is the result of the quantitative relation between the independent variable and the dependent variable,
X1，X2，X3to predict the best arguments for reading comprehension and text difficulty,
β1,β2,β3is a partial regression coefficient
Random error.
Formula is the classification formula, YkBoth reading comprehension scores and text difficulty scores. A high reading comprehension ability score implies a low text difficulty. Multiple decision coefficient R of the formula20.94, the goodness of fit is high, accounting for the 94% variation in reading comprehension and text difficulty.
The method distinguishes the cognitive characteristics of reading in the reading comprehension test and reading in the non-reading comprehension test from the view point of the information processing psychology theory, adopts a method for analyzing language variables in an answer source text of the reading comprehension problem, adopts a standardized T score to refer to reading comprehension capability and text difficulty, has high fitting goodness, and accords with the language characteristics of Chinese. The method is tested to finish the reading comprehension problems of 1-2 channels, namely the reading comprehension capacity of the user can be measured and calculated, the composite correlation coefficient is 0.97, the goodness of fit is 94%, the calculation is simple and quick, and the method has good applicability and popularization.
Claims (3)
1. A classification method of Chinese reading ability based on text linguistic variables is characterized in that: the method comprises the following steps:
1) determining a tested text and a reading understanding question of a reading understanding test, and finding a text segment related to an answer corresponding to each reading understanding question in the text as a source text;
2) respectively calculating language variables of source texts corresponding to the reading and understanding problems in the step 1) as independent variables;
3) testing a subject by using the text of the step 1) and the reading understanding problem, and referring the reading understanding score of the subject to reading understanding capability as a dependent variable;
4) calculating a Pearson product-difference correlation coefficient between the independent variable in the step 2) and the dependent variable in the step 3);
5) sorting the correlation coefficients obtained in the step 4) from high to low, screening out independent variables sorted at the first 5 bits, determining the optimal linguistic variable of the prediction dependent variable by adopting a least square method, and deriving an optimal matching function;
the type of the reading and understanding problem in the step 1) is a whole process reflecting the tried reading and understanding psychology, including feeling, perception, memory, thinking and imagination;
the independent variables in the step 2) have 6 types, and the 6 types of independent variables are respectively connected with characters, words, sentences, paragraphs, chapters and words; the independent variable calculation method comprises the following steps: calculating the repeated times of words by adopting a Chinese word frequency tool, calculating cooked words by adopting Chinese Text Analyzer software and a word frequency table of a modern Chinese language database, and respectively calculating word connection and stroke number by adopting a Text readability index automatic analysis system;
the dependent variable of the step 3) adopts a T score, and the calculation step and definition of the T score are as follows:
firstly, reviewing a test paper to be tested to obtain the original average score of all the tests of each reading understanding problem; each of the above original average scores is then converted to a percentage, and the formula is: percent is original average score/full score value multiplied by 100 percent; the full score is the full score of the reading understanding problem corresponding to the original average score;
and (3) performing Z fraction processing on the percentage by taking the piece as a unit, wherein the formula is as follows:
z fraction (x- μ)/σ,
wherein:
σ is the standard deviation; x is the percentage of the single reading comprehension problem of the text; μ is the mean of the percentages of all read understanding problems of this text; n is the number of reading understanding problems of the text;
and converting the obtained Z fraction into a T fraction, wherein the formula is as follows: t fraction is 500+10 × Z fraction.
2. The method of claim 1 for ranking chinese language readability based on text linguistic variables, wherein: the calculation formula of the Pearson product difference correlation coefficient in the step 4) is as follows:
in the formula:
p-Pearson product difference correlation coefficient
m: reading and understanding the number of questions
Yi: t-score of ith reading comprehension question;
Xi: the independent variable corresponding to the ith reading understanding problem;
3. The method of claim 2, wherein the method comprises the following steps:
the specific method for deriving the optimal matching function in the step 5) is as follows:
sorting the Pearson Product-difference correlation coefficients obtained in the step 4) from high to low, screening out independent variables sorted at the first 5 bits, inputting the screened independent variables and the dependent variables obtained in the step 3) into Statistical Product and Service Solutions software to perform least square method multiple linear regression analysis, and according to the output multiple judgment coefficient R2And (3) checking the goodness of fit, and outputting the best matching function as follows:
Yk＝β0+β1χ1+β2χ2+β3χ3+
in the above formula, Yk: the predicted dependent variable;
χ1，χ2，χ3: predicting the optimal independent variable of reading comprehension ability and text difficulty;
β1,β2,β3: partial regression coefficients;
: random error
The best match function is a hierarchical formula.
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| CN107977362B (en) * | 2017-12-11 | 2021-05-04 | 中山大学 | Method for grading Chinese text and calculating Chinese text difficulty score |
| CN107977449A (en) * | 2017-12-14 | 2018-05-01 | 广东外语外贸大学 | A kind of linear model approach estimated for simplified form of Chinese Character readability |
| CN110020377B (en) * | 2018-01-02 | 2021-02-09 | 北大方正集团有限公司 | Network reading activity interaction method, device, server, terminal and storage medium |
| CN108491451B (en) * | 2018-02-27 | 2021-09-17 | 北京云知学科技有限公司 | English reading article recommendation method and device, electronic equipment and storage medium |
| CN109406926A (en) * | 2018-09-10 | 2019-03-01 | 国网江苏省电力有限公司连云港供电分公司 | The calculation method that environmental factor influences leakage current of an arrester |
| CN110895787B (en) * | 2018-09-10 | 2021-06-22 | 周刚 | Method for dynamically matching English corpus difficulty and student ability analysis |
| CN109933668B (en) * | 2019-03-19 | 2021-03-26 | 北京师范大学 | Hierarchical evaluation modeling method for readability of simplified Chinese text |
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