CN117952796B - Reading teaching quality assessment method and system based on data analysis - Google Patents
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Abstract
The invention discloses a reading teaching quality assessment method and a system based on data analysis, which belong to the technical field of reading teaching quality assessment and comprise a resource module, a student analysis module, a reading acquisition module and a reading assessment module; the resource module is used for collecting various reading data and establishing a corresponding reading resource library according to the collected reading data; the student analysis module is used for carrying out student interest analysis and determining reading data of students; the reading acquisition module is used for acquiring reading state data of students in the reading process in real time and transmitting the acquired reading state data to the reading evaluation module; the reading evaluation module evaluates the reading quality of students to obtain reading state data, and evaluates the reading state data to obtain corresponding duration values, efficiency values and attitude coefficients; calculating a corresponding reading evaluation value according to the duration value, the efficiency value and the attitude coefficient; and displaying the obtained reading evaluation value to corresponding teachers and students in real time.
Description
Technical Field
The invention belongs to the technical field of reading teaching quality assessment, and particularly relates to a reading teaching quality assessment method and system based on data analysis.
Background
With the development of education informatization, reading education is increasingly important in the education field. However, how to effectively evaluate the reading teaching quality has been a problem facing the educator. The traditional reading teaching quality assessment method is usually carried out manually, such as by listening to lessons, observing, examining and the like. This approach is not only inefficient, but also susceptible to subjective factors, and it is difficult to ensure accuracy and objectivity of the assessment. Therefore, it is necessary to develop a reading teaching quality evaluation system based on computer technology. Based on the data analysis, the invention provides a reading teaching quality assessment method and a reading teaching quality assessment system based on the data analysis.
Disclosure of Invention
In order to solve the problems of the scheme, the invention provides a reading teaching quality assessment method and system based on data analysis.
The aim of the invention can be achieved by the following technical scheme:
A reading teaching quality evaluation system based on data analysis comprises a resource module, a student analysis module, a reading acquisition module and a reading evaluation module;
The resource module is used for collecting various reading data and establishing a corresponding reading resource library according to the collected reading data.
Further, the method for collecting the reading data comprises the following steps:
Acquiring each piece of student information, dynamically generating a corresponding student feature map according to the student information, and setting a corresponding reading range based on the student feature map;
Acquiring resource input budget, identifying each reading type in a reading range, marking the reading type as unit classification, counting the quantity proportion of each unit classification in a reference target, and marking the unit classification as reference proportion; obtaining a corresponding student feature map, and setting unit features of each unit classification according to the student feature map;
distributing resource input budget according to the unit characteristics;
Determining each piece of data information to be selected corresponding to each unit classification, prioritizing each piece of data information to be selected to obtain a first sequence, and determining corresponding target data information according to the ordering of each piece of data information to be selected in the first sequence and the corresponding cost; and collecting reading data according to each piece of target data information.
Further, the method for dynamically generating the student feature map according to the student information comprises the following steps:
Updating student information in real time, presetting a corresponding initial template, and carrying out feature recognition extraction on the student information according to the initial template to obtain initial features; and counting the number of student information corresponding to each initial feature and the number proportion corresponding to the corresponding number, and generating a corresponding student feature map according to each obtained initial feature and the corresponding number and number proportion.
Further, the method for determining the reading range comprises the following steps:
the manager presets the corresponding reference targets, acquires the reading records corresponding to the reference targets, identifies the reading types corresponding to the reading records, integrates the reading types corresponding to the reference targets, and forms the corresponding reading range.
Further, the method for allocating the resource investment budget according to the unit characteristics comprises the following steps:
According to the formula Calculating a corresponding distribution value;
Wherein: a lloci is the assigned value of the corresponding unit class; i represents the cell classification within the corresponding initial feature; i=1, 2, … …, n being a positive integer; j represents the corresponding initial feature, j=1, 2, … …, m being a positive integer; t preij represents the number ratio of the corresponding initial features of the corresponding unit classification; c peri denotes the reference duty cycle of the corresponding cell class; d (x ij) is the coefficient model, x ij is the read data for the respective cell class in the corresponding initial feature.
And allocating the resource input budget according to the allocation value corresponding to each unit classification.
Further, the expression of the coefficient model is:
; x ij is the read data for the respective cell class in the corresponding initial feature.
The student analysis module is used for carrying out student interest analysis and determining reading data of students.
Further, the working method of the student analysis module comprises the following steps:
Acquiring reading association data of students, and setting a corresponding reading interest statistical table according to the reading association data; the reading interest statistical table is used for counting each reading interest category and corresponding interest coefficient of the student;
Identifying each subject word in the reading record according to the reading associated data, and calculating the weight value of each subject word;
determining to-be-selected reading data in a reading resource library; counting the weight ratio of each subject term in the reading data to be selected, and forming a corresponding vector DP to be selected according to the weight ratio of each subject term;
According to the formula Calculating a corresponding recommended value; wherein: QW is a recommended value;
Recommending the corresponding to-be-selected reading data to the students according to the sequence of the recommended values from high to low, and determining the reading data corresponding to the students.
Further, the method for establishing the reading interest statistical table comprises the following steps:
determining each reading interest category of the student according to the reading association data, and counting the corresponding interest ratio of each reading interest category;
According to the formula Calculating an interest coefficient corresponding to the reading interest classification;
Wherein: delta is an interest coefficient; xz is the interest duty cycle; x1 is a threshold;
And establishing a corresponding reading interest statistical table according to the obtained reading interest classifications and the corresponding interest coefficients.
Further, the calculation formula of the weight value is:；
wherein: w c is a weight value; c represents a corresponding subject term, c=1, 2, … …, v being a positive integer; a=1, 2, … …, e is a positive integer; z perc represents the topic duty cycle of the corresponding topic word; delta ac is the corresponding interest coefficient.
Further, the method for determining the to-be-selected reading data in the reading resource library comprises the following steps:
marking the subject words with the weight values larger than the threshold value X2 as check words, and combining the check words into a check set; marking the number of check words as N, wherein N is not identical to N;
identifying the subject words corresponding to each reading data in the reading resource library to form a subject word set; checking each check word in the check set and the subject word set one by one, and marking 1 when the subject word set comprises the corresponding check word, otherwise marking 0; forming corresponding check vector ；
Will |Read data of >0.9N are marked as candidate read data.
The reading acquisition module is used for acquiring the reading state data of the students in the reading process in real time and sending the acquired reading state data to the reading evaluation module.
The reading evaluation module evaluates the reading quality of students to obtain reading state data, and evaluates the reading state data to obtain corresponding duration values, efficiency values and attitude coefficients;
Calculating a corresponding reading evaluation value according to the formula P rior=H(s)×[η×(b1×ST+b2 xXL);
Wherein: p rior is a reading evaluation value; b 1、b2 are all proportional coefficients, and the value range is 0<b 1≤1,0<b2 to be less than or equal to 1; η is an attitude coefficient; ST is a duration value; XL efficiency value; h(s) is a range judgment model; the expression of the range judgment model is S is the reading content; FU is the read content range;
And displaying the obtained reading evaluation value to corresponding teachers and students in real time.
A reading teaching quality assessment method based on data analysis comprises the following steps:
Collecting various reading data, and establishing a reading resource library according to the collected reading data;
analyzing the reading interests of students and determining the reading data of the students;
collecting reading state data of students in a reading process in real time;
acquiring reading state data, and evaluating the reading state data to acquire a duration value, an efficiency value and an attitude coefficient; calculating a corresponding reading evaluation value according to the duration value, the efficiency value and the attitude coefficient;
and displaying the reading evaluation value to corresponding teachers and students in real time.
Compared with the prior art, the invention has the beneficial effects that:
Through the mutual coordination among the resource module, the student analysis module, the reading acquisition module and the reading evaluation module, the whole course service for student reading is realized, and the manual participation degree is greatly reduced; by setting the resource module, automatic and intelligent reading data acquisition is realized, and a reading resource library is enriched; dynamically adjusting the acquired reading data according to the change condition of the students; the manual labor is greatly reduced, and valuable data is screened from a plurality of reading data; the method and the device provide high-value reading data for students. By generating the student characteristic diagram, the student detail can be known dynamically and intuitively, and the reading range of the student can be determined conveniently. By arranging the student analysis module, reading data is intelligently recommended according to student data, and personalized reading resource recommendation of students is realized.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art.
Fig. 1 is a functional block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, a reading teaching quality evaluation system based on data analysis comprises a resource module, a student analysis module, a reading acquisition module and a reading evaluation module;
The resource module is used for collecting various reading data in a summarizing way, and establishing a corresponding reading resource library according to the collected reading data. The specific method comprises the following steps:
acquiring relevant data of each student information such as grade, specialty, age and the like, wherein the specialty comprises corresponding disciplines; in the step, the student information does not need detailed student personal information, such as privacy information of names, photos, contact ways and the like; therefore, corresponding student information can be rapidly collected according to relevant data such as teaching records and the like; the method comprises the steps of presetting a corresponding initial template, wherein the initial template is preset and used for carrying out information identification and extraction according to corresponding characteristic items and mainly comprises the characteristic items related to reading ranges such as grades, professions and ages.
Performing feature recognition on the acquired student information according to a preset initial template, determining various initial features, and counting the quantity and quantity ratio corresponding to the initial features; generating a corresponding student characteristic map according to the obtained initial characteristics, quantity and quantity ratio, and generating the student characteristic map in a map form such as a histogram, a sector map and the like; dynamically updating the student feature map according to the change condition of the subsequent acquired student information; by generating the student characteristic diagram, the student detail can be known dynamically and intuitively, and the reading range of the student can be determined conveniently.
Determining the reading range of students according to the student characteristic diagram, such as determining various reading types possibly required by the conditions according to the grades, professions, ages and the like of the students, such as various small-classification reading types of extracurriculum reading data, chinese exercise data, past-year test papers and the like of a certain grade; the method can also preset reference targets, such as reading records of some excellent students, reading records of students in schools with higher teaching quality and the like, and various reading types corresponding to the initial characteristics are determined according to the reference targets so as to integrate and form corresponding reading ranges; if the north-size is taken as a reference target, reading records of students of the reference target are obtained, corresponding reading records irrelevant to teaching reading are removed, induction summarization is carried out according to the obtained reading records, reading classifications corresponding to all initial characteristics are formed, if specific reading data names are provided, the specific reading data names are required to be marked in the corresponding reading classifications, the specific reading data names are generally directly taken as the reading data, and then the corresponding reading classifications are determined according to the reading data; summarizing to form a reading range; while a plurality of reference targets may be set.
Acquiring resource input budget, namely inputting budget for purchasing reading resources, identifying each reading type in a reading range, marking the reading type as unit classification, and setting the unit classification according to the minimum classification of the current reading classification;
Counting the number duty ratio of each unit classification in a reference target, and marking the number duty ratio as a reference duty ratio; obtaining a corresponding student feature map, setting unit features of each unit classification according to the student feature map, wherein the unit features comprise a reference duty ratio, corresponding initial features, and corresponding quantity and quantity duty ratios of the initial features;
According to the formula Calculating a corresponding distribution value;
Wherein: a lloci is the assigned value of the corresponding unit class; i represents the cell classification within the corresponding initial feature; i=1, 2, … …, n being a positive integer; j represents the corresponding initial feature, because in practical application, the same unit classification may correspond to a plurality of initial features, j=1, 2, … …, m being a positive integer; t preij represents the number ratio of the corresponding initial features of the corresponding unit classification; c peri denotes the reference duty cycle of the corresponding cell class; d (x ij) is a coefficient model, x ij is reading data of corresponding unit classification in corresponding initial characteristics, and the reading data is determined according to detailed reading data corresponding to a reference target; the input data of the coefficient model is x ij; the output data is the association coefficient; the specific coefficient mode is set by combining the existing judgment model, and the expression is that ; The first standard to the tenth standard are all set by a manager according to the actual teaching requirements of the school, for example, the specific gravity of the reading data classified in the initial characteristics according to the unit is set for improving the learning score or the specific gravity of the reading data in the examination teaching, the first standard is checked one by one, and the next level check is not met; in order to further improve the intellectualization, a corresponding standard check model can be established based on a CNN network or a DNN network and other neural networks, a corresponding training set is set for training in a manual mode, the training set comprises input data and output data, the input data is corresponding reading data and various standards, and the output data is a result whether the input data accords with the standards or not; outputting corresponding association coefficients according to the combination result;
and allocating the resource input budget according to the allocation value corresponding to each unit classification.
Determining various reading resource information corresponding to each unit category, which refers to the related information of the uncollected, uncollected and unowned reading data; marking as data information to be selected, prioritizing the data information to be selected to obtain a first sequence, determining corresponding target data information according to the ordering of the data information to be selected in the first sequence and the corresponding cost, selecting according to the sequence, accumulating the corresponding cost, comparing with the allocated budget, and determining the corresponding target data information; and collecting reading data according to the information of each target data.
The priority ordering of the data information to be selected can be directly performed according to the existing resource information priority evaluation mode. And carrying out comprehensive evaluation on the corresponding priority value according to evaluation items such as cost, purchase amount, score and the like of the resource information.
In other embodiments, the data may be collected according to an existing manner, such as a teacher counting various reading data as the reading data, manually recommending, and so on.
By setting the resource module, automatic and intelligent reading data acquisition is realized, and a reading resource library is enriched; dynamically adjusting the acquired reading data according to the change condition of the students; the manual labor is greatly reduced, and valuable data is screened from a plurality of reading data; the method and the device provide high-value reading data for students.
The student analysis module is used for analyzing specific students and determining the reading data of the students, and the specific process is as follows:
Acquiring reading related data of students, namely various data related to reading recommendation of the students, such as age, reading record and the like, identifying each unit classification corresponding to the reading record in the reading related data as corresponding reading interest classification, counting the corresponding interest proportion of each reading interest classification, marking the interest coefficient of the reading interest classification with the interest proportion larger than a threshold value X1 as 1, and taking the interest coefficient of other reading interest classifications as the interest coefficient according to the corresponding interest proportion, namely ; Wherein: delta is an interest coefficient; xz is the interest duty cycle; the threshold value X1 is generally 10%, and can be adjusted according to actual requirements; establishing a corresponding reading interest statistical table according to the obtained reading interest classifications and the corresponding interest coefficients;
Identifying the subject words of each article in the reading record according to the reading related data, and carrying out arrangement statistics to obtain various subject words and the corresponding subject proportion of the subject words, wherein the number proportion of the subject words is the subject proportion;
According to the formula Calculating a weight value of a corresponding subject term; wherein: w c is a weight value; c represents a corresponding subject term, c=1, 2, … …, v being a positive integer; a represents a corresponding appearing subject term, if the subject term appears 2 times, each occurrence represents one question, namely a can be equal to 1 and 2; a=1, 2, … …, e is a positive integer; z perc represents the topic duty cycle of the corresponding topic word; delta ac is the corresponding interest coefficient;
marking the subject words with the weight values larger than the threshold value X2 as check words, and combining the check words into a check set;
identifying the subject words corresponding to each reading data in the reading resource library to form a subject word set; checking each reading data according to the checking set to form a corresponding checking vector ; Comparing the check words in the check set with the subject words of the read data, and marking the check words as 1 if the check words are included, otherwise marking the check words as 0; combining into a check vector; such as= (0,0,1,1,0, …, 1); Will|Reading data with the number of I being more than 0.9N are marked as to-be-selected reading data; wherein N is the number of check words;
Forming a corresponding interest vector WP according to the weight value corresponding to each subject term, namely WP= (W1, W2, W3, … …);
Counting the number proportion of each subject term in the reading data to be selected, marking the number proportion as a weight proportion, forming a corresponding vector DP to be selected according to the weight proportion of each subject term, adjusting the number of the corresponding vector DP to be selected to be the same as the number of elements in the interest vector, and replacing the empty space with 0;
According to the formula Calculating a corresponding recommended value; and recommending the corresponding reading data to be selected to students according to the sequence of the recommended value from high to low.
By arranging the student analysis module, reading data is intelligently recommended according to student data, and personalized reading resource recommendation of students is realized.
The reading acquisition module is used for acquiring the reading state data of the students in the reading process in real time and sending the acquired reading state data to the reading evaluation module.
The specific acquisition mode can adopt various sensors and data acquisition equipment to acquire, such as an eye movement instrument, a handheld device and the like, and acquire reading state data of students, such as reading time, reading speed, eyeball movement paths and the like, in real time.
The reading evaluation module evaluates the reading quality of students, acquires reading state data, identifies reading content, analyzes the reading content through a preset range judgment model, and acquires a corresponding range value, wherein the range judgment model is established according to the current identification technology and the judgment technology, input data is the reading content and the current reading content range input by a teacher, and the reading content range is input in advance by the teacher; the output data is a range value, wherein the range value comprises 1 or 0; the expression of the range judgment model is; Wherein: s is the reading content; FU is the read content range;
Evaluating the reading state data to obtain corresponding time length values, efficiency values and attitude coefficients; the time length value is set according to the reading time length, the longer the reading time length is, the larger the time length value is, the earlier speed increasing is fast, and the later speed increasing is slow; specifically, different teachers can preset a time length value matching curve suitable for teaching, and the reading time length is in a range, so that after a plurality of points are preset, the corresponding time length value matching curve can be fitted; if not, the teacher can comprehensively store the time length values to match the curves, and the curve corresponding to the intermediate value is taken as the standard curve for application; the efficiency value is set according to the reading speed; the attitude coefficient is set according to the eyeball movement path, namely whether students read normally or not is determined according to whether the eyeball movement path is matched with the reading content and the reading speed; specifically, a corresponding evaluation model can be established based on a CNN network or a DNN network, and a corresponding training set is established in a manual mode to train, wherein the training set comprises input data and output data, and the input data is reading state data set by various simulation; the output data are corresponding duration values, efficiency values and attitude coefficients; evaluating the reading state data through an evaluation model after successful training to obtain a corresponding duration value, efficiency value and attitude coefficient;
respectively marking the obtained duration value, efficiency value and attitude coefficient as ST, XL and eta;
Calculating a corresponding reading evaluation value according to the formula P rior=H(s)×[η×(b1×ST+b2 xXL);
Wherein: p rior is a reading evaluation value; b 1、b2 are all proportional coefficients, the value range is 0<b 1≤1,0<b2 which is less than or equal to 1, and H(s) is a range judgment model; η is an attitude coefficient; ST is a duration value; XL efficiency value;
And displaying the obtained reading evaluation value to corresponding teachers and students in real time.
A reading teaching quality assessment method based on data analysis comprises the following steps:
Collecting various reading data, and establishing a corresponding reading resource library according to the collected reading data;
performing student interest analysis to determine student reading data;
collecting reading state data of students in a reading process in real time;
reading state data are acquired, and evaluation is carried out on the reading state data, so that corresponding duration values, efficiency values and attitude coefficients are obtained; calculating a corresponding reading evaluation value;
And displaying the obtained reading evaluation value to corresponding teachers and students in real time.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas which are obtained by acquiring a large amount of data and performing software simulation to obtain the closest actual situation, and preset parameters and preset thresholds in the formulas are set by a person skilled in the art according to the actual situation or are obtained by simulating a large amount of data.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (8)
1. The reading teaching quality evaluation system based on data analysis is characterized by comprising a resource module, a student analysis module, a reading acquisition module and a reading evaluation module;
The resource module is used for collecting various reading data and establishing a corresponding reading resource library according to the collected reading data;
The student analysis module is used for carrying out student interest analysis and determining reading data of students;
the reading acquisition module is used for acquiring reading state data of students in the reading process in real time and sending the acquired reading state data to the reading evaluation module;
the reading evaluation module evaluates the reading quality of students to obtain reading state data, and evaluates the reading state data to obtain corresponding duration values, efficiency values and attitude coefficients;
Calculating a corresponding reading evaluation value according to the formula P rior=H(s)×[η×(b1×ST+b2 xXL);
Wherein: p rior is a reading evaluation value; b 1、b2 are all proportional coefficients, and the value range is 0<b 1≤1,0<b2 to be less than or equal to 1; η is an attitude coefficient; ST is a duration value; XL efficiency value; h(s) is a range judgment model;
the expression of the range judgment model is S is the reading content; FU is the read content range;
the obtained reading evaluation value is displayed to corresponding teachers and students in real time;
the working method of the student analysis module comprises the following steps:
Acquiring reading association data of students, and setting a corresponding reading interest statistical table according to the reading association data; the reading interest statistical table is used for counting each reading interest category and corresponding interest coefficient of the student;
identifying each subject term in the reading record according to the reading related data and according to a formula Calculating the weight value of each subject term;
Wherein: w c is a weight value; c represents a corresponding subject term, c=1, 2, … …, v being a positive integer; a=1, 2, … …, e is a positive integer; z perc represents the topic duty cycle of the corresponding topic word; delta ac is the corresponding interest coefficient;
determining to-be-selected reading data in a reading resource library; counting the weight ratio of each subject term in the reading data to be selected, and forming a corresponding vector DP to be selected according to the weight ratio of each subject term;
According to the formula Calculating a corresponding recommended value; wherein: QW is a recommended value; WP is an interest vector;
recommending the corresponding to-be-selected reading data to the students according to the sequence of the recommended values from high to low, and determining the reading data corresponding to the students;
The method for determining the to-be-selected reading data in the reading resource library comprises the following steps:
Marking the subject words with the weight values larger than the threshold value X2 as check words, and combining the check words into a check set; marking the number of check words as N;
identifying the subject words corresponding to each reading data in the reading resource library to form a subject word set; checking each check word in the check set and the subject word set one by one, and marking 1 when the subject word set comprises the corresponding check word, otherwise marking 0; forming corresponding check vector ；
Will beReading data of >0.9N is marked as the reading data to be selected.
2. The reading teaching quality assessment system based on data analysis according to claim 1, wherein the reading data acquisition method comprises:
Acquiring each piece of student information, dynamically generating a corresponding student feature map according to the student information, and setting a corresponding reading range based on the student feature map;
Acquiring resource input budget, identifying each reading type in a reading range, marking the reading type as unit classification, counting the quantity proportion of each unit classification in a reference target, and marking the unit classification as reference proportion; obtaining a corresponding student feature map, and setting unit features of each unit classification according to the student feature map;
distributing resource input budget according to the unit characteristics;
Determining each piece of data information to be selected corresponding to each unit classification, prioritizing each piece of data information to be selected to obtain a first sequence, and determining corresponding target data information according to the ordering of each piece of data information to be selected in the first sequence and the corresponding cost; and collecting reading data according to each piece of target data information.
3. The reading teaching quality assessment system based on data analysis according to claim 2, wherein the method for dynamically generating a student feature map according to student information comprises:
Updating student information in real time, presetting a corresponding initial template, and carrying out feature recognition extraction on the student information according to the initial template to obtain initial features; and counting the number of student information corresponding to each initial feature and the number proportion corresponding to the corresponding number, and generating a corresponding student feature map according to each obtained initial feature and the corresponding number and number proportion.
4. The reading teaching quality evaluation system based on data analysis according to claim 2, wherein the method for determining the reading range comprises:
the manager presets the corresponding reference targets, acquires the reading records corresponding to the reference targets, identifies the reading types corresponding to the reading records, integrates the reading types corresponding to the reference targets, and forms the corresponding reading range.
5. The reading teaching quality assessment system based on data analysis according to claim 2, wherein the method for allocating resource investment budget according to unit characteristics comprises:
According to the formula Calculating a corresponding distribution value;
Wherein: a lloci is the assigned value of the corresponding unit class; i represents the cell classification within the corresponding initial feature; i=1, 2, … …, n being a positive integer; j represents the corresponding initial feature, j=1, 2, … …, m being a positive integer; t preij represents the number ratio of the corresponding initial features of the corresponding unit classification; c peri denotes the reference duty cycle of the corresponding cell class; d (x ij) is the coefficient model, x ij is the read data for the respective cell class in the corresponding initial feature.
6. The reading teaching quality assessment system based on data analysis according to claim 5, wherein the expression of the coefficient model is:
; x ij is the read data for the respective cell class in the corresponding initial feature.
7. The reading teaching quality evaluation system based on data analysis according to claim 1, wherein the method for establishing the reading interest statistical table comprises the following steps:
determining each reading interest category of the student according to the reading association data, and counting the corresponding interest ratio of each reading interest category;
According to the formula Calculating an interest coefficient corresponding to the reading interest classification;
Wherein: delta is an interest coefficient; xz is the interest duty cycle; x1 is a threshold;
And establishing a corresponding reading interest statistical table according to the obtained reading interest classifications and the corresponding interest coefficients.
8. A reading teaching quality assessment method based on data analysis, characterized in that it is applied to a reading teaching quality assessment system based on data analysis as claimed in any one of claims 1 to 7, and the method comprises:
Collecting various reading data, and establishing a reading resource library according to the collected reading data;
analyzing the reading interests of students and determining the reading data of the students;
collecting reading state data of students in a reading process in real time;
acquiring reading state data, and evaluating the reading state data to acquire a duration value, an efficiency value and an attitude coefficient; calculating a corresponding reading evaluation value according to the duration value, the efficiency value and the attitude coefficient;
and displaying the reading evaluation value to corresponding teachers and students in real time.
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