CN108197797A - A kind of students ' reading assay method and device based on big data - Google Patents
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Abstract
The invention discloses a kind of students ' reading assay methods based on big data, include the following steps：Receiving step：Receive student information and the reading parameters corresponding with student information that intelligent terminal is sent；It reads and reminds step：The reading index of the student is calculated according to the reading parameters, and is compared with model database so as to which the reading completed to the student is reminded.The present invention also provides a kind of electronic equipment and computer readable storage medium.The students ' reading assay method based on big data of the present invention receives student information and reading parameters by basis, so as to complete the indexing evaluated students ' reading and quantization, and then the weakness of students ' reading is reminded, and corresponding read is recommended to suggest for the student.
Description
Technical field
The present invention relates to a kind of IT application in education sector technical field more particularly to a kind of students ' reading analyses based on big data
Evaluation method and device.
Background technology
At present, reading instruction is a highly important part in language teaching.It is combined as a kind of artistry and instrumental
Subject, within the class period it is outer read it is indispensable, strengthen within the class period it is outer read it is imperative, but current students in middle and primary schools within the class period, outside reading
Present situation it is troubling.It is outer within the class period to read since the factors such as exam-oriented education, teacher, parent and student itself idea exist
It is in very passive condition.It by the analysis to any of the above factor, targetedly breaks through one by one, to propose one conscientiously
Feasible countermeasure improves current pupil outer reading culture within the class period.
The importance of reading is almost self-evident, and modern society is an information-intensive society, lives in information-intensive society, just
Constantly to obtain information；Reading is a critically important approach.Although the various informative and development equal ten of current social medium
Divide rapidly, but someone counts, the various information that modern society needs are there are about more than 85% directly or indirectly from book document.
It can be seen that read the importance in modern society.For students in middle and primary schools from the even more most basic approach of acquisition information of reading and most
Short-cut method.Middle and elementary school student has certain character learning amount, and student has certain connoisseurship, for extensive reading outer within the class period
Provide possibility.In order to find out different zones school, class, the difference read inside and outside students in class, in order to preferably strengthen reading
Strategy study, improve students ' reading quality become those skilled in the art it is urgently to be resolved hurrily the technical issues of.
Invention content
For overcome the deficiencies in the prior art, one of the objects of the present invention is to provide a kind of students based on big data to read
Assay method is read, can solve the problems, such as to carry out indexing prompting to students ' reading.
The second object of the present invention is electronic equipment, can solve the problems, such as to carry out indexing prompting to students ' reading.
The third object of the present invention is computer readable storage medium, can solve to carry students ' reading progress indexing
The problem of waking up.
An object of the present invention adopts the following technical scheme that realization：
A kind of students ' reading assay method based on big data, includes the following steps：
Receiving step：Receive student information and the reading parameters corresponding with student information that intelligent terminal is sent；
It reads and reminds step：Be calculated the reading index of the student according to the reading parameters, and with model database into
Row is compared so as to which the reading completed to the student is reminded.
Further, the reading parameters are reading ability parameter, and the reading ability parameter passes through reading test and appraisal article
To obtain, the reading ability parameter includes reading rate and reading is understood by rate；
Described read reminds step to include following sub-step：
Reading ability index is calculated according to reading ability parameter；
Reading ability index and reading ability section model are compared with obtain student reading ability section value,
Relationship of the reading ability section model between each section and reading ability parameter；
Reading ability section value with books recommended models is compared and is suggested with providing corresponding read.
Further, the section value is divided into 1-6 rank, and each rank is divided into 1-5 sections.
Further, the reading parameters are reading interest parameter, and the reading interest index includes books number of types；
Described read reminds step to include following sub-step：
Reading interest index is calculated according to reading interest parameter；
Reading interest index with interest empirical model is compared, and corresponding exception of reading is carried out to the student and is carried
It wakes up.
Further, the reading parameters are reading habit parameter, and the reading habit parameter includes completing reading task
Number of days and deliver book review number；
It is described read remind step be specially：
It is calculated to obtain reading habit index according to reading habit parameter；
Reading habit index with custom empirical model is compared, and corresponding read is carried out to the student and is reminded.
Further, the reading parameters include reading ability parameter, reading interest parameter and reading habit parameter；
Described read reminds step to include following sub-step：
Reading index is calculated according to reading parameters, the reading index=reading ability index * N1+ reading interests refer to
Mark * N2+ reading habit index * N3, and N1+N2+N3=1；
Judge whether the reading index of the student is more than students ' reading index average value, if it is not, then being carried out to the student
Corresponding read is reminded.
Further, wherein N1=50%, N2=20%, N3=30%.
Further, grade, student where school, student where city, student where the student information includes student
Place class and student；The reading parameters include reading ability parameter, reading interest parameter and reading habit parameter；
Statistic procedure is further included after receiving step：Statistics obtains different cities, different school, not of the same grade and different
The reading parameters of the student of class.
The second object of the present invention adopts the following technical scheme that realization：
A kind of electronic equipment can be run on a memory and on a processor including memory, processor and storage
Computer program, the processor realize being counted based on big described in arbitrary a line in one of the object of the invention when performing described program
According to students ' reading assay method.
The third object of the present invention adopts the following technical scheme that realization：
A kind of computer readable storage medium, is stored thereon with computer program, and the computer program is held by processor
The as above method described in any one is realized during row.
Compared with prior art, the beneficial effects of the present invention are：
The students ' reading assay method based on big data of the present invention receives student information with reading by basis
Parameter so as to complete the indexing evaluated students ' reading and quantization, and then reminds the weakness of students ' reading, and to be somebody's turn to do
Student recommends corresponding read to suggest.
Description of the drawings
Fig. 1 is the flow chart of the students ' reading assay method based on big data of the present invention；
Fig. 2 is the flow chart of the students ' reading assay method based on big data of embodiment one；
Fig. 3 is the flow chart of the students ' reading assay method based on big data of embodiment two；
Fig. 4 is the flow chart of the students ' reading assay method based on big data of embodiment three；
Fig. 5 is the flow chart of the students ' reading assay method based on big data of example IV；.
Fig. 6 is the flow chart of the students ' reading assay method based on big data of embodiment five；
Fig. 7, Fig. 8 and Fig. 9 are that the statistics of embodiment five realizes design sketch.
Specific embodiment
In the following, with reference to attached drawing and specific embodiment, the present invention is described further, it should be noted that not
Under the premise of conflicting, new implementation can be formed between various embodiments described below or between each technical characteristic in any combination
Example.
As shown in Figure 1, the present invention provides a kind of students ' reading assay method based on big data, including following step
Suddenly：
S1：Receive student information and the reading parameters corresponding with student information that intelligent terminal is sent；This step is main
It is students ' reading in order to get and does some the basic information inscribed, main purpose is for data in mobile phone, is passed through
The data that these are collected into are carried out integrating to be prompted accordingly for following processing step；
S2：The reading index of the student is calculated according to the reading parameters, and be compared with model database so as to
The reading to the student is completed to remind.By early period establish model database come be compared with obtained data parameters so as to
Determine whether to remind student, this step judgement in can there are many parametric form judgement.In the following embodiments
The Different Results that different reading parameters obtain are illustrated in detail respectively.
Embodiment one：
As shown in Fig. 2, a kind of students ' reading assay method based on big data provided in this embodiment specifically includes
Following sub-step：
S101：Receive student information and the reading parameters corresponding with student information that intelligent terminal is sent；The reading
Parameter is reading ability parameter, and the reading ability parameter tests and assesses article to obtain by reading, the reading ability parameter packet
It includes reading rate and reading is understood by rate；Students ' reading Analysis server acquisition student on the terminal device read by read books
Reading rate is understood by two parameter values of rate with reading；
S102：Reading ability index is calculated according to reading ability parameter；Reading rate=reading test and appraisal article it is total
The time of number of words/reading test and appraisal article；
Read be understood by rate=reading evaluation and test article correct score value (such as 10 topic in total, answers questions 6 topics, often inscribes 10 points,
Percent of pass is read as 60)；The setting of these topics needs to be configured according to different abilities, so as to more effective
Know, the short slab of student；Reading efficiency=reading is understood by rate * reading rates；System-computed reading ability index=reading
Speed * 40%+ readings are understood by rate * 60%.Its specific implementation is that student needs to complete corresponding test, only does
Corresponding examination question can just access corresponding parameter, can not be obtained if this set topic is not done then by test
Corresponding ability value；It is usually that corresponding topic setting can be carried out according to the grade of student when topic setting is carried out,
If its ability value is relatively high, it can bypass the immediate leadership and topic is set for it；
S103：Reading ability index and reading ability section model are compared with obtain student reading ability section
Place value, relationship of the reading ability section model between each section and reading ability parameter；The section value is divided into 1-6
A rank, and each rank is divided into 1-5 sections.The distribution of this section value is also to be in different grades from student according to student's difference
There is correlation, for example, each student is in different grades and there can be different human-subject tests, it is generally the case that in one grade
Human-subject test will be in contrast more lower than being in sophomoric human-subject test, 1-6 rank here can refer to i.e.
Be the 1-6 grades of primary school or be assigned to other grades according to different standard scores can also；Here 1-5 sections refer to
Which stage human-subject test is in same grade, and some students may be easier to understand the content that this grade is taught, have
Student can be understood that for so good, can more efficiently carry out phase by obtaining these test and appraisal parameters
Answer the prompting of content.
S104：Reading ability section value with books recommended models is compared and is suggested with providing corresponding read.According to
Obtained section value, system provide corresponding read according to big data empirical model and suggest, respectively from listening, think, appreciate, write four
Aspect describes.Reading ability dimensional analysis data acquisition system, primarily directed to student, in four reading ability dimensions, (essential information obtains
With inference, it is analysis integrated, using with innovation, appreciate and evaluation) under, the radar map of different characteristic is presented, system is according to data
Gather matched recommendation listen, think, appreciating, writing four aspect recommendations.Can be student according to the ability section value of student
It provides more targeted training to read, be carried out in terms of different, and be not merely that on one side, can effectively be promoted
The synthesis reading level of student.Here books recommended models are to be pre-stored within the data of server end, such as in not
Same section there may be the understanding short slab of which aspect, it is promoted so as to need the books of which aspect, is all had
It, can also be by a kind of mode by having that some education experts tested or in order to avoid artificial factor is interfered
It is that books recommended suggestion can be carried out to it by way of big data acquisition.By long-time trace ability have significantly into
Then the student of step understands the reading that these students carried out which aspect during ability progress, the reading pair of this respect
It listens in student, think, appreciating, writing which aspect is more beneficial, then storing the reading data of this respect.And it is directed to student
It is not of the same grade to carry out storage recommendation, when with the recommendation of time, collected data are more and more, then these data will
It is more and more accurate, so as to be effectively that student carries out books recommendation.
In addition to above-mentioned hierarchical approaches, GRL, DRA, Lexile and GEL etc. can also be used to be classified appraisal standards.These
Mode is the hierarchy plan standard of existing comparative maturity, according to different demands, can carry out different settings.
Embodiment two：
As shown in figure 3, a kind of students ' reading assay method based on big data provided in this embodiment specifically includes
Following sub-step：
S201：Receive student information and the reading parameters corresponding with student information that intelligent terminal is sent；The reading
Parameter is reading interest parameter, and the reading interest index includes books number of types；The acquisition of students ' reading Analysis server is learned
The number of types that the raw read books chosen on the terminal device are covered；Type is divided into science popularization, literature, humane comprehensive, logical knowledge, skill
Art intention；These classification are what is gone point according to major class, primarily to student can have at the several aspects of intelligence formation stages
More balanced development and the reference set；
S202：Reading interest index is calculated according to reading interest parameter；System-computed reading interest index=(read
The number of types of number of types/recommendation that books are covered) * 100；
S203：Reading interest index with interest empirical model is compared, and the student is read accordingly different
Often remind.It is not merely for primary acquisition evaluation to this interest index, because may be within some month, student compares
Favor and read in science popularization, but this representative conference does not influence ability in terms of its art creation, be merely able to illustrate student at this
Stage can be there are the tendentious problem of some cognitions, so general can be in 1 year to go to be commented in the range of unit
Valency, because 1 year is that a comparison has the interim time graded for the growth of student, if in 1 year, this five kinds
There are a type of books not carry out related reading by student in type, then it can there are certain to completeness that intelligence is formed
Influence；When such case occurs, then student can be prompted accordingly, the book that it is prompted to need to read which aspect
Nationality；Due between books may there are corresponding relevance, such as《Van gogh passes》Belong to the scope of literature in this major class, but
It is that it also can there are certain tendentiousness so that it generates art education interest, for example its bigger may《Van gogh's paintings are appreciated
Analysis》Interest is generated, so when books storage is carried out, crucial word association is carried out to the books of this relevance；Pass through this
The books of kind guidance quality are recommended so that student more efficiently can read.
Embodiment three：
As shown in figure 4, a kind of students ' reading assay method based on big data provided in this embodiment specifically includes
Following sub-step：
S301：Receive student information and the reading parameters corresponding with student information that intelligent terminal is sent；The reading
Parameter is reading habit parameter, and the reading habit parameter includes completing reading task number of days and delivers book review number；Student reads
The number of days that Analysis server acquisition student completes reading task on the terminal device is read, delivers two parameter values of number of book review；
S302：It is calculated to obtain reading habit index according to reading habit parameter；Complete reading task number of days=current system
Reading task number of days (all total number of days of student/total numbers of students) is averagely completed in meter city, delivers book review number=current statistic city
The book review number that city averagely delivers (all students always deliver number/total number of students)；
S303：Reading habit index with custom empirical model is compared, and corresponding read is carried out to the student and is carried
It wakes up.By the reading habit index that the student is calculated compared with the empirical model setting value in database, reading habit is referred to
The abnormal student of mark sends out prompting.Particularly reading task it is relatively low reach with the less situation of book review number actively urge reading
Purpose.
Example IV：
As shown in figure 5, a kind of students ' reading assay method based on big data provided in this embodiment specifically includes
Following sub-step：
S401：Receive student information and the reading parameters corresponding with student information that intelligent terminal is sent；The reading
Parameter includes reading ability parameter, reading interest parameter and reading habit parameter；
S402：Reading index is calculated according to reading parameters, the reading index=reading ability index * N1+ are read
Interest index * N2+ reading habit index * N3, and N1+N2+N3=1；Wherein most preferably, wherein N1=50%, N2=
20%, N3=30%.
S403：Judge whether the reading index of the student is more than students ' reading index average value, if it is not, then to the student
Corresponding read is carried out to remind.The present embodiment is in order to which the integration capability index to student understands there are one more detailed, is passed through
Each index of student is calculated so as to obtain one reading composite index, it is this reading composite index can allow student for
There are one recognize rather than be only confined to a dimension for oneself whole reading.
Embodiment five：
As shown in fig. 6, a kind of students ' reading assay method based on big data is present embodiments provided, including following
Step：
S501：Receive student information and the reading parameters corresponding with student information that intelligent terminal is sent；The student
Class and student where grade, student where school, student where city, student where information includes student；It is described to read ginseng
Number includes reading ability parameter, reading interest parameter and reading habit parameter；This step is primarily to the student got reads
Read and do some the basic information inscribed, main purpose be for data in mobile phone, by by the data that these are collected into
Row is integrated to be prompted accordingly for following processing step；
S502：The reading index of the student is calculated according to the reading parameters, and be compared with model database from
And the reading completed to the student is reminded；
S503：Statistics obtain different cities, different school, class not of the same grade and different student reading parameters.It is logical
It crosses and the reading conditions of student of different cities, different school, class not of the same grade and different is carried out for different data
Statistics, can in one class of more efficiently evaluation and test, a school and a city different regions whole reading conditions；
As shown in Figure 7, Figure 8 and Figure 9, the data statistics of a completion can be provided, and it is shown.
Whole reading conditions：Read books (sheet)：Books this number that all students in current statistic city read in total；Per capita
This number：Total this number of reading/total number of students；Read number of words (K word)：All students in current statistic city always read number of words, unit
For K word；Per capita：Total number of words/total number of students (retaining a decimal)；Read duration (hour)：All in current statistic city
Raw total reading duration, unit is hour；Per capita：It is total to read duration/total number of students；
Participate in reading activities：It participates in studying carefully movable (secondary) with class：The same class that all students in current statistic city participate in is studied carefully
Number summation；Per capita：Total degree/total number of students is studied carefully with class；Reaction to an article is delivered to think (piece)：All students in current statistic city deliver
Reaction to an article record summation；Per capita：It delivers reaction to an article and thinks total record/total number of students；
Reading habit index：Complete the number of number of days+the deliver book review of reading task；Complete reading task number of days：Currently
Averagely complete reading task number of days in statistics city；Deliver book review number：The book review number that current statistic city is averagely delivered.Pass through
Count above-mentioned data can compare different cities, different school, class not of the same grade and different student reading conditions, and
And it can be combined with regional education landscape, analyze what kind of influence different reading conditions can have achievement, be student's
Science, which is read, provides certain data support.
The above embodiment is only the preferred embodiment of the present invention, it is impossible to the scope of protection of the invention is limited with this,
The variation and replacement for any unsubstantiality that those skilled in the art is done on the basis of the present invention belong to institute of the present invention
Claimed range.
Claims (10)
- A kind of 1. students ' reading assay method based on big data, which is characterized in that include the following steps：Receiving step：Receive student information and the reading parameters corresponding with student information that intelligent terminal is sent；It reads and reminds step：The reading index of the student is calculated according to the reading parameters, and is compared with model database It pair is reminded so as to complete reading to the student.
- 2. the students ' reading assay method based on big data as described in claim 1, which is characterized in that described to read ginseng Number is reading ability parameter, and the reading ability parameter tests and assesses article to obtain by reading, and the reading ability parameter includes Reading rate and reading are understood by rate；Described read reminds step to include following sub-step：Reading ability index is calculated according to reading ability parameter；Reading ability index and reading ability section model are compared with obtain student reading ability section value, it is described Relationship of the reading ability section model between each section and reading ability parameter；Reading ability section value with books recommended models is compared and is suggested with providing corresponding read.
- 3. the students ' reading assay method based on big data as claimed in claim 2, which is characterized in that the section value It is divided into 1-6 rank, and each rank is divided into 1-5 sections.
- 4. the students ' reading assay method based on big data as described in claim 1, which is characterized in that described to read ginseng Number is reading interest parameter, and the reading interest index includes books number of types；Described read reminds step to include following sub-step：Reading interest index is calculated according to reading interest parameter；Reading interest index with interest empirical model is compared, and corresponding reading abnormity prompt is carried out to the student.
- 5. the students ' reading assay method based on big data as described in claim 1, which is characterized in that described to read ginseng Number is reading habit parameter, and the reading habit parameter includes completing reading task number of days and delivers book review number；It is described read remind step be specially：It is calculated to obtain reading habit index according to reading habit parameter；Reading habit index with custom empirical model is compared, and corresponding read is carried out to the student and is reminded.
- 6. the students ' reading assay method based on big data as described in claim 1, which is characterized in that described to read ginseng Number includes reading ability parameter, reading interest parameter and reading habit parameter；Described read reminds step to include following sub-step：Reading index, the reading index=reading ability index * N1+ reading interest indexs * is calculated according to reading parameters N2+ reading habit index * N3, and N1+N2+N3=1；Judge whether the reading index of the student is more than students ' reading index average value, if it is not, then being carried out to the student corresponding Reading remind.
- 7. the students ' reading assay method based on big data as claimed in claim 6, which is characterized in that wherein N1= 50%, N2=20%, N3=30%.
- 8. the students ' reading assay method based on big data as described in claim 1, which is characterized in that student's letter Class and student where grade, student where school, student where city, student where breath includes student；The reading parameters Including reading ability parameter, reading interest parameter and reading habit parameter；Statistic procedure is further included after receiving step：Statistics obtains different cities, different schools, class not of the same grade and different Student reading parameters.
- 9. a kind of electronic equipment including memory, processor and stores the meter that can be run on a memory and on a processor Calculation machine program, which is characterized in that the processor realizes the base in claim 1-8 described in any one when performing described program In the students ' reading assay method of big data.
- 10. a kind of computer readable storage medium, is stored thereon with computer program, it is characterised in that：The computer program The method as described in claim 1-8 any one is realized when being executed by processor.
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| CN108961878A (en) * | 2018-08-31 | 2018-12-07 | 广州双快数码科技有限公司 | A kind of innovative learning platform Internet-based |
| CN110033664A (en) * | 2019-04-22 | 2019-07-19 | 重庆多创电子技术有限公司 | A kind of wisdom reads points-scoring system and method |
| CN110175326A (en) * | 2019-05-07 | 2019-08-27 | 首都师范大学 | It is a kind of to obtain the calculation method and device for reading imago information content |
| CN110716917A (en) * | 2019-09-12 | 2020-01-21 | 北京智联起点信息技术有限公司 | Reading evaluation method and system based on integral system |
| CN110990702A (en) * | 2019-12-04 | 2020-04-10 | 北京智乐活科技有限公司 | Recommendation method, client and server for autonomous reading of children |
| CN111105111A (en) * | 2018-10-26 | 2020-05-05 | 北大方正集团有限公司 | Reading management method, device, system and storage medium |
| CN112000890A (en) * | 2020-08-31 | 2020-11-27 | 安徽爱依特科技有限公司 | Sketch recommendation method and device, computer equipment and readable storage medium |
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| CN112396901A (en) * | 2020-11-26 | 2021-02-23 | 上海松鼠课堂人工智能科技有限公司 | English reading understanding level evaluation recommendation method and system |
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102005140A (en) * | 2010-12-02 | 2011-04-06 | 无敌科技(西安)有限公司 | Graded reading electronic device and graded reading method thereof |
| CN105844996A (en) * | 2016-04-06 | 2016-08-10 | 福建海博文化产业发展有限公司 | Reading evaluation method based on big data and system |
-
2017
- 2017-12-28 CN CN201711466432.1A patent/CN108197797A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102005140A (en) * | 2010-12-02 | 2011-04-06 | 无敌科技(西安)有限公司 | Graded reading electronic device and graded reading method thereof |
| CN105844996A (en) * | 2016-04-06 | 2016-08-10 | 福建海博文化产业发展有限公司 | Reading evaluation method based on big data and system |
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| CN111105111A (en) * | 2018-10-26 | 2020-05-05 | 北大方正集团有限公司 | Reading management method, device, system and storage medium |
| CN111105111B (en) * | 2018-10-26 | 2023-09-05 | 新方正控股发展有限责任公司 | Reading management method, device, system and storage medium |
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| CN110990702A (en) * | 2019-12-04 | 2020-04-10 | 北京智乐活科技有限公司 | Recommendation method, client and server for autonomous reading of children |
| CN110990702B (en) * | 2019-12-04 | 2023-04-25 | 张家口智趣学科技有限公司 | Recommendation method, client and server for autonomous reading of children |
| CN112102121A (en) * | 2020-08-12 | 2020-12-18 | 厦门印天电子科技有限公司 | Reading capability evaluation method and system and borrowing system |
| CN112015988A (en) * | 2020-08-31 | 2020-12-01 | 安徽爱依特科技有限公司 | Book borrowing and communication learning method, book borrowing and communication learning platform, computer equipment and readable storage medium |
| CN112000890A (en) * | 2020-08-31 | 2020-11-27 | 安徽爱依特科技有限公司 | Sketch recommendation method and device, computer equipment and readable storage medium |
| CN112396901A (en) * | 2020-11-26 | 2021-02-23 | 上海松鼠课堂人工智能科技有限公司 | English reading understanding level evaluation recommendation method and system |
| CN116523712A (en) * | 2023-07-04 | 2023-08-01 | 浙江海亮科技有限公司 | Card punching reminding method, card punching system, server, medium and program product |
| CN116523712B (en) * | 2023-07-04 | 2023-11-24 | 浙江海亮科技有限公司 | Card punching reminding method, card punching system, server, medium and program product |
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