CN111861370B

CN111861370B - Word listening optimal review time planning method and device

Info

Publication number: CN111861370B
Application number: CN202010566569.XA
Authority: CN
Inventors: 周海滨
Original assignee: Beijing Guoyin Redwood Education Technology Co ltd
Current assignee: Beijing Guoyin Redwood Education Technology Co ltd
Priority date: 2020-06-19
Filing date: 2020-06-19
Publication date: 2024-02-06
Anticipated expiration: 2040-06-19
Also published as: CN111861370A

Abstract

The invention relates to the technical field of intelligent memory methods, in particular to a method and a device for planning word listening optimal review time, comprising the following steps: outputting the voice information of the learning word; receiving learning information of the learning word, wherein the learning information comprises a memory strength value, marking information and time information of a user on the learning word; calculating review interval duration according to the learning information; and receiving the review interval duration, and obtaining an optimal review time point according to the learning information and the review interval duration. Marking the learned words according to learning information, generating different current memory strength values according to different marks of different learned words, wherein the higher the memory strength value is, the higher the mastering degree of the learned words is, and otherwise, the lower the mastering degree of the learned words is; and calculating the optimal review time point, and comprehensively examining the mastering condition of the learner on the learning word, so as to provide the optimal and reasonable review time and improve the learning efficiency.

Description

Word listening optimal review time planning method and device

Technical Field

The invention relates to the technical field of intelligent memory methods, in particular to a method and a device for planning word listening optimal review time.

Background

In recent decades, learning foreign language has become a trend, and teams learning foreign language are becoming larger. In the eighties of the last century, china begins to pay attention to foreign language education and improves the position of the foreign language step by step under the trend of reform opening. Even if our country makes such efforts, a great part of students still have no way to master the excellent foreign language ability, learn the foreign language for over ten years, use the foreign language, just change into a Zhang Chengji test paper, and highlight the one-time brilliance. This is clearly a problem with our teaching and learning methods.

We have to go through a process of "listen, say, read, write" that is, in fact, a simulated process. From the time of dentistry, the two processes of hearing and speaking are reflected, a user can start to answer various language information transmitted by a parent to the user, then the brain can analyze the language information, and then imitate the language information to make feedback. The hearing training is the first part of the whole process and is the most important part, so that the problem of the best review time review can not be pointed aiming at different grasping degrees of each word at present, and the words can be more effectively memorized.

In view of this, the present invention has been proposed.

Disclosure of Invention

The present invention provides a method and system for word listening optimal review time planning, which at least solves one of the above problems.

The invention provides a method for planning word listening optimal review time, which comprises the following steps:

outputting the voice information of the learning word;

receiving learning information of the learning word, wherein the learning information comprises a memory strength value, marking information and time information of a user on the learning word;

calculating review interval duration according to the learning information;

and receiving the review interval duration, and obtaining an optimal review time point according to the learning information and the review interval duration.

By adopting the scheme, the learned words are marked according to the learning information, different current memory strength values are generated according to different marks of different learned words, the memory strength values reflect the mastering degree of the learner on the words, and the higher the memory strength values are, the higher the mastering degree of the learned words is, and otherwise, the lower the mastering degree of the learned words is; and calculating the optimal review time point by calculating the review interval time length, and comprehensively examining the mastering condition of the learner on the learning word so as to provide the optimal and reasonable review time.

Further, the outputting the speech information of the learning word further includes:

judging whether external playing is used when outputting the voice information of the learning word;

if yes, the learning of the word is counted as effective learning;

if not, receiving external audio information when outputting the voice information of the learning word;

setting a sound intensity threshold parameter;

analyzing the external audio information, and judging whether the external sound intensity of the external audio information is larger than the sound intensity threshold parameter;

if yes, counting the learning of the learning word as invalid learning;

if not, the learning of the learning word is counted as effective learning.

With the above scheme, when the external environment is too noisy and is difficult for the user to hear, the external environment has a great influence on learning objectively and should not be recorded as an effective sample, but if the learner uses an external device such as a headset, the learner is not influenced, the learning is not influenced, and the learning should be counted as effective learning.

Further, the learning information comprises review information and primary learning information, and the marking information comprises primary learning new word answering errors, review new word answering pairs, review new word answering errors and review new word answering overtime.

By adopting the scheme, the learning words encountered by the user during use can be new words which are learned for the first time or new words which are learned for the second time, so even if the same words reflect different mastering degrees of the user under different conditions, the optimal review time point can be calculated more reasonably by distinguishing the different conditions.

Further, the receiving learning information for the learning word includes receiving the time information, including the steps of:

setting a first reaction time length and a second reaction time length, wherein the first reaction time length is longer than the second reaction time length;

receiving answer information, wherein the answer information comprises answer information and answer time length, judging whether the answer information is correct, and comparing the first reaction time length, the second reaction time length and the answer time length;

when the learning word is a new word, the response information is correct, the response time is smaller than or equal to the second response time, the learning word is marked as a mature word, and the memory strength value is a first initial memory strength value;

when the learning word is a new word, the response information is correct, the response time length is longer than the second response time length and shorter than or equal to the first response time length, the learning word is marked as a new word, the memory strength value is a third initial memory strength value, the third initial memory strength value is calculated according to the formula I= (Dz- (D3 '-Db))x2, dz is an extremum, I is the third initial memory strength value, D3' is the response time length, da is the first response time length, and Db is the second response time length;

When the learning word is a new word, the learning word is marked as a new word when the answer information is wrong, and the memory strength value of the learning word is a second initial memory strength value.

By adopting the scheme, the tension of the learner during learning is improved by setting the first reaction time length and the second reaction time length according to the comparison between the actual reaction time length of the user response and the first reaction time length and the second reaction time length, and the influence of the distraction of the learner on the learning efficiency is avoided; the mastering degree of the learning word by the user is reflected more carefully and accurately according to the answering time of the user.

Further, the first reaction duration and the second reaction duration and the formula can be determined according to actual conditions and human forgetting rules.

Preferably, the deriving the optimal review time point includes: calculating a first optimal review time point after primary learning or secondary review; when the learning word marking information is a new word and the user answers by mistake, the first best review time point is according to the formula:

tbr1=trc1+d1, where tbr1 is the first best review time point, trc1 is the beginner time point, and D1 is the first review interval duration;

the calculation of the first review interval duration is according to the formula:

D1＝C1×e ^p P= (c2×sn/μ) +c3, where D1 is the first review interval duration, C1 is a power value coefficient, e is a natural constant, P is a power value, C2 is an intensity coefficient, sn is the first current memory intensity, C3 is a power value constant, μ is a calculation constant;

when the learning word mark information is a new word and the user answers, tbr1=trc2+d1, trc2 is the current review time point; when the learning word mark information is a new word and the user answers by mistake or times out, tbr1=tbr1 '+d1, wherein tbr1' is the first best review time point after the last learning is completed.

By adopting the scheme, the first optimal review time point is the optimal time point of the next review after the user learns the word, the first review interval time is the time length of the current time of the study from the first optimal review time point, and different first optimal review time points are generated through different learning conditions of the user on different words; after a user learns a new word for the first time and marks the new word as a new word, learning the new word for the next time as a review, wherein the first optimal review time point is the time point of first learning the new word and is overlapped with the first review interval time; when the learning word is marked as a word generation instruction, the learning is not the primary learning, and the review stage is entered; when the learning word marking information is a new word and the user answers or overturns to answer, the method indicates that the user has very low grasp degree of the new word, and the answer time of the user is possibly later than the first best review time point after the last learning is finished or the best review time point after the last learning is finished, so that the first best review time point after the last learning is finished is overlapped with the first review interval duration. Different computing methods are adopted under different conditions, so that the first optimal review time point can be more reasonably computed, and the learning efficiency is improved.

Preferably, the calculation of the optimal review time point further includes the steps of:

judging the number of continuous answer pairs of the same word;

if the number is equal to three, judging whether the first optimal review time point and the continuous three reviews are in the same review period;

if not, not adjusting;

if yes, the optimal review time point is set in the next review period.

By adopting the scheme, the memory method is more scientific, and the learning efficiency is improved.

Further, the determining of the first current memory strength Sn includes: when the learning word is a new word for initial learning, the first current memory strength Sn is recorded as a second initial memory strength value or a third initial memory strength value according to the above description; sn=sn' + Sni when the learning word is the user answer to the new word in the review process; when the user answers the new word by mistake or overtime, sn=sn '-Sni, where Sn' is a memory strength base value and Sni is a memory strength change value.

By adopting the scheme, the memory strength basic value Sn' is the final memory strength value after the last learning.

Preferably, the memory strength variation value Sni further includes a reaction time period influence value, and the reaction time period influence value is calculated according to the formula:

Rd= (1-Mrd/20) x Srd, wherein Mrd is response time length, srd is reaction time length influence memory strength basic value, and Rd is reaction time length influence value.

By adopting the scheme, the response time length influence memory strength basic value Srd can be determined according to the overall assignment situation and the human forgetting rule, the response time length influence memory strength basic value Srd represents the influence of the response time length on the memory strength value at most, and Mrd is the response time length unit of seconds; the influence value of the reaction time is calculated, so that the grasping degree of the user on the word can be accurately and finely calculated according to the speed of the user to answer.

Further, the memory strength variation value Sni further includes a difficulty impact value calculated according to the formula:

Df＝Dti×Mdt，Dti＝(Dm+Am)，Dm＝Rwr×λ，Rwr＝Crw/Crt；

df is a difficulty influence value, dti is a difficulty index, mdt is a memory strength basic value influenced by the difficulty index, dm is learning data calculation difficulty, am is artificial annotation difficulty, rwr is error rate of answering the new word in the user review process, λ is a difficulty mark, crw is sum of times of answering the new word in the user review process and primary learning, and Crt is total times of answering the new word in the user review process.

By adopting the scheme, the difficulty influence value can comprise manual marking difficulty and learning data calculation difficulty, wherein the learning data calculation difficulty is that the error rate of word answering is calculated by a user; the difficulty mark lambda is used for calculating the learning data calculation difficulty, the difficulty mark lambda can be displayed on a response interface in the form of an energy grid, and a memory strength basic value Mdt of the influence of a difficulty index is determined according to the overall assignment condition and the human forgetting rule and is expressed as the influence of word difficulty on the memory strength value.

Further, the memory strength variation value Sni further includes a diligence impact value calculated according to the formula:

Dli＝Dgi×Mdg，Dgi＝(Trc2-Tbr1)/24×60×60；

where Dli is the diligence impact value, dgi is the diligence impact index, mdg is the diligence index impact memory strength base value, tbr1 is the first best review time point, and Trc2 is the current review time point.

By adopting the scheme, the number of the memory strength values is calculated according to the difference value between the review time of the user and the optimal review time point, and the influence of the human forgetting rule is reasonably considered.

Further, the memory strength variation value Sni further includes a fatigue impact value calculated according to the formula:

Fa＝(1-Fi)×Mfa，Fi＝De/Ds；

wherein Fa is a fatigue influence value, fi is a fatigue index, mfa is a fatigue index influence memory strength basic value, de is a learning effective duration, and Ds is a fatigue set duration.

With the above scheme, the learning effective duration De is the interaction time of the user and the learning interface, the fatigue index influence memory strength basic value Mfa is expressed as the fatigue degree which affects the memory strength value at most, the longer the learning time is, the more fatigued the user is, the fewer the memory strength values are increased and decreased, and otherwise, the larger the memory strength values are.

Further, the method for word listen optimal review time planning further comprises a testing stage, the learning information further comprises testing information, and the optimal review time point comprises a second optimal review time point.

Further, a second best review time point is calculated according to the test information.

By adopting the scheme, the influence of the test information on the memory strength value and the influence of the review information on the memory strength value are integrated, so that the learning of the user can be more diversified, and the grasping degree of the user on the learning word can be more comprehensively and comprehensively reflected; since the user will also memorize words during the test, the test will affect the memory strength value and thus the best review time point, the second best review time point being the best review time point adjusted at the first best review time point due to the effect of the test.

Preferably, the test information comprises a new word answer pair and a new word answer mistake;

when the new word is answered by mistake, the memory strength value of the user for the new word is reduced, and the reduced value is a direct reduced value for the new word test;

when answering the new word, the memory strength value of the user for the new word is increased, and the added value is directly added for the new word test.

Further, when the test information is a word answer pair, tbr2=tq+d2, wherein D2 is a second review interval duration, tbr2 is a second best review time point, and Tq is a test time point;

when the test information is word-in-process and the test time point is later than the first best review time point, tbr2=tbr1+d3, and when the test information is word-in-process and the test time point is earlier than or equal to the first best review time point, tbr2=tq+d3, wherein Tbr1 is the first best review time point and D3 is the third review interval duration.

By adopting the scheme, the user can memorize the learning word again in the test, the user can learn more reasonably by determining the second optimal review time point, the test can cause the change of the memory strength, and the memory strength can cause the change of the review interval duration; therefore, the optimal review time point can be provided for the user more reasonably by calculating the review interval duration under different conditions according to different test information.

Preferably, the test information further comprises a word-of-note answer pair and a word-of-note answer mistake;

when the test information is a cooked word answer, a second optimal review time point Tbr2 is not generated;

when the test information is a wrongly written word, the learning word is changed to a new word, tbr2=tq.

By adopting the scheme, although the degree of mastering the cooked words by the user is very high and cannot appear in review, the user can forget the cooked words in consideration of possibility, so that the cooked words are arranged to appear in the test and detected, and when the user answers the cooked words, the user is proved to still have very high degree of mastering the cooked words, and the second optimal review time is not required to be set for the cooked words; when the user answers wrongly cooked words, the user is considered to have low mastery degree of the cooked words due to influence of forgetting factors, and the user needs to learn again, so that the memory strength value marked as the generated words is changed into a second initial memory strength value; when the test information is a cooked word wrong answer, tbr2=tq.

Further, the third review interval duration is according to the formula:

D3＝C1×e ^p p= (c2×sn3/10) +c3, sn3 being the third current memory strength value.

Further, the calculation of the direct reduction value of the word generation test is according to the formula:

Sqr=16+16× Rqw, rqw = Cqw/Cqt, where Sqr is a direct reduction value for a word test, rqw is a failure rate of answering of the word in the test, cqw is a total number of times the word in the test is answered in the error, cqt is a total number of times the word in the test is answered, and the constant 16 in the formula is determined according to a human forgetting curve.

By adopting the scheme, the answering error rate of the new words in the test is calculated, and the memory strength value reduced by the new words due to the answering error in the test is calculated according to the answering error rate, so that the grasping degree of the user on the new words can be analyzed more accurately and more on basis.

Further, when a new word is wrongly answered in the test, a third current memory strength value of the new word sn3=sn1-Sqr

Further, the third review interval duration is according to the formula:

D2＝C1×e ^p ，P＝(C2×Sn2/10)+C3；

sn2 is the second current memory strength value.

Further, a time interval Tit is determined from the current test time point Tq and the best review time point Tbr1, tit=tq-Tbr 1.

Further, when Tit < 24×60×60, the calculation formula of the direct increment value of the word test is Sqi = (14+12×meg×0.2)/3;

when Tit > 3×24×60×60, the calculation formula of the direct increment value of the word test is Sqi = (14+12×Meg×0.2);

When Tit is more than or equal to 24×60 and less than or equal to 3×24×60×60, the calculation formula of the direct added value of the new word test is Sqi = (14+12×Meg×0.2)/2;

sqi is a direct added value for word test, meg is an engine gear, and constants 14 and 12 in the formula are determined according to a human forgetting curve.

By adopting the scheme, the answer accuracy of the new words in the test is calculated, the memory strength value of the new words reduced by the answers in the test is calculated according to the answer accuracy, and the comparison of the test time point and the optimal review time point is introduced, so that the user can more accurately and more conveniently analyze the mastering degree of the new words.

Further, when a word is answered in the test, sn2=sn1+ Sqi.

Further, the engine gear reflects the memory level of the user on the word and shows the memorizing speed, and can be determined by the total correct rate Rrt of the user for answering the new word in the review information and the test information, the Rrt value corresponds to at least two numerical intervals, each numerical interval corresponds to a unique gear value, and when the maximum value of the first numerical interval is greater than the maximum value of the second numerical interval, the gear value corresponding to the first numerical interval is greater than the gear value corresponding to the second numerical interval.

Further, the calculation of the total correctness of the new word answer is according to the formula:

Rrt＝Crr+Cqr/Crt+Cqt；

wherein Crr is the total number of times the user answers the new word in the review process and the first learning process, cqr is the total number of times the user answers the new word in the test, crt is the total number of times the user answers the new word in the review process and the first learning process, and Cqt is the total number of times the user answers the new word in the test.

By adopting the scheme, the speed of memorizing each new word by a user can be reflected through the setting of the engine gear, and the test information and the review information are counted, so that the accuracy of the user response can be analyzed more comprehensively, and the analysis data is more authoritative.

Further, the total number Cqr of the user answering pairs of the new words in the test is determined according to the time interval Tit between the current test time point Tq and the best review time point Tbr.

Preferably, when the user performs a new word review, the increased or decreased memory strength value further includes a correction difficulty influence value, and the calculation of the correction difficulty influence value is according to the formula:

Df＇＝Dti＇×Mdt，Dti＇＝(Dm＇+Am),Dm＇＝Rwr＇×λ，Rwr＇＝Crw+Cqw/Crt+Cqt；

df 'is a correction difficulty influence value, dti' is a correction difficulty index, mdt is a difficulty index influence memory strength basic value, dm 'is correction learning data calculation difficulty, am is artificial labeling difficulty, rwr' is error rate of answering the raw word in the process of user review and test, lambda is a difficulty mark, crw is sum of times of answering the raw word in the process of user review and primary learning, crt is total times of answering the raw word in the process of user review, cqw is total times of answering the raw word in the process of test, cqt is total times of answering the raw word in the process of test.

By adopting the scheme, the change of the difficulty influence value is calculated and tested, and the difficulty influence value is corrected, so that the grasping degree of a user on learning words can be analyzed more accurately and finely.

Preferably, when the user performs a new word review, the increased memory strength value further includes a gear influence increment value, and the calculation of the gear influence increment value is according to the formula:

G1＝Meg×0.1×Reg；

wherein Meg is the engine gear, and Reg is the answer pair engine constant.

By adopting the scheme, G1 is a gear influence increasing value, and the answer pair engine constant Reg is determined according to a human forgetting rule.

Preferably, when the user performs a new word review, the reduced memory strength value further includes a gear influence reduction value calculated according to the formula:

G2＝Weg×Crw/Crt；

wherein Weg is an error-answering engine constant, crw is the total number of times of answering the learning new word in review, and Crt is the total number of times of answering the learning new word in review.

By adopting the scheme, G2 is a gear influence reduction value, and the error-answering engine constant Weg is determined according to a human forgetting rule.

The invention also protects a word listening optimal review time planning device, which comprises: memory, a processor and a computer program stored on the memory and executable on the processor, which processor implements the above-described method when executing the program.

In summary, the invention has the following beneficial effects:

1. according to the word memory strength calculation method provided by the invention, the learned words are marked according to the learning information, different current memory strength values are generated according to different marks of different learned words, the memory strength values reflect the mastering degree of the learner on the words, and the higher the memory strength value is, the higher the mastering degree of the learned words is, and the lower the conversely is; and calculating the optimal review time point by calculating the review interval time length, and comprehensively examining the mastering condition of the learner on the learning word so as to provide the optimal and reasonable review time.

2. According to the word memory strength calculation method provided by the invention, when the external environment is too noisy and a user is difficult to hear, the external environment has a great influence on learning objectively and is not recorded as an effective sample, but if a learner uses external equipment such as a headset, the learner is not influenced, the learning is not influenced, and the learning is counted as effective learning.

3. According to the word memory strength calculation method provided by the invention, different first optimal review time points are generated through different learning conditions of different words by a user; after a user finishes learning a new word for the first time and marks the new word as a word, when the learning word is marked as the word, the learning is not the first learning, and the review stage is already entered; when the learning word marking information is a new word and the user answers or overturns, the learning word marking information indicates that the user has very low mastering degree of the new word, and different computing methods are adopted under different conditions to more reasonably calculate a first optimal review time point, so that the learning efficiency is improved.

4. According to the word memory strength calculation method provided by the invention, the memory strength value reduced by the new word in the test is calculated according to the response accuracy by calculating the response accuracy of the new word in the test, and the user can more accurately and more conveniently analyze the mastering degree of the new word by introducing the comparison between the test time point and the optimal review time point.

Drawings

In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of one embodiment of a word memory strength calculation method of the present invention;

FIG. 2 is a flow chart of one embodiment of outputting speech information of a learning word in accordance with the present invention;

FIG. 3 is a schematic illustration of the present invention;

FIG. 4 is a schematic diagram of the answer result of the invention;

FIG. 5 is a diagram illustrating a judging and executing process according to the present invention.

Detailed Description

Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. The implementations described in the following exemplary examples do not represent all implementations consistent with the invention. Rather, they are merely examples of apparatus and methods consistent with aspects of the invention as detailed in the accompanying claims.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any or all possible combinations of one or more of the associated listed items.

The words described herein may refer to, but are not limited to, english words, and for convenience of unified calculation, the unit of operation related to the duration is unified as seconds.

Experimental example

Method one

Outputting the voice information of the learning word;

Calculating review interval duration according to the learning information;

Method II

Similar to method one, the difference is that: judging whether external playing is used when outputting the voice information of the learning word;

if yes, the learning of the word is counted as effective learning;

setting a sound intensity threshold parameter, wherein the sound intensity threshold parameter is set to 55 dB;

if yes, counting the learning of the learning word as invalid learning;

if not, the learning of the learning word is counted as effective learning.

Method III

Similar to method one, the difference is that: giving a first initial memory strength value of 100 to the user that the answer is correct within 5 seconds (including 5 seconds), marked as a cooked word; when the user response time exceeds 20 seconds, a second initial memory strength value 33 is given; when the answer time of the user is more than 5 seconds and less than or equal to 20 seconds, the answer is still correct, the memory intensity value of the user for the word is endowed with a third initial memory intensity value, the third initial memory intensity value can be calculated according to the formula I= (Dz- (D3 ' -Db))x2 because of the difference of the answer time, I is the third initial memory intensity value, D3 ' is more than or equal to 20, and D3 ' is the actual reaction time. Judging whether external playing is used when outputting the voice information of the learning word; if yes, the learning of the word is counted as effective learning; if not, receiving external audio information when outputting the voice information of the learning word; setting the sound intensity threshold parameter to 55 dB; judging whether the external sound intensity of the external audio information is larger than the sound intensity threshold parameter; if yes, counting the learning of the learning word as invalid learning; if not, the learning of the learning word is counted as effective learning.

Method IV

Outputting the voice information of the learning word;

calculating review interval duration according to the learning information;

Judging whether external playing is used when outputting the voice information of the learning word; if yes, the learning of the word is counted as effective learning; if not, receiving external audio information when outputting the voice information of the learning word; setting the sound intensity threshold parameter to 55 dB; judging whether the external sound intensity of the external audio information is larger than the sound intensity threshold parameter; if yes, counting the learning of the learning word as invalid learning; if not, the learning of the learning word is counted as effective learning.

The memory strength increasing or decreasing value further comprises a difficulty influence value, and the calculation formula of the difficulty influence value is as follows: df=dti×mdt, dti= (dm+am), dm=rwr×λ, rwr=crw/Crt;

df is a difficulty influence value, dti is a difficulty index, mdt is a memory strength basic value influenced by the difficulty index, dm is learning data calculation difficulty, am is artificial labeling difficulty, rwr is error rate of answering the raw word in a user review process, λ is a difficulty mark, crw is sum of times of answering the raw word in the user review process and primary learning, crt is total times of answering the raw word in the user review process, and Mdt takes a value of 3; lambda takes a value of 5.

The memory strength increase or decrease further comprises a reaction duration influence value, and the calculation formula of the reaction duration influence value is as follows: rd= (1-Mrd/Da) x Srd, wherein Mrd is the response time length, srd is the reaction time length influence memory strength basic value, rd is the reaction time length influence value, da is the first reaction time length, the Srd value is 7, and Mrd unit is second.

The memory strength increasing or decreasing value further comprises a fatigue influence value, and the fatigue influence value is calculated according to the following formula:

Fa＝(1-Fi)×Mfa，Fi＝De/Ds；

the fatigue strength control method comprises the steps that Fa is a fatigue influence value, fi is a fatigue index, mfa is a fatigue index influence memory strength basic value, de is a learning effective duration, ds is a fatigue setting duration, de is the interaction time of a user and a learning interface, 30 minutes per day can be obtained according to a human forgetting curve, 30 multiplied by 60 is converted into 1800 seconds, ds can be 1800 seconds, the fatigue index influence memory strength basic value Mfa is expressed as the fatigue degree influence memory strength value at most, and Mfa can be 4.

Method five

The method is different from the method IV in that: the relearning further includes a test, the relearning information further includes test information including: when the user answers the cooked word in the test stage, the memory strength of the cooked word is not changed; when the user answers the cooked word in the test stage, the cooked word is re-marked as a new word and the memory strength value becomes a second initial memory strength value; when the user answers the new word, the memory strength value of the new word is reduced by the strength reduction value; when the user answers the new word, the memory strength value of the new word is increased by the strength increasing value.

60 volunteers aged 18-21 years are divided into 6 groups of 10 people each, 500 people learn the same English word, and the learning time is 2 weeks; the test results after the learning of each group are shown in the following table:

table 1 test results obtained with different learning methods

Referring to the results in table 1, the accuracy is obviously improved (P < 0.01) from group two to group six compared with group one, the marking of new words and cooked words is illustrated, the user is helped to learn in a targeted manner through the displayed memory strength degree, and the learning effectiveness is improved; compared with the group II, the group III and the group IV have obviously improved accuracy (P is less than 0.01), divide the memory intensity degree more finely and learn more specifically; the improvement of the word-cooked accuracy (P < 0.01) is obviously improved in the group five and the group six compared with the group two, which means that the increased or decreased value in the group five and the group six can be changed according to the fatigue degree, the word difficulty degree and the like, and compared with the mechanically increased or decreased fixed value, the memory strength value can more accurately reflect the mastery degree of the user; and compared with the group III, the group III has the advantages that the accuracy rate of the cooked words is improved (P is less than 0.01), the increase test is described, and the identification of the cooked words is dynamically changed, so that the memory strength value can more accurately reflect the actual mastering condition of a user.

Examples

Referring to fig. 1, 3 and 4, the present invention provides a method for optimizing a word listening review time plan, including:

outputting the voice information of the learning word;

calculating review interval duration according to the learning information;

In the implementation process, the voice information of the output learning word is output from a learning library selected by a user, wherein the learning library can be a four-level learning library, a six-level learning library or a yasi learning library; the output from the user selected learning library may be a random output, and the english information includes, but is not limited to, word spelling information, word phonetic symbol information, and word part-of-speech information.

In the implementation process, playing the audio of the corresponding word in the selected word bank, wherein the user can select the smiling face or crying face in the graph 3 to answer, the smiling face shows that the learning word is known, the crying face shows that the learning word is not known, then the interface of the graph 4 can appear, and the user can select to beat or cross to determine whether to answer or answer wrong; and then marking the learned words according to the first learning information of the user, and generating different current memory strength values according to different marks of different learned words.

By adopting the scheme, the user can be initially distinguished into different mastering degrees of different words through the marks, the initial memory strength value can further represent the different mastering degrees of the user on the different words, and the first optimal review time point is calculated by calculating the first review interval duration. The first optimal review time point can comprehensively consider the mastering condition of the learning word by the user, so that the optimal and reasonable review time is provided.

As shown in fig. 2, in the implementation process, the outputting the voice information of the learning word further includes:

judging whether external playing is used when outputting the voice information of the learning word, wherein the external playing comprises external earphone playing, sound playing or Bluetooth equipment playing;

If yes, the learning of the word is counted as effective learning;

setting a sound intensity threshold parameter;

if yes, counting the learning of the learning word as invalid learning;

if not, the learning of the learning word is counted as effective learning.

In the specific implementation process, the ineffective learning is an ineffective learning sample, namely, the ineffective learning is regarded as not performing the learning; the effective learning is normal learning.

In a specific implementation, the db threshold parameter may be 50, 55, 60 db, or the like.

In a preferred implementation of this embodiment, the decibel threshold parameter is 55 decibels.

By adopting the scheme, the indoor noise standard cannot exceed 55 dB in daytime according to national law, and when the dB number of the external audio information is more than 55 dB, the influence on a learner is larger.

In the specific implementation process, the learning information comprises review information and primary learning information, and the marking information comprises primary learning new word answering errors, review new word answering pairs, review new word answering errors and review new word answering overtime.

In a specific implementation process, the receiving learning information of the learning word includes receiving the time information, including the steps of:

In the implementation process, the first reaction time length can be 20 seconds, the second reaction time length can be 5 seconds, and the user can answer correctly within 5 seconds (including 5 seconds), which means that the user has high mastering degree on the learning word; when the answer time of the user exceeds 20 seconds, the user is considered to answer overtime, the user is stated to grasp the word very little and needs to think for a long time to answer, and the setting of the answer overtime avoids the user from consuming too much time, and the user is considered to not grasp the learning word no matter how much of the answer time is wrong under the same answer condition; when the answer time of the user is more than 5 seconds and less than or equal to 20 seconds, the user still answers, and the user is proved to have a certain mastering degree of the learning word, but the mastering degree is not high, at the moment, the memory intensity value given to the learning word by the user is a third initial memory intensity value, the third initial memory intensity value is more than the second initial memory intensity value but less than the first initial memory intensity value, the size of the initial memory intensity value can be determined according to actual conditions, for example, the highest first initial memory intensity value is 100, the second initial memory intensity value is 10, the third initial memory intensity value can be calculated according to the formula I= (Dz- (D3 ' -Db)) ×2, I is the third initial memory intensity value, D3 ' is more than or equal to 20, and D3 ' is the actual reaction time.

By adopting the scheme, the first reaction time length and the second reaction time length are set, so that the mastering degree of the user on the learning word can be further and accurately reflected according to the user response time length, the concentration degree of the user can be increased, and the user has a sense of urgency, so that the learning efficiency is improved.

In a specific implementation process, the obtaining the optimal review time point includes: calculating a first optimal review time point after primary learning or secondary review; when the learning word marking information is a new word and the user answers by mistake, the first best review time point is according to the formula:

In a specific implementation process, the calculating of the optimal review time point further includes the steps of:

judging the number of continuous answer pairs of the same word;

if not, not adjusting;

if yes, the optimal review time point is set in the next review period.

In the implementation process, when the learning word is answered three times in succession on the same day and the calculated first optimal review time point is still on the same day as the three continuous learning, the first optimal review time point is adjusted to 5:00-11:00 in the morning of the next day.

In a specific implementation process, the first optimal review time point can be adjusted to be 5 points, 6 points or 7 points in the morning of the next day.

In a preferred implementation of the present embodiment, the first best review time point is adjusted to 6 a.m. the second day.

By adopting the scheme, firstly, according to the promotion effect of sleep on memory, the optimal review time point is adjusted to the morning of the next day, so that scientific memory is facilitated; secondly, considering that the brain needs to be fully rested, the review time should not be too early, the review time is scientifically distributed, and the learning efficiency is improved.

In a specific implementation process, the determining of the first current memory strength Sn includes: when the learning word is a new word for initial learning, the first current memory strength Sn is recorded as a second initial memory strength value or a third initial memory strength value according to the above description; sn=sn' + Sni when the learning word is the user answer to the new word in the review process; when the user answers the new word by mistake or overtime, sn=sn '-Sni, where Sn' is a memory strength base value and Sni is a memory strength change value.

In a specific implementation process, the memory strength variation value Sni further includes a reaction duration influence value, and a calculation formula of the reaction duration influence value is as follows:

By adopting the scheme, the basic value Srd of the response time length influence memory strength can be determined according to the overall assignment situation and the human forgetting rule, in the embodiment, the basic value Srd of the response time length influence memory strength is 8, the maximum influence of the response time length on the memory strength value is represented, and Mrd is the unit of the response time length is seconds; the influence value of the reaction time is calculated, so that the grasping degree of the user on the word can be accurately and finely calculated according to the speed of the user to answer.

In a specific implementation process, the memory strength variation value Sni further includes a difficulty influence value, and the calculation formula of the difficulty influence value is as follows:

df=dti×mdt, dti= (dm+am), dm=rwr×λ, rwr=crw/Crt; df is a difficulty influence value, dti is a difficulty index, mdt is a memory strength basic value influenced by the difficulty index, dm is learning data calculation difficulty, am is artificial annotation difficulty, rwr is error rate of answering the new word in the user review process, λ is a difficulty mark, crw is sum of times of answering the new word in the user review process and primary learning, and Crt is total times of answering the new word in the user review process.

By adopting the scheme, the difficulty influence value can comprise manual marking difficulty and learning data calculation difficulty, for example, the manual marking difficulty is the difficulty of a word or a sentence, the length, the word forming rule, chinese interpretation and other aspects are reflected, the words with more letters than letters are difficult to record, the letters are arranged regularly and more than irregularly difficult to record, and the different difficulty of different words need to be marked manually to distinguish; the calculation difficulty of the learning data is that the error rate of word response is calculated by a user; the difficulty mark lambda is used for calculating the learning data calculation difficulty, the difficulty mark lambda can be displayed on a response interface in the form of an energy grid, the memory strength basic value Mdt influenced by the difficulty index is determined according to the overall assignment condition and the human forgetting rule, the difficulty mark lambda is expressed as the influence of the word difficulty on the memory strength value, and the Mdt value in the embodiment is 3.

In a specific implementation process, the memory strength variation value Sni further includes a diligence influence value, and a calculation formula of the diligence influence value may be: dli= Dgi × Mdg, dgi = (Trc 2-Tbr 1)/24×60×60, where Dli is a diligence impact value, dgi is a diligence impact index, mdg is a diligence index impact memory strength base value, tbr1 is the first best review time point, trc2 is the current review time point.

In a specific implementation process, the memory strength variation value Sni further includes a fatigue impact value, and a calculation formula of the fatigue impact value is as follows:

Fa＝(1-Fi)×Mfa，Fi＝De/Ds；

With the above scheme, the learning effective duration De is the interaction time of the user and the learning interface, and as the learning time of 30 minutes per day is most suitable according to the human forgetting curve, 30×60 is the conversion of 30 minutes into 1800 seconds, the fatigue index influences the memory strength basic value Mfa to represent the fatigue degree to influence the memory strength value most, the longer the learning time is, the fatigued the user is, the less the memory strength values are increased and decreased, and otherwise the larger the memory strength values are increased and decreased. The fatigue influence value is sufficiently calculated from the physiological law of the person to take the influence on the memory ability into consideration, and the increase and decrease of the memory strength value is more accurately and finely calculated, and the Mfa value is 4 in the embodiment according to the human forgetting law.

In a specific implementation, the fatigue impact value is calculated at 30 minutes when the learning time of a day is greater than 30 minutes.

In the implementation process, the method for planning the best review time of the word listen further comprises a test stage, the learning information further comprises test information, and the best review time point comprises a second best review time point.

In the specific implementation process, a second optimal review time point is calculated according to the test information.

In the specific implementation process, the test information comprises a new word answer pair and a new word answer mistake;

In a specific implementation process, when the test information is a word answer pair, tbr2=tq+d2, wherein D2 is a second review interval duration, tbr2 is a second best review time point, and Tq is a test time point;

In the specific implementation process, the test information also comprises a cooked word answer pair and a cooked word answer mistake;

In a specific implementation process, the third review interval duration is according to the formula:

In a specific implementation process, the calculation of the direct reduction value of the word generation test is according to the formula:

In the specific implementation process, when a new word is wrongly answered in the test, the third current memory strength value of the new word is Sn3=Sn1-Sqr

D2＝C1×e ^p ，P＝(C2×Sn2/10)+C3；

sn2 is the second current memory strength value.

In a specific implementation process, a time interval Tit is determined according to the current test time point Tq and the optimal review time point Tbr1, wherein tit=tq-Tbr 1.

In the specific implementation process, when Tit is less than 24 multiplied by 60, the calculation formula of the direct added value of the new word test is Sqi = (14+12 multiplied by Meg multiplied by 0.2)/3;

In a specific implementation, sn2=sn1+ Sqi when word answering is occurring in the test.

The engine gear reflects the memory level of the user on the words and shows the memory speed, the total correct rate Rrt of the user for answering the new words in review information and test information can be determined, and the engine gear can be divided into 10 gears as follows:

when Rrt is 5 or less: the gear value is 1;

when Rrt is more than 5 and less than or equal to 15, the gear value is 2;

when Rrt is more than 15 and less than or equal to 20, the gear value is 3;

when Rrt is more than 20 and less than or equal to 30, the gear value is 4;

when Rrt is more than 30 and less than or equal to 45, the gear value is 5;

when Rrt is more than 55 and less than or equal to 70, the gear value is 6;

when Rrt is more than 70 and less than or equal to 80, the gear value is 7;

when Rrt is more than 80 and less than or equal to 85, the gear value is 8;

When Rrt is more than 85 and less than or equal to 95, the gear value is 9;

when Rrt is greater than 95: the gear value is 10.

In a specific implementation process, the calculation formula of the total accuracy of the new word response may be: rrt= Crr + Cqr/crt+ Cqt, where Crr is the total number of times the user answers the new word in the review process and in the first learning, cqr is the total number of times the user answers the new word in the test, crt is the total number of times the user answers the new word in the review process and in the first learning, and Cqt is the total number of times the user answers the new word in the test. The speed of memorizing each new word by the user can be reflected through the setting of the engine gear, and the test information and the review information are counted, so that the accuracy of the user response can be more comprehensively analyzed, and the analysis data is more authoritative.

The total number of times Cqr the user answers the new word pairs in the test is determined according to the time interval Tit between the current test time point Tq and the optimal review time point Tbr.

When Tit < -7×24×60×60, the total number of times Cqr the user answers the new word pairs in the test does not increase; when Tit > 7×24×60×60, the total number Cqr of the user to the new word answer pairs in the test is increased by 2 times; when the Tit is less than or equal to 7×24×60 and less than or equal to 7×24×60×60, the total number Cqr of the user's answers to the new word in the test is increased by 1+Tit/(7×24×60×60).

When Tit < -7×24×60×60, the total number of times Cqw of user's mistakes to the new word in the test is increased by 2 times; when Tit > 7×24×60×60, the total number Cqw of user mistakes the new word in the test does not increase; when Tit is less than or equal to 7 multiplied by 24 multiplied by 60 and less than or equal to 7 multiplied by 24 multiplied by 60, the total number Cqw of times the user answers the new word in the test is 1-Tit/(7 multiplied by 24 multiplied by 60).

By adopting the scheme, the representation modes of the optimal review time point and the test time point adopt a time stamp mode, namely the number of seconds from 1 month, 1 day, 00:00:00 in 1970 to the corresponding time point; the influence of forgetting on human memory is comprehensively considered by determining according to the time interval Tit, so that the fact that the answer pair or the answer mistake is recorded as one time in a general way is avoided, and statistics can be accurately carried out by combining human physiological and psychological rules. When the test time point is 7 days or more earlier than the optimal review time point, the number of test answer pairs Cqr is not increased because the user is considered to respond to the answer pairs in the time period, but the user does not answer pairs; when the test time point is 7 days later than the optimal review time point, the test answer number Cqr is increased by 2 because the user is considered to have forgotten in the time period, but the user still can answer the answer; when the test time point is not earlier than 7 days or not later than 7 days of the optimal review time point, then the calculation is reasonably performed according to a formula.

In the specific implementation process, when the user performs word-making review, the increased or decreased memory strength value further comprises a correction difficulty influence value, and the calculation of the correction difficulty influence value is as follows: df ' =Dti ' x Mdt, dti ' = (Dm ' +Am), dm ' =Rwr ' ×λ, rwr ' =crw+ Cqw/crt+ Cqt; df 'is a correction difficulty influence value, dti' is a correction difficulty index, mdt is a difficulty index influence memory strength basic value, dm 'is correction learning data calculation difficulty, am is artificial labeling difficulty, rwr' is error rate of answering the raw word in the process of user review and test, lambda is a difficulty mark, crw is sum of times of answering the raw word in the process of user review and primary learning, crt is total times of answering the raw word in the process of user review, cqw is total times of answering the raw word in the process of test, cqt is total times of answering the raw word in the process of test.

In the implementation process, when the user performs the new word review, the increased memory strength value further includes a gear influence increasing value, and a calculation formula of the gear influence increasing value may be g1=meg×0.1×reg, where Meg is an engine gear, and Reg is an answer pair engine constant.

By adopting the scheme, G1 is a gear influence increasing value, the answer pair engine constant Reg is determined according to the human forgetting rule, and the value can be 6 in the embodiment.

In the specific implementation process, when the user performs the word-learning, the reduced memory strength value further includes a gear influence reduction value, and the calculation formula of the gear influence reduction value may be g2=weg×crw/Crt, where Weg is an error-learning engine constant, crw is the total number of times of answering the learning word in the learning, and Crt is the total number of times of answering the learning word in the learning.

By adopting the scheme, G2 is a gear influence reduction value, the error-answering engine constant Weg is determined according to a human forgetting rule, and the value can be 7.5 in the embodiment.

Referring to fig. 5, in some embodiments of the present invention, the system performs the judgment in each step on the word according to the judgment result, and then performs the assignment of parameters to the judgment results, respectively; the first step is to judge whether the word is a new word or not, judge whether the user answers according to the judging result, give the parameters to the answering result, and finally calculate and store the review time.

The invention also provides a word listening optimal review time planning device, which comprises: memory, a processor and a computer program stored on the memory and executable on the processor, which processor implements the above-described method when executing the program.

It should be noted that it will be apparent to those skilled in the art that various changes and modifications can be made to the present invention without departing from the principles of the invention, and such changes and modifications will fall within the scope of the appended claims.

Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.

It should be understood that in the embodiments of the present application, the claims, the various embodiments, and the features may be combined with each other, so as to solve the foregoing technical problems.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein, so as to enable or to enable persons skilled in the art with the aid of the foregoing description of the disclosed embodiments. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for word listening optimal review time planning, comprising:

Outputting the voice information of the learning word;

calculating review interval duration according to the learning information;

receiving the review interval duration, and obtaining an optimal review time point according to the learning information and the review interval duration;

the obtaining of the optimal review time point comprises calculating a first optimal review time point; when the learning word marking information is a new word and the user answers by mistake, the first best review time point is according to the formula:

when the learning word mark information is a new word and the user answers, tbr1=trc2+d1, trc2 is the current review time point; when the learning word marking information is a new word and the user answers by mistake or overtime, tbr1=tbr1 '+d1, wherein tbr1' is the first best review time point after the last learning is completed;

The step of obtaining the optimal review time point comprises the steps of calculating a first optimal review time point after primary learning or secondary review;

the outputting the speech information of the learning word further includes:

if yes, the learning of the word is counted as effective learning;

setting a sound intensity threshold parameter;

if yes, counting the learning of the learning word as invalid learning;

if not, the learning of the word is counted as effective learning;

the receiving learning information for the learning word includes receiving the time information, including:

when the learning word is a new word, the response information is correct, the response time is longer than the second response time and shorter than or equal to the first response time, the learning word is marked as a new word, the memory strength value is a third initial memory strength value, the third initial memory strength value is calculated according to the formula i= (Dz- (D3 '-Db))x2, dz is an extremum, I is a third initial memory strength value, D3' is a response time, da is a first response time, db is a second response time;

2. The method of claim 1, wherein the calculating of the best review time point further comprises the steps of:

judging the number of continuous answer pairs of the same word;

If not, not adjusting;

if yes, the optimal review time point is set in the next review period.

3. The method of claim 2, wherein the determining of the first current memory strength Sn comprises: when the learning word is a new word for initial learning, the first current memory strength Sn is recorded as a second initial memory strength value or a third initial memory strength value; sn=sn' + Sni when the learned word is the user answer to the new word in the review process; when the user answers the new word by mistake or overtime, sn=sn '-Sni, where Sn' is a memory strength basic value and Sni is a memory strength change value.

4. A method of optimizing review time planning for a word listening according to claim 2 or 3, wherein the second best review time point is calculated based on test information, the test information including a new word answer pair, a new word answer mistake;

5. The method of claim 4, wherein when the test information is a word answer, tbr2=tq+d2, wherein D2 is a second review interval duration, wherein Tbr2 is a second best review time point, and Tq is a test time point;

6. The method of claim 5, wherein the test information further comprises a word-in-word answer pair and a word-in-word answer mistake;

7. An apparatus for scheduling a word-listening optimal review time, comprising: memory, a processor and a computer program stored on the memory and executable on the processor, said processor implementing the method of any of the preceding claims 1-6 when said program is executed.