CN109711760A - It is suitble to measure the analysis method of adaptive students ' learning performance - Google Patents
It is suitble to measure the analysis method of adaptive students ' learning performance Download PDFInfo
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Abstract
The invention discloses a kind of suitable analysis methods for measuring adaptive students ' learning performance comprising following steps: S1: student login Adaptable System carries out answer to teaching exercise, the answer data are automatically stored in logging modle；S2: user inputs screening index, the answer data stored in logging modle is screened and marked according to the screening index, form garbled data；S3: user inputs analysis indexes, analysis module reads garbled data, carries out analytical calculation to garbled data, and exports analysis result；S5: user reads analysis as a result, adjusting the content of courses according to the analysis result.The present invention can the quick high knowledge point of abnormal rate in positioning teaching content, targetedly adjust the teaching resource of investment, improve teaching job efficiency, excavate the teaching potentiality of teacher.
Description
Technical field
The invention belongs to on-line study technical fields, relate in particular to a kind of suitable adaptive students ' learning performance of measurement
Analysis method.
Background technique
Traditional school instruction and education product can not accumulate large-scale student individuality learning data in a short time,
Therefore develop in the analysis of teaching data more slowly, and existing instruction analysis method is mainly based upon student examination achievement
Determine students ' learning performance, index is single and can not rule out a lot of other influencing factors, for example can not judge the road student Zuo Duimou
Topic is because student has hit it and answer or grasped this knowledge point.Teacher can not be according to this method to duration of giving lessons, and religion
It learns content emphasis point or content is optimized.It cannot achieve the progress that teaching plan is led by teaching data.It is open
Number for CN201711191614.2 patent application disclose a kind of on-line study effect evaluating method, itself the following steps are included:
Obtain user's learning records data；It obtains the learning effect that user proposes and evaluates and tests request；It is found out pair according to the learning Content of user
The test examination question is showed user by the test examination question answered；Obtain the script that user submits；Calculate test result, knot
It shares family learning records, test examination question and test result to be analyzed and assessed, obtains the assessment result of learning effect；It will be described
Assessment result is sent to user.This scheme can realize the index for measuring students ' learning performance by web terminal.But the program according to
So student can not cannot be provided to the grasp feelings of concrete knowledge point to the habit effect of course or class time by students ' immediately
Condition.Therefore, a kind of novel self-adaptive teaching system how is developed, is that those skilled in the art need to overcome the above problem
The direction to be studied.
Summary of the invention
It, can quickly abnormal rate be high in positioning teaching content the object of the present invention is to provide a kind of self-adaptive teaching system
Knowledge point targetedly adjusts the teaching resource of investment, improves teaching job efficiency, excavates the teaching potentiality of teacher.
The technical scheme adopted is as follows:
A kind of self-adaptive teaching system comprising: memory module, management module, logging modle, interactive module screen mould
Block and analysis module.Memory module is for storing teaching exercise and the corresponding knowledge point of each teaching exercise；Management module is for remembering
Record and management student information；Logging modle is used to receive and store student to the answer data of teaching exercise；Interactive module is used for
Receive the screening index and analysis indexes of user's input；Screening module is used for the screening index according to input, reads answer data
And it screened and is marked, generated and export garbled data；Analysis module is used for the analysis indexes according to input, reads sieve respectively
Modeling block and memory module carry out analytical calculation to garbled data and export analysis result.
By using this scheme:
Preferably, in above-mentioned self-adaptive teaching system: further including visualization model, the visualization model is for reading
Analysis module is shown according to analysis result generation visual image.
By using this technical solution: being shown according to visualization tool to analysis result, facilitate teacher users can
More intuitive and perceptual observation and analysis result.
It is further preferred that in above-mentioned self-adaptive teaching system: the visualization model uses Tableau server.
It is further preferred that in above-mentioned self-adaptive teaching system: the screening module uses kettle.
The present invention is based on above-mentioned self-adaptive teaching systems, further disclose a kind of adaptive student's study effect of suitable measurement
The analysis method of fruit
The technical scheme adopted is as follows:
A kind of suitable analysis method for measuring adaptive students ' learning performance comprising following steps: S1: student login is certainly
Adaptation system carries out answer to teaching exercise, the answer data are automatically stored in logging modle；S2: user input screening index, according to
The answer data stored in logging modle are screened and marked according to the screening index, form garbled data；S3: user's input
Analysis indexes, analysis module read garbled data, carry out analytical calculation to garbled data, and export analysis result；S5: user reads
Analysis is taken as a result, adjusting the content of courses according to the analysis result.
Preferably, in the above-mentioned suitable analysis method for measuring adaptive students ' learning performance: the step S3 and S5 it
Between further include step S4, the step S4 are as follows: generated according to analysis result for indicating to analyze result by visualization model
Visual image.
It is further preferred that in the above-mentioned suitable analysis method for measuring adaptive students ' learning performance: being sieved in the step S2
Select index using student information.
It may further be preferable that step S3 includes in the above-mentioned suitable analysis method for measuring adaptive students ' learning performance
Following steps: S31: defined analysis index: single student occurs connecting to 3 topics or connects wrong 3 entitled exceptions in single teaching exercise；
S32: according to meeting analysis indexes defined in S31, all garbled datas be marked: label 0 be it is without exception, be labeled as
0, it is abnormal then be labeled as 1, obtain flag data；S33: counting flag data obtained by S32, obtains the label labeled as 1
The corresponding course of data, class time, knowledge point and calculating are labeled as 1 flag data proportion.
It is also an option that following teaching plan:
First mapping spectrum knowledge points；Data area: all formal class does topic record for the life；Defined analysis index: it surveys in advance
The map knowledge point in examination stage counts.
First method of determining and calculating speculates weakness/grasp knowledge points: data area: all formal class does topic record for the life；Definition point
Analysis index: algorithm speculates that the weakness/grasp knowledge point of (student is not necessarily to inscribe) counts according to map relevance.
First survey to do to inscribe and judge weakness/grasp knowledge points: data area: the whole formal classes of the life do topic and record；Definition point
Analysis index: judge that weakness/grasp knowledge point counts by doing topic.
Learning and mastering knowledge points；Data area: all formal class does topic record for the life；Defined analysis index: study rank
The knowledge point that section is grasped counts.
First survey final knowledge points: data area of grasping: all formal class does topic record for the life；Defined analysis index: logical
Cross the sum of the knowledge point counting for doing that topic or algorithm speculative arbitration are grasped.
Learnt not grasp knowledge points: data area: all formal class does topic record for the life；Defined analysis index: it learns
The habit stage learns the knowledge point that do not grasp and counts.
Progress/room for manoeuvre knowledge points: data area: all formal class does topic record for the life；Defined analysis index: study rank
Section, ability value promotion/decline knowledge point count.
First survey/learning knowledge point master rate: data area: all formal class does topic record for the life；Defined analysis item index:
It first surveys the/study stage, grasps knowledge points divided by first mapping spectrum knowledge points.
By using above-mentioned technical proposal:
Teacher users are by reading the analysis of the teaching key to the exercises situation generation based on student as a result, it is possible to accurately capture
To single student to each course, class time, knowledge point learning effect, to find out student in learning process, effect is bad
Class time and knowledge point, and accordingly improve the teaching method to single student/adjustment content of courses, realize teacher in teaching process
In targetedly adaptive adjustment.
Compared with prior art, automated analysis of the present invention by the feedback data to student's answer teaching exercise, energy
The high knowledge point of abnormal rate, targetedly adjusts the teaching resource of investment, to assign study in enough quick positioning teaching contents
System better performance, so that more students be attracted to use.More students are further generated more during using this system
More feedback data further pushes above-mentioned process, forms benign cycle.
Detailed description of the invention
Present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments:
Fig. 1 is construction module schematic diagram of the invention；
Fig. 2 is workflow schematic diagram of the invention；
Fig. 3 is the data analysis chart that visualization model generates in embodiment 1；
Each appended drawing reference and component names corresponding relationship are as follows:
1, memory module；2, management module；3, logging modle；4, interactive module；5, screening module；6, analysis module；7,
Visualization model.
Specific embodiment
In order to illustrate more clearly of technical solution of the present invention, it is further described below in conjunction with each embodiment.
It is as shown in Figs. 1-2 embodiment 1, its technical solution is as follows:
A kind of self-adaptive teaching system comprising: memory module 1, management module 2, logging modle 3, interactive module 4, sieve
Modeling block 5, analysis module 6 and visualization model 7.
Wherein, the memory module 1 is for storing teaching exercise and the corresponding knowledge point of each teaching exercise；The management mould
Block 2 is for recording and managing student information；The logging modle 3 is used to receive and store student to the answer number of teaching exercise
According to；In this example, the answer data include each individual students in different terms, different courses, the total data of different classes time.Institute
Interactive module 4 is stated for receiving the screening index and analysis indexes of user's input；The screening module 4 is used for the sieve according to input
Index is selected, answer data are read and screened and is marked, generated and export garbled data；The analysis module 5 is used for basis
The analysis indexes of input read screening module 4 and memory module 1 respectively, analytical calculation are carried out to garbled data and exports analysis
As a result.The visualization model 7 is shown for reading analysis module 6, generating visual image according to analysis result.It is described
Visualization model 7 uses Tableau server.The screening module 5 uses kettle.
Its course of work is as follows:
S1: student login Adaptable System carries out answer to teaching exercise, the answer data are automatically stored in logging modle 3；
S2: teacher users log in Adaptable System, screening index are inputted by interactive module 4, specifies the single student of screening
Answer data, screening module 5 filters out the answer data of the single student simultaneously in logging modle (3) according to the screening index
Additional marking forms the readable garbled data of teacher users；
S3: teacher users continue through interactive module 4 and input analysis indexes, first defined analysis index: single student exists
Individually teaching exercise occurs even inscribing or connect wrong 3 entitled exceptions to 3；Analysis module 6 is based on the analysis indexes, to all screening numbers
According to being marked: label 0 is without exception then labeled as 0, it is abnormal then labeled as 1, obtain flag data；It is marked simultaneously to obtained by S32
Numeration obtains the label that the corresponding course of flag data labeled as 1, class, knowledge point and calculating are labeled as 1 according to being counted
Data proportion, finally output analysis result；
S4: it is read by visualization model 7 and generates visual image as shown in Figure 3 according to analysis result: is horizontal in Fig. 3
The abnormal rate of axial coordinate expression knowledge point；Ordinate of orthogonal axes is to indicate the sample data volume of knowledge point, and each dot represents one and knows
Know point, position of the knowledge point in Fig. 3 coordinate system is higher more its abnormal rate of keeping right, its more top data volume is bigger.And in figure
What framework was chosen is the maximum highest some knowledge points of abnormal rate simultaneously of data volume.
S5: user obtains analysis result information by observation Fig. 3, accordingly adjusts the content of courses for the knowledge point in frame.
The above, only specific embodiments of the present invention, but scope of protection of the present invention is not limited thereto, it is any ripe
The technical staff of art technology is known in technical scope disclosed by the invention, any changes or substitutions that can be easily thought of, should all contain
Lid is within protection scope of the present invention.Protection scope of the present invention is subject to the scope of protection of the claims.
Claims (5)
1. a kind of suitable analysis method for measuring adaptive students ' learning performance, which comprises the steps of:
S1: student login Adaptable System carries out answer to teaching exercise, the answer data are automatically stored in logging modle (3)；
S2: user input screening index, according to the screening index to the answer data stored in logging modle (3) carry out screening and
Label forms garbled data；
S3: user inputs analysis indexes, analysis module (6) reads garbled data, carries out analytical calculation to garbled data, and exports
Analyze result；
S5: user reads analysis as a result, adjusting the content of courses according to the analysis result.
2. being suitble to measure the analysis method of adaptive students ' learning performance as described in claim 1, it is characterised in that: the step
It further include step S4 between S3 and S5, the step S4 are as follows: generated according to analysis result for indicating by visualization model (7)
Analyze the visual image of result.
3. being suitble to measure the analysis method of adaptive students ' learning performance as described in claim 1, it is characterised in that: the step
Screening index uses student information in S2.
4. being suitble to measure the analysis method of adaptive students ' learning performance as described in claim 1, which is characterized in that step S3 packet
Include following steps:
S31: defined analysis index: single student occurs connecting to 3 topics or connects wrong 3 entitled exceptions in single teaching exercise；
S32: according to meeting analysis indexes defined in S31, all garbled datas be marked: label 0 be it is without exception, mark
It is denoted as 0, it is abnormal then be labeled as 1, obtain flag data；
S33: counting flag data obtained by S32, obtains the corresponding course of flag data labeled as 1, class, knowledge point
And calculating is labeled as 1 flag data proportion.
5. being suitble to measure the analysis method of adaptive students ' learning performance as described in claim 1, it is characterised in that: in step S3
The analysis indexes are counted using the map knowledge point of leading test phase, algorithm foundation map relevance speculates that (student is not necessarily to
Inscribe) weakness/grasp knowledge point count, by do topic judge weakness/grasp knowledge point count, the study stage grasp know
Know that point, which is counted, the sum of counted by doing the knowledge point that topic or algorithm speculative arbitration are grasped, study level-learning is not grasped knows
Know point count, study stage ability value promotion/decline knowledge point count, first survey/the study stage grasp knowledge points divided by elder generation
Mapping composes any one of knowledge points.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112017086A (en) * | 2020-08-31 | 2020-12-01 | 上海松鼠课堂人工智能科技有限公司 | Intelligent class-dividing method for matching students, teachers and parents |
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| CN103744846A (en) * | 2013-08-13 | 2014-04-23 | 北京航空航天大学 | Multidimensional dynamic local knowledge map and constructing method thereof |
| CN105355111A (en) * | 2015-12-02 | 2016-02-24 | 华中师范大学 | After-class reinforced learning system based on learning situation analysis |
| CN106327391A (en) * | 2016-08-29 | 2017-01-11 | 南京奥派信息产业股份公司 | Teaching system of flipped classroom pattern |
| CN108597280A (en) * | 2018-04-27 | 2018-09-28 | 中国人民解放军国防科技大学 | Teaching system and teaching method based on learning behavior analysis |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103744846A (en) * | 2013-08-13 | 2014-04-23 | 北京航空航天大学 | Multidimensional dynamic local knowledge map and constructing method thereof |
| CN105355111A (en) * | 2015-12-02 | 2016-02-24 | 华中师范大学 | After-class reinforced learning system based on learning situation analysis |
| CN106327391A (en) * | 2016-08-29 | 2017-01-11 | 南京奥派信息产业股份公司 | Teaching system of flipped classroom pattern |
| CN108597280A (en) * | 2018-04-27 | 2018-09-28 | 中国人民解放军国防科技大学 | Teaching system and teaching method based on learning behavior analysis |
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| CN112017086A (en) * | 2020-08-31 | 2020-12-01 | 上海松鼠课堂人工智能科技有限公司 | Intelligent class-dividing method for matching students, teachers and parents |
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