CN111199348A - S-P table-based learner stability and question set quality discrimination method - Google Patents
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Abstract
The invention discloses a learner stability and question set quality screening method based on an S-P table, which is applied to the field of big data application and aims at solving the problems that a teacher cannot be helped to improve question setting skills, a learning evaluation method or teaching efficiency and the learning difficulty of a learner cannot be diagnosed in the prior art; the invention converts the learner and the corresponding test question set into S-P original; calculating according to the SP table to obtain a learner attention coefficient and a test question set attention coefficient; finally, obtaining the discrimination result of the stability of the learner and the quality of the test question set by the learner attention coefficient and the test question set attention coefficient; the invention adopts the attention coefficient as an index, and can objectively and effectively discriminate the stability of learners and the quality of test question sets.
Description
Technical Field
The invention belongs to the field of big data application, and particularly relates to an education big data teaching application technology.
Background
In the conventional educational test, the degree of mastery of the learner is generally determined by scoring the learner on each topic or each knowledge point, but the determination cannot be made on the overall learning characteristics and stability of the learner. And the difficulty and the distinguishing capability of the question set are judged through the difficulty and the distinguishing degree.
However, none of the above processing modes can analyze the learner's response data in a microscopic manner. For example, for a learner with the same total points and answer pairs: for example, four classmates of first, second and third have 10 total questions in a certain examination, 10 scores, and the response types are respectively: a [1111100000], B [0000011111], C [1010101010] and D [0101010101] (1 represents a right answer, and 0 represents a wrong answer), the traditional treatment considers that the total score and the number of right answers are the same, the academic ability of learners is the same or the examination questions are consistent to learners, and the meaning represented by the response types of the 4 different learners is the same, so that the traditional examination analysis cannot judge.
Therefore, the conventional test question set analysis cannot further help teachers improve the question-setting skill, the learning evaluation method or the teaching performance according to the test analysis results, and cannot diagnose the learning difficulty of learners. Then, for the actual situation, how to carry out microscopic analysis according to the answer response data of the learners, objectively diagnose each learner, and judge the problems, learning ability, learning stability and effort degree of a certain learner in the examination; objectively analyzing and diagnosing each examination question, and determining the true quality, meaning, form and quality of the examination question? The attention coefficient is used as a diagnosis index of a novel education test, and can objectively and effectively reflect the problems from two aspects of learners and test question sets.
Disclosure of Invention
In order to solve the technical problems, the invention provides a method for discriminating the stability and the quality of a question set of a learner based on an S-P table, which provides a basis for discriminating an abnormal learner or an abnormal test set by adopting an attention coefficient.
The technical scheme adopted by the invention is as follows: a method for discriminating the stability and the question set quality of a learner based on an S-P table is shown in figure 1 and comprises the following steps:
s1, obtaining the scoring results of N test question sets of N trainees of a certain subject to obtain an Nxn S-P original table; the S-P original table is used for scoring N test subject sets corresponding to a learner, and the columns of the S-P original table are the scores of N learners corresponding to a test subject set;
s2, arranging the S-P original tables obtained in the step S1 from top to bottom according to the total score of the n test question sets of the learner from high to low;
s3, sequencing the number of answer pairs of the S-P original table processed in the step S2 from left to right according to the test question set;
s4, counting the number of test question sets from left to right, which is the same as the total number of the test question set answer pairs corresponding to the learner, of the S-P original table processed in the step S3, drawing a boundary on the right side of the test question set of the last answer pair, and connecting two adjacent lines of boundaries by straight lines to form a first step-shaped curve which is marked as an S curve;
counting the number of learners which is the same as the number of answers of each test question set from top to bottom according to the number of answers of each test question set in the S-P original table processed in the step S3, drawing a boundary line below the last answer pair learner, and connecting two adjacent lines of boundary lines by using a straight line to form a second step-shaped curve which is marked as a P curve;
s5, respectively calculating the learner attention coefficient and the test question set attention coefficient according to the difference ratio of the abnormal group and the perfect group;
s6, obtaining the discrimination result of the stability of the learner and the quality of the test question set according to the attention coefficient of the learner and the attention coefficient of the test question set.
Step S6 specifically includes: A. screening the stability of learners by the following steps:
a1, taking the learner's attention coefficient as the horizontal axis and taking the answer rate of n test question sets corresponding to the learner as the vertical axis;
a2, dividing the horizontal axis into Z1 parts according to the attention coefficient of the learner, and dividing the vertical axis into Z2 parts according to the answer pair rate of n test question sets corresponding to the learner; a total of Z1 × Z2 zones were obtained;
a3, outputting the learner distribution in Z1X Z2 areas as the learner stability discrimination result;
B. screening the quality of the test question set by the following steps:
b1, taking the attention coefficient of the test question set as the horizontal axis and taking the answer-to-person number percentage corresponding to a single test question set as the vertical axis;
b2, dividing the horizontal axis into Z3 parts according to the attention coefficient of the test question set, and dividing the vertical axis into Z4 parts according to the answer-to-person number percentage corresponding to a single test question set; a total of Z3 × Z4 zones were obtained;
b3, outputting the distribution of the test question set in Z3X Z4 areas as the quality screening result of the test question set.
The invention has the beneficial effects that: the invention analyzes the learner answer result by combining the S-P table, objectively diagnoses each learner, and judges the problems, the learning ability, the learning stability and the effort degree of a certain learner in the examination; objectively analyzing and diagnosing each examination question, and judging the real quality, the meaning, the form and the compiling quality of the examination question; in addition, the method adopts the attention coefficient as an index, and can objectively and effectively screen the stability of learners and the quality of test question sets.
Drawings
FIG. 1 is a flow chart of a protocol of the present invention;
FIG. 2 is a diagram showing the relationship between the learner CS and the test question CP;
FIG. 3 is a diagram of learner feature intervals;
FIG. 4 is a diagram of the quality interval of the test question set.
Detailed Description
In order to facilitate the understanding of the technical contents of the present invention by those skilled in the art, the present invention will be further explained with reference to the accompanying drawings.
1. S-P meter
The S-P table, i.e. the relation table of the learner and the question, is an effective tool for researching and analyzing the integral learning characteristics and the stability of the learner. The method analyzes the response type of each learner and each test question set, and tries to judge whether the response type is unusual or abnormal by using a plurality of indexes.
2. S-P table preparation process
The teacher collects N test question sets of N learners of a family from any class, and after scoring (1 for the responders and 0 for the wrong responders), N × N raw scoring matrix data without any processing is obtained, which is called S-P raw table, as shown in Table 2.1.
The first ten questions of the first segment of the first class of the class A, before ten study students are examined, are selected as an example, and are shown in Table 1.
TABLE 1S-P original Table
Arranging the S-P original tables from top to bottom according to the total score of each learner, if the total phase separation is the same, arranging the seat numbers or school numbers of each learner from top to bottom, as shown in Table 2
TABLE 2 arranged from top to bottom according to the learner's total score
Then, the test question sets are arranged according to the number of the answer questions of the test question sets from left (the test question set with the largest number of answer questions is arranged at the leftmost) to right, and the test question sets with the same score can also be arranged according to the size of the question numbers, as shown in Table 3.
TABLE 3 how many people are ranked from left to right according to the number of test question sets
Finally, according to the number of questions answered by each learner (called total score for short), counting the number of test question sets from left to right, which is the same as the total score, drawing a boundary line on the right, drawing the boundary line corresponding to the total score of each learner from the high score to the low score, and connecting the lower parts of the boundary lines by using a straight line to form a step-like curve, which is called an S curve, as shown in Table 4. Similarly, according to the number of answer pairs of each test question set, the number of learners is counted from top to bottom, and a boundary line is drawn on the lower side of the learners, and the boundary line corresponding to the number of answer pairs of each test question set is drawn from the left end to the right end, so that a step-like curve is formed, which is called a P-curve, as shown in table 4.
The S curve is the accumulated distribution curve of the learner score, which is used to distinguish the boundary between the learner 'S answer and wrong answer and reflect the achievement degree of the learner' S learning achievement. The P curve is the cumulative distribution curve of the number of the test question set answers, and is used for distinguishing the boundary between the number of the test question set answers and the number of the wrong answer, and reflecting the degree of the class learner reaching and failing to reach the teaching target. The top left of the S-P table represents the better learner and the simpler test question set, most of which are the test question sets that are expected to be answered, so that most of 1' S should appear in this area. In contrast, in the lower right of the S-P table, most 0' S should appear.
Table 4 shows the overlapping portion (bold line) of the S-curve (solid line) and the P-curve (dotted line)
For each learner, the number of wrong answers [0] on the left side of the S-curve is equal to the number of right answers [1 ]. For each test question, the number of wrong answers (0) above the P curve is equal to the number of wrong answers (1) below the P curve. When the part of the S curve above the left curve or the P curve is 1, the condition is called as an optimal reaction type, and the other parts are called as abnormal reaction types, and the abnormal degree is judged through the difference coefficient.
3. Answer ratio P
N indicates the number of learners, and N is the number of question X sets.
Average answer rate
The ratio of the left portion of the S-curve or the portion above the P-curve to the entire S-P surface area is shown.
4. Note the coefficient (CP/CS)
The attention coefficient includes a learner attention coefficient CS and a test question attention coefficient CP, representing the difference ratio of the abnormal group to the perfect group. It is mainly used as the index for judging whether the learner or the test question set has abnormal phenomena in the reaction group, and the teacher can use the index to know the condition and the problem of the learner or the test question set.
The learner attention coefficient CS is calculated by the formula:
wherein y isijShows the reaction condition of learner i on the jth question, yjIndicates the number of right-handed people of the test question j, yiThe total score of the learner is shown, and u' represents the average number of right-answer test questions. y isijThe data corresponding to the ith row and the jth column in the SP table.
The test question attention coefficient CP is calculated by the formula:
wherein y isijShows the reaction condition of learner i on the jth question, yiRepresents the total score y of learner ijThe number of the answer pairs of the test question j is shown, and the average score of the learner is shown in u.
The present invention illustrates one particularly feasible way of discrimination by way of example:
as shown in fig. 2, the attention coefficient mainly provides a basis for screening abnormal learners or abnormal test sets. The CS and CP are combined for analysis to discriminate the stability and the quality of the question set of the learner; on the basis, the stability of the learner and the distinguishing force of the test question are judged according to the CS and CP values.
Discrimination of learner stability
According to the result of the S-P table analysis, the learner 'S attention coefficient is taken as the horizontal axis, the learner' S answer rate (the learner score is the percentage of the total number of questions) is taken as the vertical axis, and the graph is drawn as shown in FIG. 3, which can be used as the type of learning diagnosis for the learner, and according to the application, the learner type is described as follows:
1. stable learning: the learner characteristics fall into the area A, which shows that the learning condition of the learner is very good and stable, the learning achievement of the learners is higher, and the test performance is stable and normal.
2. Type of carelessness: the characteristics of the learner fall into the B area, which indicates that the learning condition of the learner is slightly unstable and has good ability, but the attention is not concentrated too much, and careless answering habits and impulsions exist.
3. Effort-deficient: the learner's characteristics fall into zone C, and the learner's learning status is good, stable, and the effort is insufficient, and needs more effort. The ability is medium.
4. Lack of adequate typing: the characteristics of the learner fall into the D area, the learner has insufficient learning preparation, carelessness and mistakes, and the learning becomes unstable and insufficient in effort.
5. Capacity-deficient type: the learner's features fall into zone E, and the learner has insufficient basic ability, insufficient learning, insufficient effort, and low learning success.
6. Learning abnormality type: the characteristics of the learner fall into the F area, the learner is extremely unstable in learning, has a random reading habit, does not fully prepare examination contents, and the examination score is good or bad. The response group is peculiar (may cheat, blindly guess questions or answer at will).
Test question set quality discrimination
According to the results of the S-P table analysis, the attention coefficient of the test question set is taken as the horizontal axis, the answer rate (the percentage of the number of learners in the test question set in answer pairs to the total number of learners) is taken as the vertical axis, and the graph is drawn as shown in FIG. 4, which can be used as the diagnosis question type, and the question type is explained as follows according to the application mood:
1. excellent type test subject set: the test question set falls into zone a, indicating that the test question set is reasonably appropriate and can be used to distinguish low-achievement learners from other learners.
2. Heterogeneous test problem set: the test question set falls in zone b, indicating that the test question set may contain heterogeneous components, require local correction, or contain poor options.
3. Difficult type test problem set: the test question set falls into the area c, which indicates that the test question set is difficult to use and is suitable for being used as a good test question set for distinguishing high-achievements.
4. Poor test question set: the test question set falls in the d-zone, indicating that the test question set is extremely poor, contains quite heterogeneous components, may have data misregistration or ambiguous, and must be modified.
In practice, the test question set diagnosis graph can provide references for the person making the self-diagnosis test question set, and when the test question set is judged to be excellent, comprehensive judgment needs to be carried out by combining indexes such as difficulty, discrimination, option response and the like.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (5)
1. A learner stability and question set quality screening method based on an S-P table is characterized by comprising the following steps:
s1, obtaining the scoring results of N test question sets of N trainees of a certain subject to obtain an Nxn S-P original table; the S-P original table is used for scoring N test subject sets corresponding to a learner, and the columns of the S-P original table are the scores of N learners corresponding to a test subject set;
s2, arranging the S-P original tables obtained in the step S1 from top to bottom according to the total score of the n test question sets of the learner from high to low;
s3, sequencing the number of answer pairs of the S-P original table processed in the step S2 from left to right according to the test question set;
s4, counting the number of test question sets from left to right, which is the same as the total number of the test question set answer pairs corresponding to the learner, of the S-P original table processed in the step S3, drawing a boundary on the right side of the test question set of the last answer pair, and connecting two adjacent lines of boundaries by straight lines to form a first step-shaped curve which is marked as an S curve;
counting the number of learners which is the same as the number of answers of each test question set from top to bottom according to the number of answers of each test question set in the S-P original table processed in the step S3, drawing a boundary line below the last answer pair learner, and connecting two adjacent lines of boundary lines by using a straight line to form a second step-shaped curve which is marked as a P curve;
s5, respectively calculating the learner attention coefficient and the test question set attention coefficient according to the difference ratio of the abnormal group and the perfect group;
s6, obtaining the discrimination result of the stability of the learner and the quality of the test question set according to the attention coefficient of the learner and the attention coefficient of the test question set.
2. The method for discriminating the stability and the problem set quality of a learner based on an S-P table as claimed in claim 1, wherein the learner attention coefficient CS of step S5 is calculated as:
wherein cs isiIndicating the corresponding attention coefficient, y, of the learner iijShows the reaction condition of learner i on the jth question, yjIndicates the number of right-handed people of the test question j, yiThe total score of the learner is shown, and u' represents the average number of right-answer test questions. y isijThe data corresponding to the ith row and the jth column in the SP table.
3. The method for discriminating the stability of a learner from the quality of a question set based on an S-P table as claimed in claim 2, wherein the test question attention coefficient CP is calculated by the formula:
wherein cpjThe attention coefficient of the test question corresponding to the jth question is shown,yijshows the reaction condition of learner i on the jth question, yiRepresents the total score y of learner ijThe number of the answer pairs of the test question j is shown, and the average score of the learner is shown in u.
4. The method as claimed in claim 3, wherein the step S6 is to discriminate the learner' S stability and question set quality by following sub-steps:
a1, taking the learner's attention coefficient as the horizontal axis and taking the answer rate of n test question sets corresponding to the learner as the vertical axis;
a2, dividing the horizontal axis into Z1 parts according to the attention coefficient of the learner, and dividing the vertical axis into Z2 parts according to the answer pair rate of n test question sets corresponding to the learner; a total of Z1 × Z2 zones were obtained;
and A3, outputting the learner distribution in Z1X Z2 areas as the learner stability screening result.
5. The method of claim 4, wherein the step S6 is performed by the following steps:
b1, taking the attention coefficient of the test question set as the horizontal axis and taking the answer-to-person number percentage corresponding to a single test question set as the vertical axis;
b2, dividing the horizontal axis into Z3 parts according to the attention coefficient of the test question set, and dividing the vertical axis into Z4 parts according to the answer-to-person number percentage corresponding to a single test question set; a total of Z3 × Z4 zones were obtained;
b3, outputting the distribution of the test question set in Z3X Z4 areas as the quality screening result of the test question set.
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Application publication date: 20200526 |