JP6152713B2 - Information processing method, apparatus and program - Google Patents

Description

  The present invention relates to a technique for correcting a class satisfaction survey.

  For example, as part of FD (Faculty Development), universities conduct student satisfaction surveys on the last day of classes. The results of the lesson satisfaction survey can be used to objectively grasp the situation of universities and classes, and can be used to stimulate students' motivation to learn and improve teaching methods for teachers.

  However, some of the students who responded seemed unscrupulous and rarely participated in the class, and included students who could give an emotionally bad evaluation, so the reliability of the survey results is low . Because of this situation, the teachers are complaining that they do not want to evaluate the class based on the students' opinions alone. Since the results of this lesson satisfaction survey also lead to teacher evaluation, it is very important for teachers and it is preferable to improve the reliability of the lesson satisfaction survey.

  On the other hand, since it is not possible to conclude that a student's response that is not serious is 100% disqualified, it cannot be completely excluded from the survey results.

  Although there is a conventional technique for correcting a questionnaire result, it cannot be applied to a class satisfaction survey in a school.

JP 2005-352620 A JP 2008-204044 A

  Therefore, according to one aspect of the present invention, there is provided a technique for correcting a lesson satisfaction survey so that teachers can easily accept it.

  In the information processing method of the present invention, (A) a weight value of each student is determined from an index value relating to learning behavior for each student stored in the first data storage unit, and (B) stored in the second data storage unit. And a class satisfaction survey result from each student related to class evaluation is corrected based on the weight value calculated for the student, and (C) a process of outputting the corrected class satisfaction survey result is included.

  According to one aspect, the lesson satisfaction survey can be corrected to make it easier for teachers to accept.

FIG. 1 is a functional block diagram of the information processing apparatus according to the present embodiment. FIG. 2 is a diagram showing a main processing flow according to the present embodiment. FIG. 3 is a diagram illustrating an example in which learning action points are calculated for one evaluation item. FIG. 4 is a diagram illustrating an example in which learning action points are calculated for one evaluation item. FIG. 5 is a diagram illustrating an example of calculating total learning action points. FIG. 6 is a diagram illustrating a processing flow of weight value calculation processing. FIG. 7 is a diagram illustrating an example of a weight value calculation result. FIG. 8 is a diagram illustrating an example of a weight value calculation result. FIG. 9 is a diagram illustrating an example of a weight value calculation result. FIG. 10 is a diagram illustrating an example of the content of a lesson satisfaction survey. FIG. 11 is a diagram illustrating a processing flow of a lesson satisfaction level survey result correction process. FIG. 12 is a diagram illustrating a processing flow of a lesson satisfaction survey result correction process. FIG. 13 is a diagram illustrating a display example of the processing result. FIG. 14 is a functional block diagram of a computer.

  An information processing apparatus 100 according to the present embodiment is shown in FIG. The information processing apparatus 100 includes a preprocessing unit 110, a weight calculation unit 121, a weight data storage unit 122, a survey result storage unit 123, a correction unit 124, a corrected survey result storage unit 125, and an output processing unit 126. And an output data storage unit 127.

  The preprocessing unit 110 includes a learning behavior log data storage unit 111, a point calculation unit 112, and a point storage unit 113.

  The learning behavior log data storage unit 111 manages learning materials (materials, reports, questionnaires, tests, discussions, attendance, Q & A, bulletin boards, etc.) electronically for each lecture, and distributes teaching materials and submits assignments. Stores historical data relating to learning activities occurring in the learning support system. More specifically, presentation, disclosure, reference, attendance at class, posting to bulletin board, browsing bulletin board, following bulletin board, posting discussion, viewing discussion, rating in discussion, posting Q & A, Q & A Data such as browsing, Q & A responses, FAQ (Frequently Asked Question) browsing, report material reference, report material evaluation, report submission, test material reference, test material evaluation, test, questionnaire response, etc. are stored The

  The point calculation unit 112 calculates the learning action points of each student from the log data stored in the learning action log data storage unit 111 and stores the learning action points in the point storage unit 113.

  The preprocessing unit 110 may be included in the learning support system, and in this case, the information processing apparatus 100 stores data stored in the data storage unit corresponding to the point storage unit 113 from the learning support system. Is acquired through a network or the like, for example, and the processing described below is performed. The information processing apparatus 100 may be integrated with the learning support system.

  The weight calculation unit 121 calculates the weight value of each student from the learning action points of each student stored in the point storage unit 113 and stores it in the weight data storage unit 122.

  The survey result storage unit 123 stores the result of the class satisfaction survey. The result of the lesson satisfaction survey may be acquired separately and stored in the survey result storage unit 123, or may be acquired from the learning support system.

  The correction unit 124 performs processing for correcting the result of the lesson satisfaction survey based on the data stored in the weight data storage unit 122, and stores the corrected result in the corrected survey result storage unit 125.

  The output processing unit 126 performs predetermined statistical processing or the like on the data stored in the corrected survey result storage unit 125 to generate output data and store the output data in the output data storage unit 127 or a display device. To an output device such as a computer or other computer.

  Next, processing contents of the information processing apparatus 100 will be described with reference to FIGS.

  First, the point calculation unit 112 of the information processing apparatus 100 calculates the learning action points of each student from the learning action log data stored in the learning action log data storage unit 111 and stores it in the point storage unit 113 (see FIG. 2: Step S1).

  In the present embodiment, the appropriateness of student learning behavior is evaluated in three directions: aggressiveness, planning, and continuity.

  Aggressiveness expresses activity and evaluates whether or not you can act more. The planability is to evaluate whether or not the preferred learning behavior is executed early. Continuity is an evaluation of whether or not learning behavior is performed repeatedly and uniformly.

  As mentioned above, pre-assessment evaluation items related to the learning behavior that is performed during preparatory training (ie preparation before class), during class, and during review (including responding to issues such as report submission) Evaluate after classifying into planability and continuity. Specifically, from log data output by materials, reports, questionnaires, discussions, tests and other teaching materials related functions, bulletin board functions, attendance management functions, Q & A functions, etc., log data related to the evaluation items for each evaluation item And learning action points of the evaluation item are assigned according to a predetermined evaluation criterion.

More specifically, the evaluation items included in the aggressiveness include the following.
(1) The rate of access to the teaching materials published before the class before the class (2) The follow-up rate of the bulletin board thread (3) The amount of submissions to the discussion materials (4) The rate of submission of the questionnaire Items are often evaluated by frequency and rate.

In addition, there are the following evaluation items related to planning.
(1) Period until report submission and other submissions or period until deadline As described above, for evaluation items related to planning, the period until it becomes available after use, the period until deadline, etc. There are many cases to evaluate with.

Furthermore, the evaluation items regarding continuity include the following.
(1) Class attendance rate (2) Percentage of class courses attended without dropping out in the latter half of the class (for example, from the 8th to the 15th) (3) Document materials released before class In this way, evaluation items related to continuity are often evaluated by rate and value dispersion.

  For example, a method of calculating learning action points for an evaluation item called a class attendance rate will be described.

  For example, Fig. 3 shows the status of attendance (○ or ×) up to the 4th class, for example, 1 point for students with 80% or more attendance and 0 for students with less than 80% attendance. Points are given.

  Furthermore, a method of calculating learning action points for an evaluation item called the amount of contribution to the discussion material will be described.

  For example, FIG. 4 shows the posting amount for themes 1 to 4 for each student. For example, the total number of postings is about 30% for the top 30% students (for example, F and G) and about 30% for the bottom. Students (eg D and E) are awarded 0 points and others are 0.5 points.

  In this way, the students are evaluated relative to each evaluation item here, but may be absolute evaluated based on the criteria.

  And a final learning action point is obtained by totaling such learning action points for every evaluation item. For example, as shown in FIG. 5, for each student, the total of the learning action points for positiveness, the total of learning action points for planning, and the total of learning action points for continuity is further calculated. . The calculation result as shown in FIG. 5 is stored in the point storage unit 113.

  Next, the weight calculation unit 121 executes a weight value calculation process (step S3). The weight value calculation process will be described with reference to FIGS.

  First, the weight calculation unit 121 determines whether or not to set a weight value proportional to the learning action point based on the setting (FIG. 6: step S11). In the present embodiment, either learning action point proportion or learning action point ranking based on ranking of learning action points is adopted.

  When setting a weight value proportional to the learning action point, the weight calculation unit 121 calculates a value obtained by multiplying the learning action point of each student by the coefficient k included in the setting as a weight value, and the weight data storage unit 122. (Step S13). Then, the process returns to the caller process.

  For example, when learning action points as shown in FIG. 5 are obtained and k = 0.1, a weight value as shown in FIG. 7 is obtained. In the example of FIG. 5, the upper limit of the learning action points is 30 points, so if k = 0.1, the upper limit of the weight value is 3.0. When such a weight value is too large, k may be set to a smaller value. For example, when k = 0.05, a weight value as shown in FIG. 8 is obtained. The weight value may be adjusted so that a value smaller than a predetermined minimum value (for example, 0.1) is not set. Similarly, an upper limit value may be set.

  On the other hand, when fixed weight value assignment based on ranking is set, the weight calculation unit 121 sorts the students by learning action points in descending order (step S15). In the example of FIG. 5, the already sorted state is shown.

  The weight calculation unit 121 identifies one unprocessed student (step S17). For example, in the list as shown in FIG. Then, the weight calculation unit 121 determines whether the identified student is within the top x% (step S19). x is a predetermined value, for example, x = 20. If it is in the top x%, the weight calculator 121 sets the identified student's weight value to 1.5 (step S21). Then, the process proceeds to step S29.

  On the other hand, if the identified student is not in the upper x%, the weight calculation unit 121 determines whether it is in the lower y% (step S23). y is a predetermined value, for example, y = 40. If it is not in the lower y%, that is, if it is determined as an intermediate layer, the weight calculator 121 sets the identified student's weight value to 1.0 (step S27). Then, the process proceeds to step S29. On the other hand, when the identified student is in the lower y%, the weight calculation unit 121 sets the identified student's weight value to 0.5 (step S25). Then, the process proceeds to step S29.

  Then, the weight calculation unit 121 determines whether there is an unprocessed student (step S29). If there is an unprocessed student, the process returns to step S17. On the other hand, if there is no unprocessed student, the process returns to the caller process.

  For example, when steps S15 to S29 are executed, data as shown in FIG. 9 is obtained. In the example of FIG. 9, a weight value of 1.5 is set for four students in the top 20%, a weight value of 0.5 is set for eight students in the bottom 40%, and a weight value is set for eight intermediate students. 1.0 is set.

  In this way, the weight according to the learning action point is set.

  Returning to the description of the processing in FIG. 2, the correction unit 124 then executes a lesson satisfaction level survey result correction process (step S <b> 5). The lesson satisfaction survey result correction process will be described with reference to FIGS.

  In the present embodiment, the class satisfaction survey is performed by selecting one of the stages for each survey item as shown in FIG. This example shows an example in which satisfaction is higher as the value is larger in five stages. The five steps are merely examples, and other numbers of steps may be used. However, in the process described below, the description will be made on the assumption that the larger the value, the higher the satisfaction level.

  First, the correction unit 124 identifies one unprocessed survey item among the class satisfaction surveys stored in the survey result storage unit 123 (FIG. 11: step S31). The correcting unit 124 identifies one unprocessed student in the list of weight values stored in the weight data storage unit 122 (step S33).

  Then, the correction unit 124 determines whether the processing setting is negative side enhancement (step S35). In the present embodiment, it is assumed that a case where there is negative side emphasis for emphasizing a negative answer and a case where there is no negative side emphasis for emphasizing only a positive answer are set. If negative side enhancement is present, the process proceeds to the process of FIG.

  On the other hand, when there is no negative side emphasis, the correction unit 124 determines whether or not the identified student's answer is below a predetermined level for the identified survey item (step S37). The upper limit stage (eg, “3”) for negative answers is set in advance. If the answer is below a predetermined level, the correction unit 124 stores the answer in the post-correction survey result storage unit 125 without correction (step S40). Then, the process proceeds to step S41.

  On the other hand, when the answer exceeds the predetermined level, the correction unit 124 corrects the answer by multiplying the identified student weight value, and stores it in the corrected survey result storage unit 125 (step S39). . Then, the process proceeds to step S41. For example, if the answer is “4” and the weight value is “1.5”, the corrected answer is “6”.

  Thereafter, the correction unit 124 determines whether there is an unprocessed student (step S41). If there is an unprocessed student, the process returns to step S33. On the other hand, if there is no unprocessed student, it is determined whether there is an unprocessed survey item (step S45). If there is an unprocessed survey item, the process returns to step S31. On the other hand, if there is no unprocessed survey item, the process returns to the caller process.

  By performing such processing, a positive answer can be corrected by a weight value set according to the learning action point. That is, positive answers are appropriately weighted according to the student's learning behavior.

  Next, processing after the terminal A will be described with reference to FIG. The correcting unit 124 determines whether the identified student's answer corresponds to the predetermined lower level for the identified survey item (step S47). For example, in the case of five stages, lower stages are determined in advance such as “1” and “2”. When the answer corresponds to a predetermined lower level, the correction unit 124 corrects the answer by multiplying the reciprocal of the identified student weight value, and stores the correction result in the corrected survey result storage unit 125 ( Step S49). Thus, by multiplying the reciprocal of the weight value, the negative answer is weighted by the reciprocal of the weight value. For example, if the weight value of the identified student is “2” and the answer is “1”, the correction result is “0.5”, and the negative answer is more emphasized. On the other hand, if the weight value of the identified student is “0.5” and the answer is “1”, the correction result is “2”, so the student with a small weight value (ie, a student with a low learning action point). ) Negative responses will be relaxed. If the weight value is “0”, a predetermined minimum value is used. Then, the process returns to the process of FIG.

  On the other hand, when the answer does not correspond to the predetermined lower level, the correction unit 124 determines whether the answer corresponds to the predetermined upper level (step S51). For example, in the case of five stages, upper stages are determined in advance such as “4” and “5”. When the answer corresponds to a predetermined upper level, the correction unit 124 corrects the answer by multiplying the identified student weight value, and stores the correction result in the corrected survey result storage unit 125 (step S53). ). For example, if the weight value of the identified student is “2” and the answer is “4”, the correction result is “8”, and the positive answer is more emphasized. On the other hand, if the weight value of the identified student is “0.5” and the answer is “4”, the correction result is “2”, so that a positive answer by a student with a small weight is deducted. Then, the process returns to the process of FIG.

  On the other hand, when the answer does not correspond to the predetermined upper level, the correction unit 124 stores the answer in the corrected survey result storage unit 125 without correcting the answer (step S55). For example, if it is “3” in 5 stages, it is processed in step S55. Note that a predetermined upper level or a predetermined lower level may be set so as not to perform this process. Thereafter, the processing returns to the processing in FIG.

  In this way, the answers can be weighted according to the learning action points. A positive answer is emphasized or relaxed according to a weight value corresponding to the learning action point. For negative answers, weighting with the reciprocal of the weight value can emphasize negative answers by students with large learning action points, while relaxing negative answers by students with small learning action points. become.

  Returning to the description of the processing in FIG. 2, the output processing unit 126 executes statistical processing of the survey result after correction, and stores the processing result in the output data storage unit 127 (step S <b> 7). For example, the average value of answers is calculated for each survey item.

  Then, the output processing unit 126 outputs the processing result to an output device (including other computers connected to the network) (step S9).

  For example, data as shown in FIG. 13 is output. In the example of FIG. 13, for each survey item (Q1-Q6), a table including the number of answers of answers 1-5, the total number of answers (total), and the corrected average value calculated in step S7; The actual average value (actual value: solid line) and the radar chart for the corrected average value (dotted line) are included. In this example, a good value is obtained by weighting based on learning behavior by correction.

  In this way, students who have many learning action points (that is, students who are seriously engaged in classes every time) seem to understand the good or bad of the classes well, so the weight of the students' answers is increased. On the other hand, students who have few learning action points (students who passively take less lessons to participate in the lesson) seem to have not grasped the situation of the lesson properly, so the student's voting weight is reduced. As a result, the results of a lesson satisfaction survey can be obtained that is satisfactory to teachers.

  Although the embodiment of the present invention has been described above, the present invention is not limited to this embodiment. For example, the functional block diagram of FIG. 1 is an example, and may not match the program module configuration. As for the processing flow, as long as the processing result does not change, the order of the steps may be changed, or a plurality of steps may be executed in parallel.

  Furthermore, in the example described above, the example in which the learning action points of each evaluation item defined for aggressiveness, continuity, and planability are totaled and used for calculating the weight value has been described. For example, (A) aggressiveness The weight value may be calculated from the sum of the evaluation items related to. The evaluation item regarding the positiveness represents the degree of participation in the class, and it is estimated that the student who has been given a large value of the learning action point with respect to the evaluation item regarding the positiveness observes the class well. Therefore, a weight value that is reasonable to some extent is calculated even if the weight value is calculated using only the sum of the evaluation items related to aggressiveness.

Further, for example, (B) the weight value may be calculated by the total of learning action points for evaluation items related to attendance, materials, reports, and the like. For example, the total may be calculated by limiting to the following evaluation items.
(1) Percentage of access to material materials published before class before class (2) Percentage of class courses attended without dropping out in the latter half of the class (for example, 8th to 15th) (3) Period until report work or other submissions or period until deadline

  This is because learning behavior regarding attendance, materials, and reports is an easy-to-understand evaluation item from the teacher side as a student's learning attitude, and even if weighting is performed from this viewpoint, the teacher's satisfaction can be maintained.

  In the above, the weight value is calculated by the learning action point and the answer of the class satisfaction survey is corrected by the weight value. However, the lecture is for a class with a large number of students, a lecture using a lot of teaching materials, and a student. If you look at the comment rate to the report and the response rate in the Q & A, you can understand the teacher's popularity and motivation for the class.

  Furthermore, the information processing apparatus 100 may be configured by a plurality of computers instead of a single computer. Further, it may be implemented by a client server type system or a standalone type system.

  The information processing apparatus 100 described above is a computer apparatus, and as shown in FIG. 14, a memory 2501, a CPU (Central Processing Unit) 2503, a hard disk drive (HDD: Hard Disk Drive) 2505, and a display device. A display control unit 2507 connected to 2509, a drive device 2513 for the removable disk 2511, an input device 2515, and a communication control unit 2517 for connecting to a network are connected by a bus 2519. An operating system (OS) and an application program for executing the processing in this embodiment are stored in the HDD 2505, and are read from the HDD 2505 to the memory 2501 when executed by the CPU 2503. The CPU 2503 controls the display control unit 2507, the communication control unit 2517, and the drive device 2513 according to the processing content of the application program, and performs a predetermined operation. Further, data in the middle of processing is mainly stored in the memory 2501, but may be stored in the HDD 2505. In an embodiment of the present technology, an application program for performing the above-described processing is stored in a computer-readable removable disk 2511 and distributed, and installed from the drive device 2513 to the HDD 2505. In some cases, the HDD 2505 may be installed via a network such as the Internet and the communication control unit 2517. Such a computer apparatus realizes various functions as described above by organically cooperating hardware such as the CPU 2503 and the memory 2501 described above and programs such as the OS and application programs. .

  The above-described embodiment can be summarized as follows.

  In the information processing method according to the present embodiment, (A) the weight value of each student is determined from the index value related to the learning behavior for each student stored in the first data storage unit, and (B) the second data is stored. The process of correcting the class satisfaction survey results from each student related to the evaluation of the class stored in the department based on the weight value calculated for the student, and (C) the process of outputting the corrected class satisfaction survey results Including. In this way, the class satisfaction survey results can be corrected so that teachers can easily accept them.

  Note that the index value related to the learning behavior described above may be an index value calculated from the degree of aggressiveness with respect to learning, the degree of planning of learning, and the degree of continuity of learning. Comprehension of teachers can be increased by calculating weight values after comprehensively evaluating learning attitudes.

  Further, the index value related to the learning behavior described above may be an index value based on the degree of aggressiveness with respect to learning. Highly active students are more likely to understand the substance of the lesson well, and less active students are more likely not to understand the substance of the lesson. The teacher is also convinced.

  In addition, the index value related to learning behavior described above is the index value calculated from the evaluation values of the evaluation items related to attendance at class, preparation before class, and submission after class among a plurality of evaluation items related to learning behavior Can be. Regarding such evaluation items, it is preferable to pay attention to understanding the substance of the lesson, which is effective for weighting.

  Further, the correction process may include a process of multiplying an evaluation exceeding a predetermined value included in the lesson satisfaction survey result by the weight value. In addition, the correction process is performed by multiplying an evaluation greater than or equal to the first predetermined value included in the satisfaction survey result by the weight value, and an evaluation equal to or less than the second predetermined value included in the class satisfaction survey result. May include a process of multiplying the reciprocal of the above weight value. The weight value may or may not be used to emphasize negative answers.

  Furthermore, the process of determining the weight value described above includes a process of sorting the students in descending order of the index value, classifying the students according to the result of sorting, and assigning a weight value set in advance according to the classification. Also good. On the other hand, the processing for determining the weight value described above may include processing for calculating the weight value by multiplying the index value by a predetermined coefficient. Various weight value calculation methods are possible.

  A program for causing a computer to execute the processing described above can be created, and the program is, for example, a flexible disk, an optical disk such as a CD-ROM, a magneto-optical disk, or a semiconductor memory (for example, ROM). Or a computer-readable storage medium such as a hard disk or a storage device. Note that data being processed is temporarily stored in a storage device such as a RAM.

  The following supplementary notes are further disclosed with respect to the embodiments including the above examples.

(Appendix 1)
A weight value for each student is determined from an index value stored in the first data storage unit and related to learning behavior for each student;
Correcting the class satisfaction survey results from each student stored in the second data storage unit and regarding the class evaluation based on the weight value calculated for the student,
A program that allows a computer to execute the process of outputting the revised class satisfaction survey results.

(Appendix 2)
The program according to claim 1, wherein the index value related to the learning behavior is an index value calculated from a degree of aggressiveness with respect to learning, a degree of planning of learning, and a degree of continuity of learning.

(Appendix 3)
The program according to claim 1, wherein the index value related to the learning behavior is an index value based on a degree of aggressiveness toward learning.

(Appendix 4)
An index value related to the learning behavior is
The program according to supplementary note 1, which is an index value calculated from evaluation values of evaluation items related to attendance at a class, preparation before class and submission after class among a plurality of evaluation items regarding learning behavior.

(Appendix 5)
The correction process is
The program according to any one of appendices 1 to 4, including a process of multiplying an evaluation exceeding a predetermined value included in the lesson satisfaction survey result by the weight value.

(Appendix 6)
The correction process is
Multiplying the weight value for the evaluation above the first predetermined value included in the lesson satisfaction survey result,
The program according to any one of appendices 1 to 4, including a process of multiplying an evaluation less than or equal to a second predetermined value included in the lesson satisfaction result by a reciprocal of the weight value.

(Appendix 7)
The process of determining the weight value includes:
Sort the students in descending order of the index value,
The program according to any one of appendices 1 to 6, including a process of classifying the students according to the result of the sorting and assigning a weight value set in advance according to the classification.

(Appendix 8)
The process of determining the weight value includes:
The program according to any one of appendices 1 to 6, including a process of calculating a weight value by multiplying the index value by a predetermined coefficient.

(Appendix 9)
A weight value for each student is determined from an index value stored in the first data storage unit and related to learning behavior for each student;
Correcting the class satisfaction survey results from each student stored in the second data storage unit and regarding the class evaluation based on the weight value calculated for the student,
An information processing method executed by a computer, including a process of outputting a corrected class satisfaction survey result.

(Appendix 10)
A determination unit for determining a weight value of each student from an index value stored in the first data storage unit and relating to learning behavior for each student;
A correction unit that is stored in the second data storage unit and corrects the lesson satisfaction survey result from each student related to the evaluation of the lesson based on the weight value calculated for the student;
An output unit that outputs the revised class satisfaction survey results;
An information processing apparatus.

DESCRIPTION OF SYMBOLS 100 Information processing apparatus 110 Preprocessing part 121 Weight calculation part 122 Weight data storage part 123 Investigation result storage part 124 Correction part 125 Correction | amendment investigation result storage part 126 Output processing part 127 Output data storage part

Claims (14)

A weight value for each student is determined from an index value stored in the first data storage unit and related to learning behavior for each student;
Correcting the class satisfaction survey results from each student stored in the second data storage unit and regarding the class evaluation based on the weight value calculated for the student,
A program for causing a computer to execute a process of outputting a corrected class satisfaction survey result ,
The process of determining the weight value includes:
Each student is assigned a weight value for a category corresponding to the index value related to the student's learning behavior.
A program that includes processing . The program according to claim 1, wherein the index value related to the learning behavior is an index value calculated from a degree of aggressiveness with respect to learning, a degree of planning of learning, and a degree of continuity of learning. The program according to claim 1, wherein the index value related to the learning behavior is an index value based on a degree of aggressiveness toward learning. An index value related to the learning behavior is
The program according to claim 1, wherein the program is an index value calculated from evaluation values of evaluation items regarding attendance at a class, preparation before class, and submission after class among a plurality of evaluation items regarding learning behavior. The correction process is
The program of any one of Claims 1 thru | or 4 including the process which multiplies the said weight value with respect to the evaluation exceeding predetermined value contained in the said lesson satisfaction investigation result. The correction process is
Multiplying the weight value for the evaluation above the first predetermined value included in the lesson satisfaction survey result,
The program according to any one of claims 1 to 4, further comprising a process of multiplying an evaluation less than or equal to a second predetermined value included in the lesson satisfaction survey result by an inverse of the weight value. A weight value for each student is determined from an index value stored in the first data storage unit and related to learning behavior for each student;
Correcting the class satisfaction survey results from each student stored in the second data storage unit and regarding the class evaluation based on the weight value calculated for the student,
An information processing method executed by a computer, including a process of outputting a corrected class satisfaction survey result ,
The process of determining the weight value includes:
Each student is assigned a weight value for a category corresponding to the index value related to the student's learning behavior.
Information processing method including processing . A determination unit for determining a weight value of each student from an index value stored in the first data storage unit and relating to learning behavior for each student;
A correction unit that is stored in the second data storage unit and corrects the lesson satisfaction survey result from each student related to the evaluation of the lesson based on the weight value calculated for the student;
An output unit that outputs the revised class satisfaction survey results;
The information processing apparatus having,
The determination unit is
Each student is assigned a weight value for a category corresponding to the index value related to the student's learning behavior.
Information processing device . A weight value for each student is determined from an index value stored in the first data storage unit and related to learning behavior for each student;
Correcting the class satisfaction survey results from each student stored in the second data storage unit and regarding the class evaluation based on the weight value calculated for the student,
A program for causing a computer to execute a process of outputting a corrected class satisfaction survey result ,
The correction process is
Multiply the weight value for evaluations that exceed the predetermined value included in the lesson satisfaction survey results.
A program that includes processing . A weight value for each student is determined from an index value stored in the first data storage unit and related to learning behavior for each student;
Correcting the class satisfaction survey results from each student stored in the second data storage unit and regarding the class evaluation based on the weight value calculated for the student,
An information processing method executed by a computer, including a process of outputting a corrected class satisfaction survey result ,
The correction process is
Multiply the weight value for evaluations that exceed the predetermined value included in the lesson satisfaction survey results.
Information processing method including processing . A determination unit for determining a weight value of each student from an index value stored in the first data storage unit and relating to learning behavior for each student;
A correction unit that is stored in the second data storage unit and corrects the lesson satisfaction survey result from each student related to the evaluation of the lesson based on the weight value calculated for the student;
An output unit that outputs the revised class satisfaction survey results;
The information processing apparatus having,
The correction unit is
Multiply the weight value for evaluations that exceed the predetermined value included in the lesson satisfaction survey results.
Information processing device . A weight value for each student is determined from an index value stored in the first data storage unit and related to learning behavior for each student;
Correcting the class satisfaction survey results from each student stored in the second data storage unit and regarding the class evaluation based on the weight value calculated for the student,
A program for causing a computer to execute a process of outputting a corrected class satisfaction survey result ,
The correction process is
Multiplying the weight value for the evaluation above the first predetermined value included in the lesson satisfaction survey result,
Multiply the reciprocal of the weight value for the evaluation below the second predetermined value included in the lesson satisfaction survey result.
A program that includes processing . A weight value for each student is determined from an index value stored in the first data storage unit and related to learning behavior for each student;
Correcting the class satisfaction survey results from each student stored in the second data storage unit and regarding the class evaluation based on the weight value calculated for the student,
An information processing method executed by a computer, including a process of outputting a corrected class satisfaction survey result ,
The correction process is
Multiplying the weight value for the evaluation above the first predetermined value included in the lesson satisfaction survey result,
Multiply the reciprocal of the weight value for the evaluation below the second predetermined value included in the lesson satisfaction survey result.
Information processing method including processing . A determination unit for determining a weight value of each student from an index value stored in the first data storage unit and relating to learning behavior for each student;
A correction unit that is stored in the second data storage unit and corrects the lesson satisfaction survey result from each student related to the evaluation of the lesson based on the weight value calculated for the student;
An output unit that outputs the revised class satisfaction survey results;
The information processing apparatus having,
The correction unit is
Multiplying the weight value for the evaluation above the first predetermined value included in the lesson satisfaction survey result,
Multiply the reciprocal of the weight value for the evaluation below the second predetermined value included in the lesson satisfaction survey result.
Information processing device .

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