JP2006065448A - Effective extraction method and program for motivation influence factor of student for lesson - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a method and program for effectively and efficiently extracting the motivation influence factors for lessons of students. <P>SOLUTION: This motivation influence factor extraction program of students for lessons is provided to perform student questionnaire survay concerning motivations for lessons and item-based demands for lessons, and to calculate the correlation of the motivations and demand items, and to perform multiple regression analysis by using objective variables as motivations and explanatory variables as demand items whose correlation with motivations is high, and to effectively and efficiently extract the demand items affecting the level of motivations. This motivation influence factor extraction program comprises a first process for performing student questionnaire survay concerning motivations for the lessons and the item-based demands for the lessons and a second process for calculating the correlation of motivations and demand items, and for narrowing down the demand items whose correlation with the motivations is high and a third process for performing multiple regression analysis by using the objective variables as motivations and the explanatory variables as demand items whose correlation is high. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to a method and a program for effectively extracting a student's motivation influencing factor for a class.

  In Patent Document 1, a questionnaire survey is conducted for the training participants after the training is completed, and the needs of the training participants are extracted as factors or principal components using factor analysis or principal component analysis for each question item. Educational training evaluation has been proposed to help improve the content of educational training by calculating correlation coefficients between factors and determining learning items that can be separated or integrated in educational training.

  Patent Document 2 diagnoses the subject's “motivation”, “motivation factor”, “ability”, “appropriate occupation”, and the like, and by looking at the motivation improvement diagnostic sheet on which the diagnosis result is described, Knowing the strengths and how to extend the strengths, the mental brakes that are preventing the strengths from being demonstrated, knowing how to resolve them, and knowing the occupations that take advantage of the strengths, improve the motivation of the subjects, Methods have been proposed for diagnosing the most common environments affecting motivation and obtaining personal guidance on strengths and motivation improvements.

  Non-Patent Document 1 surveys employees, measures employee motivation, and includes 11 items such as business execution, self-expression, human relations, suitable jobs, expectation / evaluation, private, compensation, etc. as factors affecting motivation. A method to improve employee motivation has been proposed by conducting a survey on awareness, finding out factors that hinder employee motivation, interviewing employees, and so on.

JP 2002-49303 A JP 2003-242307 A JTB Motivation Research & Development Team "Science Motivation" Kawade Shobo Shinsha 1998

  However, according to the above prior art, the motivation-influencing factors for students' classes are different for each class in the educational field, depending on the type of class, such as classroom learning, practical training, the teacher's qualities, the student's temperament, and the target grade. It does not propose a method to extract the motivation influencing factors of students effectively. Moreover, although the method of extracting and narrowing down the needs of educational trainees has been implemented, these narrowed needs are not necessarily factors that effectively stimulate the motivation of the trainees.

  Therefore, an object of the present invention is to provide a method and program for effectively and efficiently extracting motivation-influencing factors for student classes.

  In order to solve the above problems, the first invention performs a questionnaire survey on student motivation for the class and the degree of requirement for each class for the class, calculates the correlation between motivation and requirement items, and uses principal component analysis, etc. By narrowing down requirements that are highly correlated with motivation, and by performing multiple regression analysis with objective variables as motivation and explanatory variables as highly correlated requirements, requirements that affect student motivation for classes are effectively and efficiently It is a method for extracting motivation influencing factors of students for lessons characterized by efficient extraction.

  The second invention is a first process of conducting a questionnaire survey on students regarding motivation for a class and the degree of requirement for each class, and calculating the correlation between motivation and required items and correlating with motivation using principal component analysis etc. Students' motivation impact on lessons characterized by a second process that narrows down high requirement items, a third process that performs multiple regression analysis with objective variables motivated and explanatory variables as highly correlated requirement items This is a factor extraction program.

  According to the present invention, it is possible to limit effective factors (required items) that can stimulate student motivation for classes, and by conducting detailed interviews with students focusing on these factors (required items), The motivation of the whole class can be improved and the proficiency level can be improved.

Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings.
FIG. 1 shows a flowchart regarding an example of extracting influence factors of student motivation for machine work training. In FIG. 1, 1 is a questionnaire item of student motivation for machine work training, FIG. 2 is a questionnaire item of item-specific request level for machine work training, 3 is a correlation coefficient calculation result, and 4 is a multiple regression analysis result. In the questionnaire, mark cards were used to tabulate the applicable levels for questions using numerical data.

  As shown in 1 of FIG. 1, the student questionnaire method for investigating the first process, that is, the motivation for the class, conducts about five possible questions about the items related to the motivation level of the work training class. In order to obtain a larger number for the applicable level, such as 4 “I think so”, 3 “I think so”, 2 “I think a little”, 1 “I don't think so” Digitize. As for the itemized demanding for teaching, as shown in 2 of FIG. 1, leave elicit how motivation increases if advance students set questions about as many request item, the request Levels that apply using mark cards, such as 4 for “I think so”, 3 for “I think so”, 2 for “I think so”, and 1 for “I do n’t think so” The questionnaire results are digitized so that a larger number can be obtained. All of these students' motivation level data and item-specific requirement data are stored as variables in a computer using a mark card reader.

  The second process is to narrow down requirement items that contribute most to the level of motivation. First, the motivation for a student's class is the average of the level that applies to a single question or multiple questions, and the level of motivation of the individual student is determined by the numerical value. Similarly to the motivation for the class, the required level of each required item for the class is obtained by taking an average value of the required levels for a single question or a plurality of questions, and quantifying the required level of the student. For all students to be taught, the level of motivation for these classes and the required level for each class are stored as random variables, and the correlation coefficient with the required items including motivation is calculated. For example, as shown in 3 of FIG. 1, paying attention to items with a high correlation coefficient between motivation and request items (for example, 0.5 or more), the request items are extracted, and the program user determines them as variables and stores them in the computer. To do. Here, if there are many request items having a high motivation and correlation coefficient, the correlation coefficient between the request items may be focused and the request items may be further narrowed down. In the example of FIG. 1, it is better to have a “prospect” work training theme / content, that is, a prospect for a given work task, or “creativity” work training theme / content should include any factors to think about, “Production of assembly product” The theme of the practical training and the contents of whether or not the product that can be assembled is produced were extracted as factors contributing to the motivation to the practical training.

  In the third process, it is finally confirmed by multiple regression analysis whether the motivation for the class can be explained by the requirement items extracted in the second process. For example, as shown in 4 of FIG. 1, the calculated contribution ratio is 0.54, with “prospect”, “creativity”, and “assembly production” extracted as motivation for work training as objective variables. Yes, it means that 54% of motivation for practical training can be explained. The multiple correlation coefficient between the motivation for work training and the extracted three requirement items is 0.73, and the error of the correlation coefficient of 0.73 from the F test result occurs only with a probability of 1%. This means that a multiple correlation coefficient of 0.73 is guaranteed with a probability of 99%. Here, when the contribution rate and the correlation coefficient are low, the program user reviews the contents of the questionnaire. In the program, multiple regression analysis is performed using numerical values related to the motivation level for the target class of all students and the required level for each class extracted for the target class to determine the contribution rate, multiple correlation coefficient, and significance. Is displayed.

  According to this embodiment, three factors, “forecast”, “creativity”, and “assembly production” are extracted as influential factors (requirements) that are highly correlated with student motivation for the machine training class. The motivation for the machine training class can be explained by 57% by three factors: “forecast”, “creativity”, and “assembly production”. In other words, it was found that students can have high prospects in machine work training, and can be expected to be highly motivated by incorporating the elements they think into and having a training theme and content that can produce assemblies that can be assembled after production. In addition, disassembly and assembly training of various machines was considered as the most representative example, and improvements were made by incorporating disassembly and assembly training into machine work training classes.

  In the embodiment of FIG. 1, the student motivation influencing factors (required items) for the machine training class are extracted, but in other embodiments, the student motivation influencing factors for various subject classes such as mathematics and physics are extracted. May be. In addition, it can be applied to the extraction of factors that influence students' motivation not only for classes but also for various learning materials.

  If influential factors (required items) that can stimulate the motivation of students working on the class can be extracted effectively, the student's motivation to various classes can be increased efficiently and further the proficiency can be increased. This method can be used not only in educational institutions, but also in service schools that handle cram schools and various learning materials.

It is a flowchart which shows one Embodiment of this invention.

Explanation of symbols

1 Student motivation questionnaire item for machine work training 2 Question item requirement questionnaire item for machine work training 3 Correlation coefficient calculated from questionnaire results 4 Motivation and multiple regression analysis results with requirement items highly correlated with motivation

Claims (2)

  Students are surveyed about motivation for the class and the degree of requirement for each class, and the correlation between the motivation and the required items is calculated and the required items highly correlated with motivation are narrowed down using principal component analysis. By performing multiple regression analysis using highly motivated variables and explanatory variables as highly correlated requirement items, the requirements of students for a lesson can be effectively and efficiently extracted. Motivation influencing factor extraction method. Motivation for class and the first process of conducting a questionnaire survey for students regarding the degree of requirement for each class. Calculation of the correlation between motivation and requirements and narrowing down the requirements that are highly correlated with motivation using principal component analysis. A student motivation influencing factor extraction program for a class characterized by comprising a second process for performing a class, a third process for performing a multiple regression analysis with the objective variable as a motivation and an explanatory variable as a highly correlated requirement item.

Images