Research On Individual Differences In High School Mathematics Learning Efficiency

In today’s fast-paced development of society, the value of efficiency has become the focus of widespread popular topic. For learners, the discretion of the academic performance depends largely on the students’learning efficiency in their own learning process. If the learning efficiency can not be assured, it will be difficult for student to obtain the desired learning outcomes, the final result will not satisfactory. Therefore, students should not only attache great importance to the capacity of knowledge has been mastered and the extent of understanding, but also pay more attention to the capacity of acquired knowledge on the basis of the original size and in unit time, namely, the level of learning efficiency. Previous research showed that students’ learning efficiency are influenced by the way of teachers’teaching, learning time students has invested in mathematics, intellectual factors and non-intellectual factors of students in the process of mathematics study and many other aspects, as the carrier to mathematics subject, the paper selects four elements of them:the students’ existing math test scores, non-intellectual factors of students in the process of mathematics learning, mathematics learning time and students’ mathematics foundation, using the mode of combining theory and practice, building measurement methods and tools of individual mathematics learning efficiency, using data envelopment analysis to measure,compare and analyze different individual’s mathematics learning efficiency and learning effect.In theory part, through literature analysis, selecting four main factors influencing students’ mathematics learning efficiency on the basis of existing theory, building measuring tools and model of mathematics learning efficiency. In empirical research part, selecting a high model school as the research object, recovered50valid questionnaires from two classes of senior one in this school. Using SPSS18.0to census and arrange the data of students’non-intelligence factors in the process of mathematics learning, using Deap2.1software to achieve data envelopment analysis, that is to say, taking students’learning process as an input-output system to evaluate students’individual mathematics learning efficiency and to analyze their characteristics and differences, providing a reference for correctly guiding students to effective learning. After analysis, the major conclusions can be drawn as follows:1. There has been significant differences in efficiency existed among different students in the process of mathematics study, through the analysis results of DEAmodel, student’s mathematics learning efficiency can be roughly divided into three categories:DEA effective, weak DEA effective and non DEA effective;2. The mathematics learning efficiency of same individual exists difference to a certain extent in different periods;3. The mathematics study efficiency of experimental class students is higher than the regular class students’as a whole;4. The number of students in experimental class whose mathematics efficiency is DEA effective is significantly higher than the number of students on the same term in regular class;5. Among the students whose mathematics learning conditions are weak DEA effective and non DEA effective, the mathematics learning synthesized efficiency of students from experimental class is higher than the students’of regular class. 6. The mathematics learning synthesized efficiency and the comprehensive quality of the original graduating school is a significant positive correlation.In the study of individual differences in students’ mathematics learning efficiency, we try to explore influencing factors in various aspects which lead to efficiency difference, in addition, according to the survey results, this paper put forward some advice can improve student’s mathematics learning efficiency, providing feasible suggestions and guidance for students to reasonably arrange and distribute their learning time and to strengthen some specific weak links.