Research On The In-depth Learning Assessment In The Junior High School Mathematics Based On SOLO Classification Theory

Deep learning is a comprehension-based approach that aims to develop higher-order thinking and problem-solving skills,enabling students to actively and critically process information and integrate it into a body of knowledge that can be effectively transferred to new contexts,with a greater emphasis on students’ inquiry into the nature of mathematics.The implementation of a deep learning approach is conducive to the implementation of the new curriculum reform and is more conducive to the implementation of core literacy.As deep learning is widely used in mathematics teaching and learning,how to evaluate students’ deep learning has become a hot issue in deep learning research,and is the main focus of this study.A review of existing research reveals that SOLO classification theory has unique advantages for deep learning evaluation.Therefore,this study constructs an assessment tool for deep learning in junior high school mathematics based on SOLO classification theory and the characteristics of the content of junior high school mathematics teaching.Firstly,the propensity of junior secondary school students to use deep learning approaches in the process of learning mathematics was investigated by developing corresponding evaluation scales;Secondly,the assessment criteria for deep learning were constructed based on the contents of the junior secondary school curriculum in the areas of "Mathematics and Algebra","Graphics and Geometry" and "Statistics and Probability",and a deep learning test was designed accordingly.The test was designed to determine the level of students’ deep learning based on their responses,and to obtain a picture of the deep learning of junior secondary students in mathematics.In this study,students in the third year of junior high school in a secondary school in S province were randomly selected from four classes: Class 5,Class 9,Class 13 and Class 14 to conduct the survey.According to the results of the scale,Classes 5 and 15 have higher mean values for the dimensions of deep learning and overall mean values compared to Classes 9 and13,indicating that students in Classes 5 and 15 are more inclined to use deep learning in their learning process;According to the results of the test,the number of students at the deep learning level in different content areas varied from class to class,but in general,classes 5 and 15 had more students at the deep learning level in the areas of "Number and Algebra," "Graphics and Geometry," and "Statistics and Probability," while classes 9 and 13 had fewer students at the deep learning level in all three areas.Based on the above evaluation results and the analysis of the causes,this study proposes teaching suggestions to promote deep learning in middle school mathematics from the connotation of deep learning,respectively: problem leadership to develop students’ higher-order thinking skills;associative construction to promote the transfer and application of knowledge;multiple assessments to improve students’ communication and reflection skills;and variation practice to help students master problem solving skills,and uses them as the basis for the corresponding teaching design and case studies.This study can enrich the research on deep learning assessment and provide a reference for teachers to evaluate students’ deep learning.