Teaching Exploration Of The Important Concept Of "Stoichiometry" In Middle School

"Amount of substance" and "Molar volume of gas" are two important concepts in the stoichiometry system of middle schools.Because of the many derivative concepts and abstraction,these two parts have always been the most difficult points in frontline teaching.Deep learning is an effective way to develop the core literacy of students' disciplines,but there are currently few teaching research cases of chemometrics systems oriented by deep learning.Therefore,taking deep learning as the theoretical guide is a new direction for the exploration of concept teaching in the chemometric system.This research first analyzes the value of previous studies through literature analysis,and proposes new research goals.Then,based on the theory of deep learning,it clarifies the teaching design ideas,uses interactive software to design classroom teacher and student activities,and conducts teaching practices.At the same time,empirical studies have verified that in the chemometrics section,deep learning teaching activities designed using interactive software can effectively develop the level of the content of this system corresponding to the cultivation of students' core literacy goals.The case studies can provide reference for teaching practice.The thesis consists of five parts:The first part,based on the topics of "amount of substance" and "molar volume of gas",conducts statistical analysis of literature data,clarifies the causes of teaching difficulties,and understands the current status of strategic research on breakthroughs in difficulties.The research direction of the thesis is determined based on the theory of deep learning using interactive software to design teacher-student activities in two units of "Unit of Amount of Substance-Mole" and "Molar Volume of Gas",and conduct empirical research on the development of students' core literacy 1 and literacy 3 target levels.The second part,based on the analysis of the current situation,proposes the goal of this research to form teaching cases that can improve students' interest in learning and help students understand abstract concepts such as "amount of substance",so as to effectively develop their core literacy level.In addition,the specific research content was clarified according to the research goals,and research methods such as literature research,classroom observation,survey research,and comparative research were determined.The third part analyzes the core literacy goals of the subject corresponding to the research topic content,the implied subject thinking methods,and then clarifies the teaching goals.Then,based on the overview of deep learning theory guidance,it expounds how to integrate subject thinking methods and construct design ideas for deep learning teaching activities.Finally,referring to the teaching design template of class hours formed in the research and practice of deep learning projects in Haidian District,Beijing,the interactive software platform was used to design the two class teaching activities of "Unit of Amount of Substance-Mole" and "Molar Volume of Gas".The fourth part is to carry out teaching practice and empirical research.Two parallel classes in the first grade are selected for comparative teaching.The experimental class uses the teacher-student activities designed by interactive software for this study,while the control class uses traditional teaching methods.Data were collected through classroom observations,questionnaire surveys,and test paper tests,and a comparative analysis was made of the literacy development levels of the experimental and control students.The fifth part summarizes the research.In the teaching of the concept of chemometric system,guided by deep learning theory,the teaching method using interactive software designer-student activities can stimulate students' learning interest to a certain extent and effective development of this system corresponds to the training of students' core literacy goals such as "macro identification and microscopic analysis","evidence reasoning and model cognition".