WEEK 9

During the Lecture 5, we learned R-studio and Shiny app and apply them in our lesson plans for maths teacher.

Shiny App helps teachers present abstract mathematical concepts to students through interactive applications and visualizations. Students can better understand and apply mathematical concepts by interacting with the app and exploring mathematical models, graphs, and data. Such learning experiences stimulate students' interest and increase their engagement and understanding of mathematics.

Also, R-Studio provides a powerful programming and data analysis environment that can help teachers and students perform data processing, statistical analysis and visualization. By writing R code, teachers can demonstrate mathematical problem solving, conduct data exploration and empirical research, and perform statistical inference and model building. Students can also practice and explore using R-Studio, improve their data analysis and programming skills, and gain practical application experience in mathematics learning.

When it comes to specific examples of applied mathematics teaching, one scenario where Shiny App and R-Studio can be used is teaching sampling distributions in probability and statistics.

Teachers can use R-Studio to write code to simulate sampling distributions such as the normal distribution, binomial distribution, or t distribution. By generating random samples and calculating statistics (such as means, standard deviations, confidence intervals, etc.), teachers can demonstrate the effects of different sample sizes on distributions and help students understand the concepts of the central limit theorem and the law of large numbers.

Next, teachers can use the Shiny App to develop an interactive application that allows students to observe changes in the sampling distribution by adjusting the sample size and the number of repeated samplings. By interacting with the application, students can intuitively understand the effect of sample size on distribution shape, mean, and variability. They can also use applications to calculate confidence intervals, perform hypothesis testing and other statistical inference practices.

Through this example, students can better understand the concepts and applications of sampling distributions through practical operations and visualization. They can gain insight into simulation methods by writing code using R-Studio, and visually observe and explore the properties of sampling distributions by interacting with the Shiny App. Such practical learning experiences not only strengthen their statistical analysis skills, but also develop their data thinking and problem-solving abilities.

In summary, Shiny App and R-Studio provide powerful tools and platforms for mathematics teaching that can help teachers create a richer, interactive and practical learning environment. By introducing these tools, teachers can spark students' interest in mathematics, enhance their engagement and understanding, and develop their skills in data analysis and programming. This will provide students with a more in-depth, comprehensive and practical mathematics learning experience, laying a solid foundation for their future study and career development.