In sunniraleigh/schoolR: Aids Students And Teachers In Learning And Teaching Data Science

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(schoolR)
library(dplyr)

Coding is being introduced to students at younger and younger ages. With the schoolR package, teachers and students can use coding, specifically in R, as a tool to help learn and teach mathematical concepts.

SchoolR helps teachers:

• visually explain shapes and areas to younger children

• explain trends in data visually through graphing

• explain how to interpret simple linear regression diagnostics

• generate large numbers of practice problems for students with the correct answers for students to check their work

SchoolR helps students:

• see how different shapes create different areas

• accurately graph their data to better understand trends

• practice their math skills

This document provides an introduction to schoolR's functions with examples to show teachers how to use them while teaching to improve their student's learning and comprehension of mathematical concepts. It includes different examples and applications for different age/skill levels.

Drawing Shapes

Drawing squares with square

square allows students to input a desired side length and produces the respective square visualization along with accurate calculations for the perimeter and area of the square. Students and/or teachers can also specify a vector of square lengths.

square(2:3)

Draw a circle with circle

circle allows the user to input a desired radius length which returns the respective circle visualization along with calculations of its diameter, perimeter, and area. The visualization is in form of a graph and the geometrical calculations are print in a data frame.

circle(3)

The user can also specify a vector containing more than one radius length. When there are multiple radius lengths inputted, the function will graph each respective circle layered over one another.

lengths <- c(4,7,9)
circle(lengths)

circle(2:6)

Calculate area and perimeter of an n-gon with ngon

In order to draw an ngon, there are three separate functions that need to be called. First, a user can use the ngon function by inputting a given length of radius and the number of sides for the shape. ngon creates and returns a tibble that contains the calculated area, perimeter, radius, angle and number of sides.

ngon(2,3)
ngon(2:5,3)

Calculating coordinates

The user can call map_ngon after calling ngon in order to map the vector radius \code{r} and number of sides \code{n} to the function coords_ngon. map_ngon maps each unique value of radius or number of side to the function coords_ngon -- which, simply creates a tibble containing the coordinate information, area and perimeter of each of the different polygons specified in the vector radius \code{r} and number of sides \code{n}

ngon(2,3) %>%
  map_ngon()

ngon(2:5,3) %>%
  map_ngon()

Drawing an n-gon with draw_ngon

The user can pipe the function draw_ngon to the function ngon to visualize the respective polygon. The draw_ngon function takes in as an argument the output of map_ngon, a tibble created based off the arguments passed to ngon.

ngon(2,3) %>%
  map_ngon() %>%
  draw_ngon()

ngon(2:5,3) %>%
  map_ngon() %>%
  draw_ngon()

Graphing with graph_my_data

graph_my_data allows students and teachers to produce high-quality graphs of data that they obtain, or of data from data.frames. It outputs basic simple linear regression diagnostics such as correlation coefficients, means, and medians, and interprets the r value. This allows for a deeper understanding of general trends in data for students who likely have very little statistical background. Teachers who are teaching students about mean and median can use example data sets and pre-existing data.frames to help students understand how they are generated from data, and how outliers affect them differently. graph_my_data allows teachers to introduce students to the concept of trends in data, and how one variable can be correlated to another. By producing graphs as well as the Pearson correlation coefficient with its interpretation, graph_my_data lets students gain a more intuitive understanding of how to identify and interpret trends in data, thus providing younger students with an introduction to bivariate data analysis without getting tangled up in the details of computing or coding for this information.

for younger students: elementary to middle school

For younger students, teachers and/or students can create simple vectors of data, creating a vector x of points to plot on the x-axis and a vector y of points to plot on the y-axis. For example, teachers can provide students with very basic data sets, such as a set of 5 points with x-coordinates being the number of students called in sick by their parents and y-coordinates being the total number of students absent at school for each day.graph_my_data will automatically graph their points, allowing them to understand how graphing and relationships between variables work. Teachers will then be able to show students how when more students are called in sick, more students are absent overall. This allows them to introduce younger students to the basic concepts surrounding correlation and causation. Additionally, it introduces young students how to graph data, providing them with examples of what accurate graphs of data that they can input via vectors should look like.

x <- c(1, 3, 4, 6, 2)
y <- c(2, 7, 7, 11, 4)
graph_my_data(x, y, "blue", "number of students called in sick", "total number of absences", "kids out of school on a certain day")

for older students: middle to high school

For older students, they could use the previous technique of creating vectors for data that they might be actually collecting, as students in middle school may be performing small science experiments and collecting data, and use graph_my_data to understand what their data actually means. For example, students in a middle school science class may be doing an experiment to see what the optimal amount of daily watering is for a pea plant. They can enter the collected data for each variable in as a vector x for the ml of water for each point, and a vector y corresponding to the measured height of the plants. They can then use graph_my_data to see the results of their experiment graphed.

x <- c(0.5, 1, 2, 3, 5, 10)
y <- c(2, 3, 3.5, 6, 4, 2)
graph_my_data(x, y, "purple", "ml of water", "height of plant (cm)", "plant height vs amount of water given to plant daily")

Older students could also use data.frames to be able to use larger sets of data and try looking at relationships between different variables to understand and interpret trends. Teachers can begin introducing students to concepts of identifying correlation and causation. This function will automatically graph the data that the student is looking at, as well as interpret the trend in the data, with the goal being that students will eventually be able to look at and interpret trends in data on their own. This can also serve as an introduction to teaching students the basics of how data.frames are structured in R. For example, a student could graph mgp against disp from the mtcars data set to see what the information in this data set is like.

graph_my_data(data = mtcars, color = "blue", ind_var = "mpg", dep_var = "disp", title = "mtcars data")$r_df

Producing Practice Problems with generate_problems

generate_problems allows educators to produce sets of practice questions for students based on what they would like them to work on. Teachers can specify the upper and lower bounds of the numbers being chosen from to use in the equation, what operation to use, and how many problems to produce.

younger students

For a younger student learning basic arithmetic, a teacher can ensure that the numbers are not out of the students' range of what they can do:

generate_problems("add", 1, 10, 5)

older students

For older students such as those in middle school who are practicing multiplication tables, a teacher can easily produce more advanced multiplication problems:

generate_problems("multiply", 5, 15, 10)

sunniraleigh/schoolR documentation built on Dec. 16, 2019, 12:48 a.m.