Gold standard, again!

lessons/Data-Science/CODAP/codap-exploring/langs/en-us/index.adoc

@@ -22,8 +22,8 @@
 | Student-facing Lesson Goals
 |
 
-* Let's familiarize ourselves with CODAP.
-* Let's learn how to tell whether a value is a Number or a String.
+- Let's familiarize ourselves with CODAP.
+- Let's learn how to tell whether a value is a Number or a String.
 
 | Materials
 |[.materials-links]
@@ -32,7 +32,7 @@
 
 | Key Points For The Facilitator
 |
-* Error messages are the computer trying to give us a clue that something is wrong.  Model reacting to error messages with interest to demonstrate to students that the messages are a helpful tool.
+- Error messages are the computer trying to give us a clue that something is wrong.  Model reacting to error messages with interest to demonstrate to students that the messages are a helpful tool.
 
 
 |===
@@ -77,7 +77,7 @@ Debrief with students. Discuss any difficulties that students encountered, for i
 
 === Launch
 
-The folks at the shelter need help completing a variety of tasks using CODAP! To do this, they need to learn how CODAP handles the kinds of data in the animals data set (numbers, words, and more...).
+The folks at the shelter need help completing a variety of tasks using CODAP! To do this, they need to learn how CODAP handles the kinds of data in the animals dataset (numbers, words, and more...).
 
 === Investigate
 

lessons/Data-Science/bar-and-pie-charts/langs/en-us/index.adoc

@@ -38,7 +38,7 @@
 |===
 
 == Grouping by Value
-@objective{categorical-visualizations-pyret}
+@objective{categorical-visualizations-proglang}
 @objective{interpret-bar}
 @objective{interpret-pie}
 @objective{pros-cons-bar-pie}
@@ -169,7 +169,7 @@ Open the @opt-starter-file{hair} to try them out!
 
 
 == Groups and Subgroups
-@objective{compound-categorical-visualizations-pyret}
+@objective{compound-categorical-visualizations-proglang}
 @objective{interpret-multi-bar}
 @objective{interpret-stacked-bar}
 === Overview

lessons/Data-Science/box-plots/langs/en-us/images/lesson-images.json

@@ -204,5 +204,40 @@
     "description": "a box plot of the Smith family data clustered tightly at the right end of the number line",
     "source" : "Created by the Bootstrap Team in Pyret based on contrived data",
     "license" : "Creative Commons 4.0 - NC - SA"
+  },
+  "launch-boxplot-a.png": {
+    "description": "a box plot that is skewed to the right; the center box has a larger area on the right",
+    "source" : "Created by the Bootstrap Team in Pyret based on contrived data",
+    "license" : "Creative Commons 4.0 - NC - SA"
+  },
+  "launch-boxplot-b.png": {
+    "description": "a box plot that is skewed to the left; the center box has a larger area on the left",
+    "source" : "Created by the Bootstrap Team in Pyret",
+    "license" : "Creative Commons 4.0 - NC - SA"
+  },
+  "launch-histo-c.png": {
+    "description": "a histogram that is symmetric with a peak in the middle",
+    "source" : "Created by the Bootstrap Team in Pyret",
+    "license" : "Creative Commons 4.0 - NC - SA"
+  },
+  "launch-histo-d.png": {
+    "description": "A symmetric histogram with two peaks",
+    "source" : "Created by the Bootstrap Team in Pyret",
+    "license" : "Creative Commons 4.0 - NC - SA"
+  },
+  "boxplot1-launch.png": {
+    "description": "a box plot that is skewed to the right; the center box has a larger area on the right",
+    "source" : "Created by the Bootstrap Team in Pyret",
+    "license" : "Creative Commons 4.0 - NC - SA"
+  },
+  "boxplot2-launch.png": {
+    "description": "a box plot that is skewed to the left; the center box has a larger area on the left",
+    "source" : "Created by the Bootstrap Team in Pyret",
+    "license" : "Creative Commons 4.0 - NC - SA"
+  },
+  "histogram-launch.png": {
+    "description": "A histogram that is skewed to the right",
+    "source" : "Created by the Bootstrap Team in Pyret",
+    "license" : "Creative Commons 4.0 - NC - SA"
   }
 }

lessons/Data-Science/box-plots/langs/en-us/index.adoc

@@ -418,7 +418,7 @@ ifnotslide{@centered-image{images/codap-box-plot.png, 300}}
 @A{The third quartile is the value for which 75% of the animals weighed that amount or less. Another way of saying that would be that it is the value for which 25% of the animals weigh that amount or more.}
 @Q{Why do you think this visualization is sometimes called a "box and whisker plot"?}
 @A{The distance between Min/Q1 and Q3/Max is drawn like whiskers!}
-@Q{Could we make a box plot for every column in the data set?}
+@Q{Could we make a box plot for every column in the dataset?}
 @A{No. We can only make box plots for @vocab{quantitative} columns.}
 }
 
@@ -499,7 +499,7 @@ It is extremely common for students to forget that the quartiles divide the data
 
 Want to check student mastery of the content you've just taught? Administer @assessment{box-plots-check3-desmos} to get a snapshot of your students' current level of mastery.  Make sure you have created a link or code for your class to the assessment.
 
-If you'd prefer to wait until your students have completed the __entire__ lesson to check mastery, we also offer a cumulative assessment at the end of @link{https://www.bootstrapworld.org/materials/latest/en-us/lessons/box-plots/index.shtml?pathway=data-science#_interpreting_box_plots_pyret, "Connecting Box Plots and Histograms"}, below.
+If you'd prefer to wait until your students have completed the _entire_ lesson to check mastery, we also offer a cumulative assessment at the end of @link{https://www.bootstrapworld.org/materials/latest/en-us/lessons/box-plots/index.shtml?pathway=data-science#_interpreting_box_plots_pyret, "Connecting Box Plots and Histograms"}, below.
 
 }
 
@@ -514,17 +514,24 @@ Box plots and histograms give us two different views of the shape of quantitativ
 
 @slidebreak
 
-[cols="^.^1a,^.^1a", frame="none", grid="none"]
+
+@QandA{
+@Q{One of these box plots represents the same data as the histogram. Which one is it? How do you know?}
+@A{The first box plot represents the same data as the histogram.}
+@A{Sample response: The right whisker of the first box plot is longer, suggesting that there are outliers on the right causing the data to skew right.}
+
+@center{@image{images/histogram-launch.png,175}}
+
+[cols="^.^1a,^.^1a"]
 |===
-| @image{images/box-plot-pounds-cropped.png}
-| @image{images/histogram-pounds.png}
+| @image{images/boxplot2-launch.png, 125}
+| @image{images/boxplot1-launch.png, 125}
 |===
 
-@lesson-instruction{
-- Take a minute to look at these two visualizations we've made for the "pounds" column and try to connect them in your mind.
-- How would you explain the differences in how the data gets displayed?
 }
 
+@teacher{Students may notice that the left part of the box is larger on the box plot B. Although that observation is correct, the box's larger area does *not* indicate that *more* data points are clustered there. Remind students that an equal number of data points are clustered in each quadrant of the box plot. You can emphasize this core idea as you discuss the table comparing box plots and histograms@ifnotslide{,below.}@ifslide{ on the next slide.}}
+
 @slidebreak
 
 [cols="1,^1,^3, ^3", options="header"]
@@ -549,6 +556,44 @@ shorter bars to one side
 |===
 
 
+@slidebreak
+
+@QandA{
+@Q{Match the box plots below with the corresponding histograms. How did you make your matches?}
+@A{Box plot A represents the same data as histogram C. Box plot B represents the same data as histogram D. Students may suspect that the short whiskers of histogram B match with the short bars on the edges of histogram C. Because box plots represent density and not frequency, this reasoning is incorrect. }
+
+++++
+<style>
+/* Add letters to the top left corner, relative to each table cell */
+.lettering td { position: relative; }
+.lettering .paragraph:first-child p { position: absolute; top: 0; font-weight: bold; }
+</style>
+++++
+
+[.lettering, cols="^.^1a,^.^1a", header="none", stripes="none"]
+|===
+| A
+
+@image{images/launch-boxplot-a.png, 125}
+| B
+
+@image{images/launch-boxplot-b.png, 125}
+| C
+
+@image{images/launch-histo-c.png, 125}
+| D
+
+@image{images/launch-histo-d.png, 125}
+
+|===
+
+
+}
+
+@teacher{The matching activities in this launch section were drawn from research conducted by @citation{lem-et-al-2011, "Lev et al (2011)"}.}
+
+
+
 @strategy{Kinesthetic Activity}{
 
 Divide the class into groups, and give each group a ruler and a ball of play-dough. Have them draw a number line from 0-6 with the ruler, marking off the points at 0, 3, 4, 4.5 and 6 inches. Have the groups roll the dough into a thick cylinder, divide that cylinder in half, and then split each half to form four _equally-sized cylinders_. The play-dough represents a @vocab{sample}, with values divided into four quarters.

lessons/Data-Science/contracts-tables-visualizations/langs/en-us/index.adoc

@@ -399,7 +399,7 @@ Students will be introduced to functions for making one-variable visualizations
 
 The goal here is for students to become familiar with using @dist-link{Contracts.shtml, Contracts} to write expressions that will produce visualizations. But knowing how to __make__ a histogram doesn't mean a student really __understands__ histograms, and that's OK!
 
-@teacher{Once students know how to use Contracts to write expressions to make these visualizations, we have dedicated, in-depth lessons focused on understanding @lesson-link{bar-and-pie-charts}, @lesson-link{histograms}, @lesson-link{visualizing-the-shape-of-data}, @lesson-link{box-plots}, @lesson-link{scatter-plots}, @lesson-link{linear-regression}, @lesson-link{advanced-visualizations}, etc.}
+@teacher{Once students know how to use Contracts to write expressions to make these visualizations, we have dedicated, in-depth lessons focused on understanding @lesson-link{bar-and-pie-charts}, @lesson-link{histograms-visualize}, @lesson-link{histograms-interpret}, @lesson-link{box-plots}, @lesson-link{scatter-plots}, @lesson-link{linear-regression}, @lesson-link{advanced-visualizations}, etc.}
 
 === Launch
 

lessons/Data-Science/correlations/langs/en-us/index.adoc

@@ -175,7 +175,7 @@ Complete @printable-exercise{identifying-form.adoc} and focus _just on the secon
 
 === Synthesize
 - It only makes sense to look for direction in linear relationships!
-- Which data sets appear to have a positive correlation between the variables?
+- Which datasets appear to have a positive correlation between the variables?
 
 == Correlations have _Strength_
 
@@ -362,7 +362,7 @@ Students often giggle at some of the Spurious Correlations examples, but fail to
 == Exploration Project (Correlations)
 
 === Overview
-Students apply what they have learned about correlations to their chosen dataset. They will add two or more items to their @starter-file{exploration-project}: (1) a correlation they think they see in the data set, and (2) the form, direction and strength of that correlation. 
+Students apply what they have learned about correlations to their chosen dataset. They will add two or more items to their @starter-file{exploration-project}: (1) a correlation they think they see in the dataset, and (2) the form, direction and strength of that correlation.
 
 @teacher{Visit @lesson-link{project-data-exploration} to learn more about the sequence and scope. Teachers with time and interest can build on the exploration by inviting students to take a deep dive into the questions they develop with our @lesson-link{project-research-paper}.
 }

lessons/Data-Science/data-cycle/langs/en-us/index.adoc

@@ -230,7 +230,7 @@ Most questions can be broken down into one of four categories:
 @Q{What kind of question is "Are more animals fixed or unfixed?"? How do you know?}
 @A{It's an _arithmetic question_ because answering it requires comparing two simple calculations.}
 @Q{What kind of question is "Are snails or tarantulas taller?"? How do you know?}
-@A{It's a _question we can't answer_ because there isn't any information in this data set about the heights of the animals.}
+@A{It's a _question we can't answer_ because there isn't any information in this dataset about the heights of the animals.}
 @Q{What kind of question is "How old is Toggle?" How do you know?}
 @A{It's a _lookup question_ because it can be answered by just looking at the table.}
 @Q{What kind of question is "Are older animals adopted more quickly than younger animals?" How do you know?}
@@ -368,7 +368,7 @@ Throughout the remainder of Bootstrap:Data Science we will be using Data Cycle p
 @slidebreak
 
 @QandA{
-So far we have always worked with the Animals Starter File, which is a sample taken from a larger data set. +
+So far we have always worked with the Animals Starter File, which is a sample taken from a larger dataset. +
 To complete this page we will be working with the @starter-file{expanded-animals}.
 @Q{What else do you Notice?}
 @A{*Be sure to surface the following:*}

lessons/Data-Science/data-cycle/langs/en-us/pages/notes-data-cycle.adoc

@@ -27,13 +27,13 @@ Each question a Data Scientist asks adds a chapter to the story of their researc
 
   * *Arithmetic questions* - Answered by doing calculations (comparing, averaging, totaling, etc.) with values from one single column. Examples of arithmetic questions might be “How much does the heaviest animal weigh?” or “What is the average age of animals from the shelter?”
 
-  * *Statistical questions* - These are questions that both _expect some variability in the data_ related to the question and _account for it in the answers_. Statistical questions often involve multiple steps to answer, and the answers aren't black and white. When we compare two statistics we are actually comparing two data sets. If we ask "are dogs heavier than cats?", we know that not every dog is heavier than every cat! We just want to know if it is _generally_ true or _generally_ false!
+  * *Statistical questions* - These are questions that both _expect some variability in the data_ related to the question and _account for it in the answers_. Statistical questions often involve multiple steps to answer, and the answers aren't black and white. When we compare two statistics we are actually comparing two datasets. If we ask "are dogs heavier than cats?", we know that not every dog is heavier than every cat! We just want to know if it is _generally_ true or _generally_ false!
 
   * *Questions we can't answer* - We might wonder where the animal shelter is located, or what time of year the data was gathered! But the data in the table won’t help us answer that question, so as Data Scientists we might need to do some research beyond the data. And if nothing turns up, we simply recognize that there are limits to what we can analyze.
 
 @vspace{1ex}
 
-- Next, we *Consider Data*, by determining which parts of the data set we need to answer our question. Sometimes we don't have the data we need, so we conduct a survey, observe and record data, or find another existing dataset. Since our data is contained in a table, it's useful to start by asking two questions:
+- Next, we *Consider Data*, by determining which parts of the dataset we need to answer our question. Sometimes we don't have the data we need, so we conduct a survey, observe and record data, or find another existing dataset. Since our data is contained in a table, it's useful to start by asking two questions:
 
   * What rows do we care about? - Is it all the animals? Just the lizards?
   * What columns do we need? - Are we examining the ages of the animals? Their weights?

lessons/Data-Science/dot-plots/langs/en-us/images/lesson-images.json

@@ -0,0 +1,122 @@
+{  
+  "histogram-a.png": {
+    "description": "Histogram with 6 columns whose respective heights from left to right are 1, 1, 3, 3, 1, 1",
+    "source" : "Created by the Bootstrap Team based on contrived data",
+    "license" : "Creative Commons 4.0 - NC - SA"
+  },
+  "histogram-b.png": {
+    "description": "histogram with 10 bars equal in height based on contrived data",
+    "source" : "Created by the Bootstrap Team",
+    "license" : "Creative Commons 4.0 - NC - SA"
+  },
+  "histogram-c.png": {
+    "description": "Histogram with 9 bars whose heights from left to right are 0,1,3,3,1,0,0,1,1",
+    "source" : "Created by the Bootstrap Team based on contrived data",
+    "license" : "Creative Commons 4.0 - NC - SA"
+  },
+  "histogram-d.png": {
+    "description": "Histogram with 9 bars whose heights from left to right are 0,1,1,0,0,1,3,3,1",
+    "source" : "Created by the Bootstrap Team based on contrived data",
+    "license" : "Creative Commons 4.0 - NC - SA"
+  },
+  "histogram-e.png": {
+    "description": "Histogram with 11 bars whose heights from left to right are 0,2,2,1,0,0,0,0,1,2,2",
+    "source" : "Created by the Bootstrap Team based on contrived data",
+    "license" : "Creative Commons 4.0 - NC - SA"
+  },
+  "pounds.png": {
+    "description": "Histogram for the pounds column of the animals dataset",
+    "source": "Created by the Bootstrap Team",
+    "license": "Creative Commons 4.0 - NC - SA"
+  }, 
+  "dot-plot-pounds.png": {
+    "description": "Dot plot for the pounds column of the animals dataset",
+    "source": "Created by the Bootstrap Team",
+    "license": "Creative Commons 4.0 - NC - SA"
+  }, 
+  "weeks.png": {
+    "description": "Histogram for the weeks column of the animals dataset",
+    "source": "Created by the Bootstrap Team",
+    "license": "Creative Commons 4.0 - NC - SA"
+  },
+  "number-line.png": {
+    "description": "A blank number line number 0 to 15, for students to add data collected during class",
+    "source": "Created by the Bootstrap Team",
+    "license": "Creative Commons 4.0 - NC - SA"
+  },
+  "Ad.png": {
+    "description": "Dot plot A",
+    "source": "Created by the Bootstrap Team",
+    "license": "Creative Commons 4.0 - NC - SA"
+  },
+  "Ah.png": {
+    "description": "Histogram A",
+    "source": "Created by the Bootstrap Team",
+    "license": "Creative Commons 4.0 - NC - SA"
+  },
+  "Bd.png": {
+    "description": "Dot plot B",
+    "source": "Created by the Bootstrap Team",
+    "license": "Creative Commons 4.0 - NC - SA"
+  },
+  "Bh.png": {
+    "description": "Histogram B",
+    "source": "Created by the Bootstrap Team",
+    "license": "Creative Commons 4.0 - NC - SA"
+  },
+  "Cd.png": {
+    "description": "Dot plot C",
+    "source": "Created by the Bootstrap Team",
+    "license": "Creative Commons 4.0 - NC - SA"
+  },
+  "Ch.png": {
+    "description": "Histogram C",
+    "source": "Created by the Bootstrap Team",
+    "license": "Creative Commons 4.0 - NC - SA"
+  },
+  "Dd.png": {
+    "description": "Dot plot D",
+    "source": "Created by the Bootstrap Team",
+    "license": "Creative Commons 4.0 - NC - SA"
+  },
+  "Dh.png": {
+    "description": "Histogram D",
+    "source": "Created by the Bootstrap Team",
+    "license": "Creative Commons 4.0 - NC - SA"
+  },
+  "Ed.png": {
+    "description": "Dot plot E",
+    "source": "Created by the Bootstrap Team",
+    "license": "Creative Commons 4.0 - NC - SA"
+  },
+  "Eh.png": {
+    "description": "Histogram E",
+    "source": "Created by the Bootstrap Team",
+    "license": "Creative Commons 4.0 - NC - SA"
+  },
+  "Fd.png": {
+    "description": "Dot plot F",
+    "source": "Created by the Bootstrap Team",
+    "license": "Creative Commons 4.0 - NC - SA"
+  },
+  "Fh.png": {
+    "description": "Histogram F",
+    "source": "Created by the Bootstrap Team",
+    "license": "Creative Commons 4.0 - NC - SA"
+  },
+  "names1.png": {
+    "description": "A dot plot showing the distribution of name lengths for a group of students",
+    "source": "Created by the Bootstrap Team",
+    "license": "Creative Commons 4.0 - NC - SA"
+  },
+  "names2.png": {
+    "description": "A dot plot showing the distribution of name lengths for a group of students",
+    "source": "Created by the Bootstrap Team",
+    "license": "Creative Commons 4.0 - NC - SA"
+  },
+  "dot-plot-weeks.png": {
+    "description": "A dot plot showing the distribution of weeks animals spend at the shelter",
+    "source": "Created by the Bootstrap Team",
+    "license": "Creative Commons 4.0 - NC - SA"
+  }    
+}