Materials Update

CourseKata Version 6.0 Release Notes

CourseKata is pleased to announce the release of Version 6.0 of its textbooks on June 1, 2025. There are no big changes in this version compared to the previous version. For example, no pages have been added, removed, or re-ordered. However, we have made a number of minor improvements, many based on feedback from our instructors and students. Thanks for all the input!

Textbook Improvements

In addition to numerous improvements in wording and slight changes in figures, here are just some of the minor improvements we have made in the textbooks since the previous version. 

  • Consistent Terminology: A widespread change throughout the document is the adoption of "dataset" (singular and plural) in place of "data set" or "data sets". This change appears in numerous chapter and page sections.
  • Clarifications and Additions to Five-Number Summary:
    • When discussing distributions, a clarification was added on how to calculate the median when there is an even number of data points (it's the average of the middle two).
    • More detailed explanations were added regarding the quartiles (Q0, Q1, Q2, Q3, Q4) as cutpoints that divide the distribution into four equal groups of data points.
    • It's clarified that Q2 is the median, and Q0/Q4 are the minimum/maximum. Q1 is described as the median of the lower half, and Q3 as the median of the upper half of the distribution.
    • A footnote was added explaining that there are different methods for calculating Q1 and Q3, and that the favstats() function in R uses the widely used type=7 method.
    • The description of the output from the favstats() function was slightly rephrased to align with the discussion of the five-number summary.
  • Refinements in Model Notation and Concepts:
    • Descriptions in a table summarizing model notation were made more specific, changing from general statements like "More specific statement; model is the mean" to "Notation for one-parameter model using the mean" and from "Most general; can be used for any one-parameter model" to "Notation for any one-parameter model (including the mean)".
    • The empty model is explicitly referred to as a "one-parameter model" because it estimates one parameter (beta_0).
  • Updates to Formulas and R Code Instructions:
    • The formula for the pooled standard deviation was corrected to include the square root symbol.
    • Instructions and associated code windows for calculating 95% confidence intervals were added, including comparing results from original and doubled datasets.
    • Minor text changes related to interacting with R code windows were made, such as changing "blue dot and the word Ready" to "a <Run> button" and updating a troubleshooting step from "pressing the <Reconnect> button" to "pressing the <Connect> button again".
    • Instructions for loading data into a Jupyter notebook from a Google Sheet were updated, specifically changing the menu path for publishing to the web from "File menu and select Publish to the Web" to "File menu and select Share > Publish to the Web".

Student Surveys

We’ve made some updates to our student surveys, but hopefully not made them longer.

We added 24 metacognitive confidence rating items to the pre-survey (prior to chapter 1) and mid2 survey (after chapter 9) to better capture students’ self-assessed understanding of key concepts. These items align with questions from the summative performance assessment that are currently being piloted as Jupyter notebooks in select classes.

At the same time, we removed 15 motivational questions that had been repeated across multiple timepoints to reduce overall survey length.

We also added one new pulse check item at the beginning of each chapter. We further added 4 specific items related to learning R programming—students’ enjoyment, anxiety, perceived difficulty, and job-related interest—at the beginning and the end of course to track changes in attitudes over the course. These changes reflect our ongoing commitment to collecting meaningful data while reducing survey fatigue and ensuring that measurement supports the flow of learning.

Jupyter Notebooks

We are working hard on developing new Jupyter notebooks and other in-class materials (e.g., paper-based) and we will tell you about that soon. Stay tuned! And as always, just email us if you have any questions.