Career Ready, World Ready.#
This page summarizes how this course addresses many of the objectives outlined in the WSU College of Arts and Science (CAS) Career Ready, World Ready initiative survey collected December 2025.
The responses to this survey were formulated for a course “Estimate Anything: The Art of Play in Science” taught in the Fall of 2024, offered to both graduate an upper-level undergraduate students as part of iSciMath. The course was attended by a mix of graduate an undergraduate students from Physics, Mathematics, Mechanical Engineering, Chemical Engineering, and Material Science and Engineering. The course was offered remotely for interested students, but we only had two students attending remotely.
I co-taught the course with Kevin Vixie, who supplemented the main material with a seminar each week on estimation in mathematics: how to develop intuition with mathematical proofs. Co-teaching is extremely valuable for teaching many of the aspects addressed in this survey.
A Syllabus, course description, and course materials are published using the Jupyter Book framework, and are available online at Read The Docs and are code-complete. All calculations and numerical code needed to produce figures, animations, etc. are included so that students and other instructors can adapt these as needed.
The overall structure included a survey of technical skills associated with estimation and approximation, including a combination of numerical and analytic analysis skills. Students were introduced to https://cocalc.com/, an online computing platform with real-time code and numerical collaboration, allowing them to rapidly perform numerical and symbolic analysis of the estimation problems. CoCalc also has integrated support for various “AI” platforms (see discussion below). (collab, tech, AI)
Students worked together to make estimations. A particularly useful format was for a student “leader” to work at the board, guiding the estimation process. Important to this was that the leader not have expert domain knowledge about the problem. Instead, their task was to use the rest of the class as a resource to generate ideas and to get useful information needed to solve the problem at hand. This required them to organize the problem at a high level, communicate with the class using multiple techniques (e.g. verbal, text, pictures, equations), and then to draw on the diverse set of experiences represented by the student body to come to a coherent solution to the problem. (collab, comm, solve, lead, aware, social, tech)
As part of the technical content of the course, we presented a careful discussion of probability, Bayesian analysis, and confidence intervals to dispel some of the myths students have about these highly non-intuitive ideas, and to promote quantitatively accurate data literacy. In keeping with the spirit of the course, the emphasis here was on using Monte-Carlo techniques to rapidly test assumptions rather than sophisticated statistical techniques. (solve, tech)
In addition to the specific estimation tasks and skills, a parallel stream of the course was to have the students engage with a series of podcasts from the Freakonomics network – especially a series focused on how to be creative, and how to succeed at failing. These podcasts were discussed in class, and students were prompted to assess their own abilities and skills. We discussed how different students learn, the importance of finding out how you learn best, and how to monitor your progress with agile development techniques to rapidly adapt as needed.
A key component to this was co-teaching where two instructors present difficult concepts using very different approaches. From this interactive presentation, it became clear to both students and instructors that students learn differently – with roughly a third of the class learning best from one approach (algebraic), another third from a geometric approach, and the rest benefiting from numerical engagement. (comm, aware, tech)
Additionally, students were asked to write a short essay about something they had mastered (see Mastery). The idea is that, by reflecting and understanding how they mastered that thing, they could identify what works for them, and apply this to mastering the academic problems they now face at university. Part of this discussion was about the System 1 / System 2 analogy put forth by Daniel Kahneman in his book “Thinking, Fast and Slow”. (aware)
The problems chosen for this course we guided by social and economic relevance: how much energy do certain activities take? What resources are required to grow a certain amount of food? This allowed us to discuss ethical concerns in the class, providing a social context for the technological skills practiced. To enhance this aspect, we need to draw as large a base of students and co-instructors as possible. (ethics, social)
Students submitted final projects where they solved an estimation problem of their choice, and communicated the results both in written form in the style of Randall Munrow’s “What If?”, and through a live presentation to the class. The goal here was explicit communication to a wide audience of peers and the general public. (comm)
Finally, a comment about “AI”. As part of my Syllabus, I expect students to engage with various “AI” tools, and require them to explicitly find examples where these tools are useful and where they hallucinate or otherwise provide untrustworthy results. That being said, we eschew the use of such tools as much as possible in class for the following reasons, which we explicitly discuss.
The use of such tools is very tempting as a way of improving efficiency and making things easier. However, much of the research discussed in the podcast series emphasizes how learning requires struggle. By making things easier, you thus reduce the improvement in your mental ability, which is the whole point of studying.
Therefore, in this course, we minimize the use of even calculators to maximize the benefit to our brains. This does not mean abandoning these: once we have exercised our brains, defined our plan of attack, and evaluated the approach, then we turn to various tools like calculators, simulations, searches, and AI – but only once we have identified ways of assessing the validity of these results.
The energy and cooling costs of AI are staggering. This needs to be considered when using these tools, and this course provides a perfect place for introducing and discussing these issues. The environmental impact alone provides sufficient justification to minimize their use, and the potential social impact from the effects described above compound this problem.
In the previous discussion, we refer to each of the nine section of the survey using the following short-codes:
(collab) Collaboration: Work well with others, build trust, and get things done together.
(comm) Communication: Share your ideas clearly and connect with different audiences.
(solve) Critical Thinking & Problem Solving: Look at challenges from every angle and find solutions that work.
(ethics) Ethical & Professional Engagement: Make choices that reflect your integrity, values, and accountability.
(lead) Leadership: Inspire and guide others with vision, clarity, and empathy.
(aware) Self-Awareness & Growth: Know your strengths, learn from challenges, and keep moving forward.
(social) Social Complexity: Understand the bigger picture by exploring history, culture, and global issues.
(tech) Digital & Tech Literacy: Feel confident using the tools, tech, and media that power today’s world.
(AI) AI Literacy: Use AI thoughtfully and creatively to spark new ideas and solve problems.