CMP@CPP: Task Design

I recently presented for the California Mathematics Project at Cal Poly Pomona and am sharing my talk and reflections here in hopes that someone can benefit and/or share their own thoughts and resources with me.

Stated goals
    Build trust
    Do math together
    Share strategies and resources

Here is a trimmed down video of the session along with the slides:

Task design CMP@CPP Summer 2018 from Evan Rushton

Overall Reflection

I felt more trust by the end of my session and feel that I was able to get many voices heard and shared during my talk. The introductory math task felt like a positive experience for everyone. Teachers shared their strategies and approaches in their small groups as well as during whole group discussions. I would like to get teachers to really visualize themselves in their actual classrooms, and plan lesson ideas around activities they intend to use with their students. This has proven difficult to do. Suggestions toward this goal are coveted.

Moment to celebrate

I was happy that I remembered to discuss the idea of taking estimates (albeit after the fact). I am not sure if making this mistake of ordering helped or hurt uptake to the idea of taking estimates before attempting a problem. I was proud that I was able to pull out of a single participants solution strategy to ask for another approach.

Missed opportunity

I intended to have multiple groups share-out after a period of small group work, and only had one group share. Then I shared my own approach. I think I made this adjustment because folks hadn't really gone down the path I intended. In retrospect, I should have shared a summary of my own approach before the group work so that the teachers had a clearer idea of what was expected of them. The conversations were too general and un-focused. Many teachers didn't talk about their lesson or unit planning at all, and the time felt less productive than it could have been. I also think that my circulating, listening, and probing was not where I want it to be in terms of active listening and pulling the nuggets from multiple groups in a short time.

What surprised me?

I was surprised at how quickly we used up the time and how little of my talk we got through. I plan to break it up into a few parts and try to deliver a more focused session on open questions later this year. My goals are the same, but my main evidence for success will be teachers leaving the session with problems they actually use in their classrooms, that have a fresh new feel thanks to our session.

Do the Math

What do folks mean when they say, "do the math"?

How about, "I'm not a math person"?

For what, by and large, do folks (randomly sampled from the entire population of English speakers who know the word math) mean by math?

In a recent survey of LAUSD elementary teachers, most agreed with the statement "Some students have a natural talent for mathematics and others do not." There is a camp of math educators who disagree with this statement, but it seems they are in the minority. Is it possible that the primary confound in this conundrum is differing definitions of math?

To me, doing math means much more than the arithmetic that comes once a solution strategy has been followed to its natural end. Once a complex problem becomes a simple computation, the math is done, and the computers have homework.

I recently presented for the California Mathematics Project at Cal Poly Pomona and had this to say,

What are some things that the computer can't do that I am going to continuously do with my students to get them to reimagine what math is?

Over the course of two hours, I tried to make the point to a group of primary teachers that math is not about getting correct answers, rather it is about discovering patterns, the structure of solution strategies and critiquing each others reasoning.

Watching the entire video and writing this post, I realized some unstated goals:

    Demonstrate how math is a creative process
    Create dissonance around what it means to "Do the math"
    Create dissonance around what it means to be or not to be a "Math person"

If you or someone you know has a similar goal for their students, parents, or teachers; what are some strategies or suggestions you have to create such experiences for folk? I'd love to discuss ways to measure progress toward these goals in the comments.

This post is mainly a recapitulation of Lockhart's Lament and a resounding "hear hear" to Conrad Wolfram's 2010 TED, Teaching Kids Real Math.

SALVIATI: If everyone were exposed to mathematics in its natural state, with all the challenging fun and surprises that that entails, I think we would see a dramatic change both in the attitude of students toward mathematics, and in our conception of what it means to be “good at math.” We are losing so many potentially gifted mathematicians— creative, intelligent people who rightly reject what appears to be a meaningless and sterile subject. They are simply too smart to waste their time on such piffle.

Yet even though we have read and watched these ideas, think to yourself the next time you say or hear someone use the phrase "do the math" or "I'm not a math person": has anything changed... yet?

Written for Sam Shah's Virtual Conference of Mathematical Flavors

Learn Data Science 2018

I am building a portfolio on a new blog to apply to Data jobs in 2018. 

from letustweak

I would like to use Data Science to support teachers, students, and learning in the information age.

    I combed through resources and am sharing highlights.

What is Data Science?

As described in a recent DataCamp podcast with David Robinson,

Data Transformation - how can I clean this and reshape it?
Statistical inference - how can I separate signal from noise?
Prediction - I've got some inputs, how can I predict the outputs? (a classifier)
Visualization - how can I better understand this? (making graphs)
Communication - how can I share the results?

Where I am learning it?

I am reading help pages, documentation, github repos, and stackoverflow to analyze open data sets. 

I did EliteDataScience's 7 day email introduction and feel confident about learning all of their Machine Learning steps through projects, online resources, and Kaggle's courses + competitions.

Python

- All courses with Dr. Chuck Severance at Python for Everybody.
- Practical Deep Learning for Coders with Jeremy Howard at fast.ai.
- Exercises on Project Euler

SQL 

- Exercises on codeacademy, hackerrank, and leetcode.

R

- Tidyverse (Hadley Whickam's R for Data Science)
- Rachel Tatman's Data Science in R Kaggle course

D3

- freeCodeCamp has a beta site offering 6 modules
- Blocks contain examples

More Resources

- DataCamp has some great tutorials in R, python, SQL, and others.
- Andrew Ng's Machine Learning Coursera course (old Octave back when it was free).
- Version Control with Git Udacity course
- MITx Introduction to Probability: The Science of Uncertainty
- TypingClub is not a crime

Online curricula

- David Venturi's DIY Masters (He did some blog updates in 2017)

- Open Source Data Science Masters

Community

I joined the Coursera Data Science community.
I attended various free Data Science meetups in SF at Galvanize and Metis.
I am stalking many Data Scientist blogposts and podcasts to keep up with trends and learn proper etiquette.
Looking for an in-person group of DIY Data Scientists who want to meet up and hack on data sets together.

Stretch Goals

- Kirill Eremenko's Data Science A-Z Udemy course
- Udacity Data Analyst Nanodegree

- crush

Hit me up if you are DIY Data Science learning also.