My JSM 2017 itinerary

JSM 2017 is almost here. I just landed in Maryland, and I finally managed to finish combing through the entire program. What a packed schedule! I like writing an itinerary post each year, mainly so I can come back to it during and after the event. I obviously won’t make it to all sessions listed for each time slot below, but my decision for which one(s) to attend during any time period will likely depend on proximity to previous session, and potentially also proximity to childcare area.

The focus of the sessions I selected are education, data science, computing, visualization, and social responsibility. In addition to talks on topics I actively work in, I also enjoy listening to talks in application areas I’m interested in, hence the last topic on this list.

If you have suggestions for other sessions (in these topics or other) that you think would be interested, let me know in the comments!

Sun, 7/30/2017

Sunday will be mostly meetings for me, and I’m skipping any evening stuff to see Andrew Bird & Belle and Sebastian!

Mon, 7/31/2017

  • DataFest meeting: 10am – 12pm at H-Key Ballroom 9. Stop by if you’re already an ASA DataFest organizer, or if you’d like to be one in the future!
    • First hour will be discussing what worked and what didn’t, any concerns, kudos, advice for new sites, etc.
    • Second hour will be drop-in for addressing any questions regarding organizing an ASA DataFest at your institution.
  • Computing and Graphics mixer: 6 – 8pm at H-Key Ballroom 1.
  • Caucus for Women in Statistics Reception and Business Meeting: 6:30 – 8:30pm at H-Holiday Ballroom 1&2.

8:30 AM – 10:20 AM

10:30 AM – 12:20 PM

2:00 PM – 3:50 PM

4:00 PM – 5:50 PM

ASA President’s Invited Speaker: It’s Not What You Said. It’s What They Heard – Jo Craven McGinty, The Wall Street Journal

Tue, 8/1/2017

8:30 AM – 10:20 AM

10:30 AM – 12:20 PM

2:00PM – 3:50 PM

4:00 PM – 5:50 PM

Deming Lecture: A Rake’s Progress Revisited – Fritz Scheuren, NORC-University of Chicago

Wed, 8/2/2017

  • Statistical Education Business Meeting – 6-7:30pm

8:30 AM – 10:20 AM

10:30 AM – 12:20 PM

2:00PM – 3:50 PM

4:00 PM – 5:50 PM

COPSS Awards and Fisher Lecture: The Importance of Statistics: Lessons from the Brain Sciences – Robert E. Kass, Carnegie Mellon University

Thur, 8/3/2017

8:30 AM – 10:20 AM

 10:30 AM – 12:20 PM

Structuring Data in Middle School

Of the many provocative and exciting discussions at this year’s Statistics Research Teaching and Learning conference in Rotarua, NZ, one that has stuck in my mind is from Lucia Zapata-Cardona, from the Universidad de Antioquia in Columbia. Lucia discussed data from her classroom observations of a teacher at a middle school (ages 12-13) in a “Northwest Columbian city”. The class was exciting for many reasons, but the reason that I want to write about it here is because of the fact that the teacher had the students structure and store their own data.

The classroom was remarkable – to my American eyes – for the large number of students (45) and for the noise (walls were thin, the playground was immediately outside, and windows were kept open because of the heat.) Despite this, the teacher led an inquiry-based discussion, skillfully prompting the students with questions from the back of the classroom. The discussion lasted over several days.

The students had collected data about the nutritional content of the foods they eat. Challenging students with real-world, meaningful problems is an important part of Prof. Zapata-Cardona’s research, since an important goal of education is to tie the world of the classroom to the real world. Lucia was interested in examining how (and whether) the students constructed and employed statistical models to reason with the data. (Modeling was the theme of this SRTL.) What fascinated me wasn’t the modeling, but the role that the structure of the data played in the students’ reasoning.

Students were asked to collect data on the food contained in their lunchboxes so that they could answer the statistical question “How nutritious is the food we bring to school in our lunchbox?” It’s important to note that in Columbia, as Lucia explained to us, the “lunch box” doesn’t contain actual lunch (which the students eat at home), but instead includes snacks for during the day. What interested me was that the teacher let the class, after discussion, decide how they would enter and organize the data. Now I’m not sure what parameters/options the students were given. I do know that the classroom had one computer, and students took turns entering the data into this computer. And I know that the students discussed which variables they wanted to store, and how they wanted to store them.

The pivotal decision here was that the students decided that each row would represent a food, for example, Chicle. They decided to record information about serving size, calories, fats, carbs, protein, sodium, sugars, whether it was “processed” (5 g, 18, 0, 5, 0, 0, 0 and si, in case you were curious). They decided not to store information about how many students brought this food, or how many servings any individual student brought.

At this point, you may have realized that their statistical question is rather difficult, if not impossible, to answer given the format in which they stored the data. Had each case of the data been an individual lunchbox or an individual person, then the students might have made headway. Instead, they stumbled over issues about how to compare the total calories of the dataset with the total calories eaten by individuals. (After much discussion, most of the class “discovered” that the average amount was a good way of summarizing the data, but some of the more perceptive students pointed out that it wasn’t clear what the average really meant.)

Lucia’s forthcoming paper will go into the details about the good and the bad in the students’ statistical reasoning, and the ways in which they used (or failed to use) statistical models. But what was fascinating to me was the opportunity this provided for helping students understand how the structure of data affects the questions that we can ask, and how the questions we ask should first consider the structure of the data.

Too often, particularly in textbooks, there is no opportunity to reason about the structure of data. When a question is asked, the students are given appropriate data, and rarely allowed even to decide which variables to consider (since the provided data usually includes only the necessary variables), much less whether or not the data should be restructured or re-collected.

Another reason classrooms have avoided letting students structure their own data is that many real-life datasets have complicated structures. The data these students collected is really (or should have been) hierarchical. If the case is the lunchbox, a lunchbox is associated with a student and possibly with more than 1 item. If data are collected on multiple days, then there is nesting within days as well as the potential for missing variables or unequal record lengths.

Data with such a complicated structure are simply not taught in middle schools, even though, as Lucia’s case study demonstrates, they arise easily from familiar contexts.   These data are messy and complicated. Should we even open this pandora’s box for middle school students, or should it wait until they are older? Is it enough to work with the simplified “flat” format such as the one these students came up with, and just modify the statistical question? Should students be taught how to manipulate such data into different formats to answer the questions they are interested in?

You might think hierarchical formats are beyond the middle school level, but work done by Cliff Konold and Bill Finzer, in the context of using the CODAP tool, suggests that it is possible. [I can’t find an online paper to link to for this result, but there are some leads here, and I’m told it has been approved for publication so should appear soon.]

So the question is: when do we teach students to reason with hierarchical data? When do we teach students to recognize that data can be stored in different formats? When do we teach students to convert data from one format to another?

We are back to the question I asked in my last blog: what’s the learning trajectory that takes statistical beginners and teaches them the computational and statistical tools to allow them to address fundamental questions that rely on data that, on the one hand, are complex but on the other hand are found in our day-to-day lives?

Are computers needed to teach Data Science?

One of the many nice things about summer is the time and space it allows for blogging. And, after a very stimulating SRTL conference (Statistics Reasoning, Teaching and Learning) in Rotorua, New Zealand, there’s lots to blog about.

Let’s begin with a provocative posting by fellow SRTL-er Tim Erickson at his excellent blog A Best Case Scenario.  I’ve known Tim for quite awhile, and have enjoyed many interesting and challenging discussions. Tim is a creator of curricula par excellence, and has first-hand experience in what inspires and motivates students to think deeply about statistics.

The central question here is: Is computation (on a computer) necessary for learning data science? The learners here are beginners in K-12. Tim answers no, and I answer, tentatively, yes. Tim portrays me in his blog as being a bit more steadfast on this position than I really am. In truth the answer is, some; maybe; a little; I don’t know.

My own experience in the topic comes from the Mobilize project  , in which we developed the course Introduction to Data Science for students in the Los Angeles Unified School District. (I’m pleased to say that the course is expanding. This summer, five new L.A.-area school districts will begin training teachers to teach this course. )

The course relies heavily on R via Rstudio. Students begin by studying the structure of data, learning to identify cases and variables and to organize unstructured data into a “tidy” format. Next, they learn to “read” tidy datafiles into Rstudio. The course ends with students learning some predictive modeling using Classification and Regression Trees. In between, they study some inference using randomization-based methods.

To be precise, the students don’t learn straight-up R. They work within a package developed by the Mobilize team (primarily James Molyneux, Amelia McNamara, Steve Nolen, Jeroen Ooms, and Hongsuda Tangmunarunkit) called mobilizR, which is based pretty heavily on the mosaic package developed by Randall Pruim, Danny Kaplan and Nick Horton.  The idea with these packages is to provide beginners to R with a unified syntax and a set of verbs that relate more directly to the analysts’ goals. The basic structure for (almost) all commands is

WhatIWantToDo(yvariable~xvariables, dataset)

For example, to see the average walking distance recorded by a fitbit by day of the week:

 > mean(Distance~DOW,data=fitbitdec)
 Friday Monday Saturday Sunday Thursday Tuesday Wednesday 1.900000 3.690000 2.020909 2.419091 1.432727 3.378182 3.644545

The idea is to provide students with a simplified syntax that “bridges the gap” between beginners of R and more advanced users. Hopefully, this frees up some of the cognitive load required to remember and employ R commands so that students can think strategically and statistically about problems they are trying to solve.

The “bridge the gap” terminology comes from Amelia McNamara, who used the term in her PhD dissertation. One of the many really useful ideas Amelia has given us is the notion that the gap needs to be bridged. Much of “traditional” statistics education holds to the idea that statistical concepts are primarily mathematical, and, for most people, it is sufficient to learn enough of the mathematical concepts so that they can react skeptically and critically to others’ analyses. What is exciting about data science in education is that students can do their own analyses. And if students are analyzing data and discovering on their own (instead of just trying to understand others’ findings), then we need to teach them to use software in such a way that they can transition to more professional practices.

And now, dear readers, we get to the heart of the matter. That gap is really hard to bridge. One reason is that we know little to nothing about the terrain. How do students learn coding when applied to data analysis? How does the technology they use mediate that experience? How can it enhance, rather than inhibit, understanding of statistical concepts and the ability to do data analysis intelligently?

In other words, what’s the learning trajectory?

Tim rightly points to CODAP, the Common Online Data Analysis Platform,  as one tool that might help bridge the gap by providing students with some powerful data manipulation techniques. And I recently learned about data.world, which seems another attempt to help bridge the gap.  But Amelia’s point is that it is not enough to give students the ability to do something; you have to give it to them so that they are prepared to learn the next step. And if the end-point of a statistics education involves coding, then those intermediate steps need to be developing students’ coding skills, as well as their statistical thinking. It’s not sufficient to help studemts learn statistics. They must simultaneously learn computation.

So how do we get there? One important initial step, I believe, is to really examine what the term “computational thinking” means when we apply it to data analysis. And that will be the subject of an upcoming summer blog.