One important component of Mobilize is to teach the teachers statistical reasoning. This is important because (a) the Mobilize content is mostly involved with using data analysis as a pathway for teaching math and science and (b) the Common Core (math) and the Next Generation (science) standards include much more statistics than previous curricula. And yet, at least for math teachers, data analysis is not part of their education.

And so I was looking forward to seeing how the teachers performed on the “rank the airlines” Model Eliciting Activity, which was designed by the CATALYST project, led by Joan Garfield at U of Minnesota. (Unit 2, Lesson 9 from the CATALYST web site.) Model Eliciting Activities (MEA) are a lesson design which I’m getting really excited about, and trying to integrate into more of my own lessons. Essentially, groups of students are given realistic and complex questions to answer. The key is to provide some means for the student groups to evaluate their own work, so that they can iterate and achieve increasingly improved solutions. MEAs began in the engineering-education world, and have been used increasingly in mathematics both at college and high school and middle school levels. (A good starting point is “Model-eliciting activities (MEAs) as a bridge between engineering education research and mathematics education research”, HamiIton, Lesh, Lester, Brilleslyper, 2008. Advances in Engineering Education.) I was first introduced to MEAs when I was an evaluator for the CATALYST project, but didn’t really begin to see their potential until Joan Garfield pointed it out to me while I was trying to find ways of enhancing our Mobilize curriculum.

In the MEA we presented to the teachers on Saturday, they were shown data on arrival time delays from 5 airlines. Each airline had 10 randomly sampled flights into Chicago O’Hare from a particular year. The primary purpose of the MEA is to help participants develop informal ways for comparing groups when variability is present. In this case, the variability is present in an obvious way (different flights have different arrival delays) as well as less obvious ways (the data set is just one possible sample from a very large population, and there is sample-to-sample variability which is invisible. That is, you cannot see it in the data set, but might still use the data to conjecture about it.)

Before the PD I had wondered if the math and science teachers would approach the MEA differently. Interestingly, during our debrief, one of the math teachers wondered the same thing. I’m not sure if we saw truly meaningful differences, but here are some things we did see.

Most of the teams immediately hit on the idea of struggling to merge both the airline accuracy and the airline precision into their ranking. However, only two teams presented rules that used both. Interestingly, one used precision (variability) as the primary ranking and used accuracy (mean arrival delay) to break ties; another group did the opposite.

At least one team ranked only on precision, but developed a different measure of precision that was more relevant to the problem at hand: the mean absolute deviations from 0 (rather than deviations from the mean).

One of the more interesting things that came to my attention, as a designer or curriculum, was that almost every team wrestled with what to do with outliers. This made me realize that we do a lousy job of teaching people what to do with outliers, particularly since outliers are not very rare. (One could argue whether, in fact, any of the observations in this MEA are outliers or not, but in order to engage in that argument you need a more sophisticated understanding of outliers than we develop in our students. I, myself, would not have considered any of the observations to be outliers.) For instance, I heard teams expressing concern that it wasn’t “fair” to penalize an airline that had a fairly good mean arrival time just because of one bad outlier. Other groups wondered if the bad outliers were caused by weather delays and, if so, whether it was fair to include those data at all. I was very pleased that no one proposed an outright elimination of outliers. (At least within my hearing.) But my concern was that they didn’t seem to have constructive ways of thinking about outliers.

The fact that teachers don’t have a way of thinking about outliers is our fault. I think this MEA did a great job of exposing the participants to a situation in which we really had to think about the effect of outliers in a context where they were not obvious data-entry errors. But I wonder how we can develop more such experiences, so that teachers and students don’t fall into procedural-based, automated thinking. (e.g. “If it is more than 1.5 times the IQR away from the median, it is an outlier and should be deleted.” I have heard/read/seen this far too often.)

Do you have a lesson that engages students in wrestling with outliers? If so, please share!

]]>I’m curious to see what they come up with, particularly whether the math teachers differ terribly from the science teachers. The math teachers are further along in our weekend professional development program than are the science teachers, and so I’m hoping they’ll identify the key characteristics of a distribution (all together: center, spread, shape; well, shape doesn’t play much of a role here) and use these to formulate their rankings. We’ve worked hard on helping them see distributions as a unit, and not a collection of individual points, and have seen big improvements in the teachers, most of whom have not taught statistics before.

The science teachers, I suspect, will be a little bit more deterministic in their reasoning, and, if true to my naive stereotype of science teachers, will try to find explanations for individual points. Since I haven’t worked as much with the science teachers, I’m curious to see if they’ll see the distribution as a whole, or instead try to do point-by-point comparisons.

When we initially started this project, we had some informal ideas that the science teachers would take more naturally to data analysis than would the math teachers. This hasn’t turned out to be entirely true. Many of the math teachers had taught statistics before, and so had some experience. Those who hadn’t, though, tended to be rather procedurally oriented. For example, they often just automatically dropped outliers from their analysis without any thought at all, just because they thought that that was the rule. (This has been a very hard habit to break.)

The math teachers also had a very rigid view of what was and was not data. The science teachers, on the other hand, had a much more flexible view of data. In a discussion about whether photos from a smart phone were data, a majority of math teachers said no and a majority of science teachers said yes. On the other hand, the science teachers tend to use data to confirm what they already know to be true, rather than use it to discover something. This isn’t such a problem with the math teachers, in part because they don’t have preconceptions of the data and so have nothing to confirm. In fact, we’ve worked hard with the math teachers, and with the science teachers, to help them approach a data set with questions in mind. But it’s been a challenge teaching them to phrase questions for their students in which the answers aren’t pre-determined or obvious, and which are empirically oriented. (For example: We would like them to ask something like “what activities most often led to our throwing away redcycling into the trash bin?” rather than “Is it wrong to throw trash into the recycling bin?” or “Do people throw trash into the recycling bin?”)

So I’ll report back soon on what happened and how it went.

]]>Year | Books | Pages |
---|---|---|

2011 | 45 | 15,332 |

2012 | 29 | 9,203 |

2013 | 45 | 15,887 |

2014 | 46 | 17,480 |

Since I have accumulated four years worth of data, I thought I might do some comparative analysis of my reading over this time period.

The trend displayed here was somewhat surprising when I looked at it—at least related to the decline in reading over the summer months. Although, reflecting on it, it maybe should not have been as surprising. There is a slight uptick around the month of May (when spring semester ends) and the decline begins in June/July. Not only do summer classes begin, but I also try to do a few house and garden projects over the summer months. This uptick and decline are still visible when a plot of the number of pages (rather than the number of books) is examined, albeit much smaller (1,700 pages in May and 1,200 pages in the summer months). This might indicate I read longer books in the summer. For example, one of the books I read this last summer was Neal “I don’t know the meaning of the word ‘brevity'” Stephenson’s *Reamde*, which clocked in at a mere 1,044 pages.

I also plotted my monthly average rating (on a five-point scale) for the four years of data. This plot shows that 2014 is an anomaly. I apparently read trash in the summer (which is what you are supposed to do). The previous three years I read the most un-noteworthy books in the fall. Or, I just rated them lower because school had started again.

I also looked at how other GoodReads readers had rated those same books. The months represent when I read the book. (I didn’t look at when the book was read by other readers, although that would be interesting to see if time of year has an effect on rating.) The scale on the *y*-axis is the residual between my rating and the average GoodReads rating. My ratings are generally close to the average, sometimes higher, sometimes lower. There are, however, many books that I rated much lower than average. The loess smooth suggests that July–November is when I am most critical relative to other readers.

I will give a bit of review of some of the books that I read this semester related to statistics. Most recently, I finished Hands-On Matrix Algebra Using R: Active and Motivated Learning with Applications. This was a fairly readable book for those looking to understand a bit of matrix algebra. The emphasis is definitely in economics, but their are some statistics examples as well. I am not as sure where the “motivated learning” part comes in, but the examples are practical and the writing is pretty coherent.

The two books that I read that I am most excited about are Model Based Inference in the Life Sciences: A Primer on Evidence and The Psychology of Computer Programming. The latter, written in the 70’s, explored psychological aspects of computer programming, especially in industry, and on increasing productivity. Weinberg (the author) stated his purpose in the book was to study “computer programming as a human activity.” This was compelling on many levels to me, not the least of which is to better understand how students learn statistics when using software such as R.

Reading this book, along with participating in a student-led computing club in our department has sparked some interest to begin reading the literature related to these ideas this spring semester (feel free to join us…maybe we will document our conversations as we go). I am very interested in how instructor’s choose software to teach with (see concerns raised about using R in Harwell (2014). Not so fast my friend: The rush to R and the need for rigorous evaluation of data analysis and software in education. *Education Research Quarterly*.) I have also thought long and hard about not only what influences the choice of software to use in teaching (I do use R), but also about subsequent choices related to that decision (e.g., if R is adopted, which R packages will be introduced to students). All of these choices probably have some impact on student learning and also on students’ future practice (what you learn in graduate school is what you ultimately end up doing).

The Model Based Inference book was a shorter, readable version of Burnham and Anderson’s (2003) Springer volume on multimodel inference and information theory. I was introduced to these ideas when I taught out of Jeff Long’s, Longitudinal Data Analysis for the Behavioral Sciences Using R. They remained with me for several years and after reading Anderson’s book, I am going to teach some of these ideas in our advanced methods course this spring.

Anyway…just some short thoughts to leave you with. Happy Holidays.

]]>Samuel Wilcock (Messiah College) talked about how while IRBs are not required for data collected by students for class projects, the discussion of ethics of data collection is still necessary. While IRBs are cumbersome, Wilcock suggests that as statistic teachers we ought to be aware of the process of real research and educating our students about the process. Next year he plans to have all of his students go through the IRB process and training, regardless of whether they choose to collect their own data or use existing data (mostly off the web). Wilcock mentioned that, over the years, he moved on from thinking that the IRB process is scary to thinking that it’s an important part of being a stats educator. I like this idea of discussing in the introductory statistics course issues surrounding data ethics and IRB (in a little more depth than I do now), though I’m not sure about requiring all 120 students in my intro course to go through the IRB process just yet. I hope to hear an update on this experiment next year from to see how it went.

Next, Shannon McClintock (Emory University) talked about a project inspired by being involved with the honor council of her university, when she realized that while the council keeps impeccable records of reported cases, they don’t have any information on cases that are not reported. So the idea of collecting student data on academic misconduct was born. A survey was designed, with input from the honor council, and Shannon’s students in her large (n > 200) introductory statistics course took the survey early on in the semester. The survey contains 46 questions which are used to generate 132 variables, providing ample opportunity for data cleaning, new variable creation (for example thinking about how to code “any” academic misconduct based on various questions that ask about whether a student has committed one type of misconduct or another), as well as thinking about discrepant responses. These are all important aspects of working with real data that students who are only exposed to clean textbook data may not get a chance practice. It’s my experience that students love working with data relevant to them (or, even better, about them), and data on personal or confidential information, so this dataset seem to hit both of those notes.

Using data from the survey, students were asked to analyze two academic outcomes: whether or not student has committed any form of academic misconduct and an outcome of own choosing, and presented their findings in n optional (some form of extra credit) research paper. One example that Shannon gave for the latter task was defining a “serious offender”: is it a student who commits a one time bad offense or a student who habitually commits (maybe nor so serious) misconduct? I especially like tasks like this where students first need to come up with their own question (informed by the data) and then use the same data to analyze it. As part of traditional hypothesis testing we always tell students that the hypotheses should not be driven by the data, but reminding them that research questions can indeed be driven by data is important.

As a parting comment Shannon mentioned that the administration at her school was concerned that students finding out about high percentages of academic offense (survey showed that about 60% of students committed a “major” academic offense) might make students think that it’s ok, or maybe even necessary, to commit academic misconduct to be more successful.

For those considering the feasibility of implementing a project like this, students reported spending on average 20 hours on the project over the course of a semester. This reminded me that I should really start collecting data on how much time my students spend on the two projects they work on in my course — it’s pretty useful information to share with future students as well as with colleagues.

The last talk I caught in this session was by Mary Gray and Emmanuel Addo (American University) on a project where students conducted an exit poll asking voters whether they encountered difficulty in voting, due to voter ID restrictions or for other reasons. They’re looking for expanding this project to states beyond Virginia, so if you’re interested in running a similar project at your school you can contact Emmanuel at addo@american.edu. They’re especially looking for participation from states with particularly strict voter ID laws, like Ohio. While it looks like lots of work (though the presenters assured us that it’s not), projects like these that can remind students that data and statistics can be powerful activism tools.

]]>During the Q&A, one of the critiques of the project was that they had displayed the data as a donut plot. “Pie charts (or any kin thereof) = bad” was the message. I don’t really want to fight about whether they are good, nor bad—the reality is probably in between. (Tufte, the most cited source to the ‘pie charts are bad’ rhetoric, never really said pie charts were bad, only that given the space they took up they were, perhaps less informative than other graphical choices.) Do people have trouble reading radians? Sure. Is the message in the data obscured because of this? Most of the time, no.

Here, is the bar chart (often the better alternative to the pie chart that is offered) and the donut plot for the data shown in the Mobilize dashboard screenshot? The message is that most of the advertisements were from posters and billboards. If people are interested in the *n*‘s, that can be easily remedied by including them explicitly on the plot—which neither the bar plot nor donut plot has currently. (The dashboard displays the actual numbers when you hover over the donut slice.)

It seems we are wasting our breath constantly criticizing people for choosing pie charts. Whether we like it or not, the public has adopted pie charts. (As is pointed out in this blog post, Leland Wilkinson even devotes a whole chapter to pie charts in his Grammar of Graphics book.) Maybe people are reasonably good at pulling out the often-not-so-subtle differences that are generally shown in a pie chart. After all, it isn’t hard to understand (even when using a 3-D exploding pie chart) that the message in this pie chart is that the “big 3″ browsers have a strong hold on the market.

The bigger issue to me is that these types of graphs are only reasonable choices when examining simple group differences—the marginals. Isn’t life, and data, more complex than that?Is the distribution of browser type the same for Mac and PC users? For males and females? For different age groups? These are the more interesting questions.

The dashboard addresses this through interactivity between the multiple donut charts. Clicking a slice in the first plot, shows the distribution of product types (the second plot) for those ads that fit the selected slice—the conditional distributions.

So it is my argument, that rather than referring to a graph choice as good or bad, we instead focus on the underlying question prompting the graph in the first place. Mobilize acknowledges that complexity by addressing the need for conditional distributions. Interactivity and computing make the choice of pie charts a reasonable choice to display this.

*If those didn’t persuade you, perhaps you will be swayed by the food argument. Donuts and pies are two of my favorite food groups. Although bars are nice too. For a more tasty version of the donut plot, perhaps somebody should come up with a cronut plot.

**The ggplot2 syntax for the bar and donut plot are provided below. The syntax for the donut plot were adapted from this blog post.

# Input the ad data ad = data.frame( type = c("Poster", "Billboard", "Bus", "Digital"), n = c(529, 356, 59, 81) ) # Bar plot library(ggplot2) ggplot(data = ad, aes(x = type, y = n, fill = type)) + geom_bar(stat = "identity", show_guide = FALSE) + theme_bw() # Add addition columns to data, needed for donut plot. ad$fraction = ad$n / sum(ad$n) ad$ymax = cumsum(ad$fraction) ad$ymin = c(0, head(ad$ymax, n = -1)) # Donut plot ggplot(data = ad, aes(fill = type, ymax = ymax, ymin = ymin, xmax = 4, xmin = 3)) + geom_rect(colour = "grey30", show_guide = FALSE) + coord_polar(theta = "y") + xlim(c(0, 4)) + theme_bw() + theme(panel.grid=element_blank()) + theme(axis.text=element_blank()) + theme(axis.ticks=element_blank()) + geom_text(aes(x = 3.5, y = ((ymin+ymax)/2), label = type)) + xlab("") + ylab("")

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Webster West took the High Statistician point of view—one shared by many, including, on a good day, myself: Data Science consists of those things that are involved in analyzing data. I think most statisticians when reading this will feel like Moliere’s Bourgeois Gentleman, who was pleasantly surprised to learn he’d been speaking prose all his life. But I think there’s more to it then that, because probably many statisticians don’t consider data scraping, data cleaning, data management as part of data analysis.

Nick Horton offered that data mining was an activity that could be considered part of data science. And he sees data mining as part of statistics. Not sure all statisticians would agree, since for many of us, data mining is a swear word used to refer to people who are lucky enough to discover something but have no idea why it was discovered. But he also offered a broader definition: using data to answer a statistical question. Which I quite like. It leaves open the door to many ways of answering the question; it doesn’t require any particular background or religion, it simply means that those activities used to bring data to bear in answering a statistical question.

Bill Finzer relied on set theory: data science is a partial union of math and statistics, subject matter knowledge, and computational thinking and programming in the service of making discoveries from data. I’ve seen similar definitions and have found such a definition to be very useful in thinking about curriculum for a high school data science course. It doesn’t contradict Nick’s definition, but is a little more precise. As always, Bill has a knack for phrasing things just right without any practice.

Deb Nolan answered last, and I think I liked her answer the best. Data science encompasses the entire data analysis cycle, and addresses the issue you face in terms of working with data within that cycle, and the skills needed to complete that cycle. (I like to use this simplified version of the cycle: ask questions–>collect/consider/prepare data –>analyze data–> interpret data–>ask questions, etc.)

One reason I like Deb’s answer is that its the answer we arrived at in our Mobilize group that’s developing the Introduction to Data Science curriculum for Los Angeles Unified School District. (With a new and improved webpage appearing soon! I promise!) Lots of computational skills appear explicitly in the collect/prepare data bit of the cycle, but in fact, algorithmic thinking — thinking about processes of reproducibility and real-time analyses–can appear in all phases.

During this talk I had an epiphany about my own feelings towards a definition. The epiphany was sparked by an earlier talk by Daniel Frischemeier on the previous day, but brought into focus by this panel’s discussion. (Is it possible to have a slow epiphany?)

Statistics educators have been big proponents of teaching “statistical thinking”, which is basically an approach to solving problems that involve uncertainty/variation and data. But for many of us, the bit of problem solving in which a computer is involved is ignored in our conceptualization of statistical thinking. To some extent, statistical thinking is considered to be independent of computation. We’d like to think that we’d reach the same conclusions regardless of which software we were using. While that’s true, I think it’s also true that our approach to solving the problem may be software dependent. We think differently with different softwares because different softwares enable different thought processes, in the same way that a pen and paper enables different processes then a word processor.

And so I think that we statisticians become data scientists the moment we reconceptualize statistical thinking to include using the computer.

What does this have to do with Daniel’s talk? Daniel has done a very interesting study in which he examined the problem solving approach of students in a statistics class. In this talk, he offered a model for the expert statistician problem solving process. Another version of the data analysis cycle, if you will. His cycle (built solidly on foundations of others) is Real Problem –> Statistical activity –> Software use–> Reading off/Documentation (interpreting) –> conclusions –> reasons (validation of conclusions)–> back to beginning.

I think data scientists are those who would think that the “software use” part of the cycle was subsumed by the statistical activity part of the cycle. In other words, when you approach data cleaning, data organizing, programming, etc. as if they were a fundamental component of statistical thinking, and not just something that stands in the way of your getting to real data analysis, then you are doing data science. Or, as my colleague Mark Hansen once told me, “Teaching R *is* teaching statistics.” Of course its possible to teach R so that it seems like something that gets in the way of (or delays) understanding statistics. But it’s also possible to teach it as a complement to developing statistical understanding.

I don’t mean this as a criticism of Daniel’s work, because certainly it’s useful to break complex activities into smaller parts. But I think that there is a figure-and-ground issue, in which statisticians have seen modeling and data analysis as the figure, and the computer as the ground. But when our thinking unites these views, we begin to think like data scientists. And so I do not think that “data science” is just a rebranding of statistics. It is a re-consideration of statistics that places greater emphasis on parts of the data cycle than traditionally statistics has placed.

I’m not done with this issue. The term still bothers me. Just what is the science in data science? I feel a refresher course in Popper and Kuhn is in order. Are we really thinking scientifically about data? Comments and thoughts welcome.

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