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ELA: There are more sobering notes among the n-grams. For instance, here's the trajectory of Marc Chagall, an artist born in 1887. And this looks like the normal trajectory of a famous person. He gets more and more and more famous, except if you look in German. If you look in German, you see something completely bizarre, something you pretty much never see, which is he becomes extremely famous and then all of a sudden plummets, going through a nadir between 1933 and 1945, before rebounding afterward. And of course, what we're seeing is the fact Marc Chagall was a Jewish artist in Nazi Germany. Now these signals are actually so strong that we don't need to know that someone was censored. We can actually figure it out using really basic signal processing. Here's a simple way to do it. Well, a reasonable expectation is that somebody's fame in a given period of time should be roughly the average of their fame before and their fame after. So that's sort of what we expect. And we compare that to the fame that we observe. And we just divide one by the other to produce something we call a suppression index. If the suppression index is very, very, very small, then you very well might be being suppressed. If it's very large, maybe you're benefiting from propaganda.

Now when Google digitizes a book, they put it into a really nice format. Now we've got the data, plus we have metadata. We have information about things like where was it published, who was the author, when was it published. And what we do is go through all of those records and exclude everything that's not the highest quality data. What we're left with is a collection of five million books, 500 billion words, a string of characters a thousand times longer than the human genome -- a text which, when written out, would stretch from here to the Moon and back 10 times over -- a veritable shard of our cultural genome.

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One of the best examples I have seen of the flex model was in Morgan Hill, California. This is a district south of San Jose where about a third of its students are Hispanic and I believe over a third of its students are on free-and-reduced price lunch. The school is called the Silicon Valley Flex Academy - Grades 6 through 12. As you walk into the school there are a couple of huge open spaces on either side where every student has his/her own office. In this space, each student has his/her own computer. The students are encouraged to decorate their own space with things they like (in the same way an adult might decorate an office at work). There are break out classrooms around the perimeter of the building. Here teachers are getting the data on how the kids are doing. Teachers can pull students into these break-out classrooms in very small groups. The teacher is then able to focus on a student's individual issues. The teacher's job is totally different in this arrangement. The fascinating thing was how much ownership the students have over their learning. They all knew exactly what was expected of them the entire year. They knew exactly how they were doing at any point. Their job was to learn the material. If they could get the work done during the school day there was no homework. So it was up to the individual students to make those decisions.

The teachers I spoke to explained that they had been trained to do lesson planning, lectures to large groups of students and classroom management -- none of which they were now doing. They explained that the adjustment was difficult. Training has not been built into the formal teacher training system for programs like this, and few are really thinking about it at the moment. Now, the teacher is still doing teaching or tutoring when pulling students out into small groups for project-based work, but instead of this being determined by a pacing guide, this is now being determined by where the students are in their learning. What was so interesting was that in this model, teachers were able to do the tutoring and value enrichment work that teachers really like to do but don't always get time to do in a classroom. One of the challenges the teachers mentioned was staying on top of scheduling. How do you keep track when you have students at different places in the curriculum? Those were tough decisions for teachers to make and they were, as you say, learning on the job.

When students own their learning, they feel responsible for it and motivated to do it. What they also appreciated was that the teacher was no longer there to "punish them" or "grade them down". Instead the teacher was there to help them reach their goal. This is much more of an environment built around success and motivation versus failure.

Causes of Academic Dishonesty from literature: Craig, Federici & Buehler, 2010 Academic - assessment design - education about academic dishonesty - poor understanding of citation styles - “poor understanding of the proper use of intellectual property”

Ethical - cutting corners - work ethics - cultureal differences Personal - personal maturity - “poor time management skills” - “new to college experience” Academic dishonesty can be defined as “anything with gives students an unearned advantage academically” - see Hart and Morgan, 2010

We also use TurnItIn.com Encourage professors to use questions from randomized question blocks Provide resources - Writing Center - Library Resources - Endnote

Really nice. Be great to see the two combined – heatmaps and topography or atleast some kind of colour banding added to the topography. That would open up all kinds of possibilities – you could slice horizontally along the bands and create layers of different ranges. In fact mixing colour and topography would also give you a way of showing two sets of data concurrently – topography for prostitution and some kind of colour banding for wealth for example.

Makes the numbers come alive. G

Brilliant work! Can you cross this data with the physical typography? I’ve always been curious if safer neighborhoods are uphill.