Category: Benjamin Peterson

Redesigning Seminar

Posted on Author Ben Peterson

Discussing how this seminar could be better built is tough, as I feel the way which it was taught this semester was extremely productive and successful. This being said, I feel redesigning it to be taught the final way suggested in this prompt, with half a semester of coding, and half a semester of working with community partners, could be a positive change.

In the way this seminar was taught, it had its students learning tools as they went along, and not  always having the skills to utilize every tool to the fullest extent. I feel this could be easily remedied by giving some basics on javascript code, html code, and the wide variety of options to visualize data, prior to actually working on application of these skills. Additionally, once this first task is completed, the second half of the seminar would be solely work-based, which I think would allow for strong relationships with community partners, and better emulate a highly-focused work environment.

I believe the seminar worked extremely well with the way which it was taught this past semester, however, I feel getting deeper into the code the first quarter, and working solely on implementing it the second quarter, could be a better option.

Transition Plan Ed Studies

Posted on Author Ben Peterson

Rachel,

I write this with the intent to create the most seamless transition possible from me working with data visualizations to not working with data visualizations, and to help integrate what I’ve created into your future powerpoint slides, and lesson plans.

Fortunately, all the visualizations and graphs I’ve been working on are sourced with html code, which means they will be easily embedded onto any web based page you need them to be. I believe it would be best to use the iframe plugin in most instances to do this most effectively, which instructions are posted for here: http://epress.trincoll.edu/dataviz/chapter/embed-iframe/

I believe all URL’s for my visualizations are openly available as well, and from this can be simply embedded via iframe.

Additionally, since all the html code for my visualizations are publicly available, they can be easily forked, edited and hosted to update, or change for any other applicable lesson plans. A simple intuitive version as to how one may edit an html file to insert appropriate data can be found here: https://github.com/JackDougherty/gviz-scatter-series/blob/master/index.html,(lines beginning with // represent instructions as to how to properly modify).

I hope to meet with you in the next few weeks to finalize how to properly meet your future data visualization needs. That being said, I hope the instructions presented here are a good start.

Best,

Ben

Educational Studies Visualizations. Income and Educational Achievement

Posted on Author Ben Peterson

The following visualizations all can be categorized as visualizations which examine the factors of educational attainment. For these specifically, we will look namely at the influence of income on scholastic achievement

This first visualization shows the most striking trend which has long been discussed and debated. This is the clearly strong correlation between income, and educational attainment.

The correlation coefficient, which is about .8 in this case, is close to a perfect correlation of 1. This means that the data is very nearly fit to a line with a constant slope, deviating very little from the trend of income to test scores. While this graph is very telling, it’s important to note that this does not show complete causation. Other factors often play roles in trends like these, and one way to show the significance of a specific factor is to discount the specific significance of others.

In light of this fact, this following visualization explores the impact of geography on test scores, and examines what impact, if any it would have on the first trend we examined.

Seeing as this data is less concrete, it is nearly difficult to unequivocally estimate fixed trends. This being said, to the naked eye, at least in the instance of the Greater Hartford Area, there appears to be little to no correlation between geography and scholastic achievement. The case could be made that areas of poverty and thus low test scores are more prevalent near the big city, but this information would not be sufficiently backed with our given data.

Now it’s important to recognize that though the data presented above is specific, plentiful, and convincing, it only deals with a confined area of Connecticut. Who’s to say that these trends wouldn’t look completely different elsewhere in the nation?

This final visualization looks at a more widespread measure of scholastic aptitude, which is the SAT.

This visualization shows a similar trend, with all three areas of the SAT having the top socioeconomic group score more than 100 points higher than the bottom socioeconomic group.

The above visualizations have given an inside look as to the ability data visualizations have, and how they can be an enhancement to any presentation.

I hope you all enjoyed this!

Assignment 9 Writing about data

Posted on Author Ben Peterson

The question has often been asked, what outside factors influence educational proficiency. The following visualizations and facts presented take a look at what some of these factors may potentially be.

This first visualization shows the most striking trend which has long been discussed and debated. This is the clearly strong correlation between income, and educational attainment.

This correlation coefficient, which is about .8 in this case, is close to a perfect correlation of 1. This means that the data is very nearly fit to a line with a constant slope, deviating very little from the trend of income to test scores. While this graph is very telling, it isn’t an end-all to the education debate.

The following visualization explores the impact of geography on test scores.

Seeing as this data is less concrete, it is nearly difficult to unequivocally estimate fixed trends. This being said, to the naked eye, at least in the instance of the Greater Hartford Area, there appears to be little to no correlation between geography and scholastic achievement. The case could be made that the areas of poverty and thus low test scores are more prevalent in the city, but this information would not be sufficiently backed with our given data.

We are able to see the tip of the iceberg with these above visualizations, but what lies in the ocean is a whole other story.

Assignment 8

Posted on Author Ben Peterson

The two data visualizations below present the same date in very different ways. Both aim to give a geographical visualization of individuals over the age of 25 in given Connecticut Counties who reported never graduated High School. The first shows a more narrow spectrum of the data by only separating the various percentages of those not graduating high school into two different categories, while the second visualization shows three different categories in color all of which are similar. The first would suggest these counties are very different and divided, while the second would suggest they are very alike. One has to be a lie, so this would suggest that we’ve accomplished our goal in lying with maps.