Epic Data Day

Part 1-

Because that’s all I have to do…

Scratch designers consider learning to be some kind of learning of technological literacy, specifically in the form of code. They seem to think that learning is best achieved through experimentation, trial and error. I am learning just how frustrating the next generation will be. I say this, as I do not have very much literacy when it comes to technology, but they will. If this is the kind of game that they are playing in their spare time, and the kind of thing that they are also learning in school, they will know much more than I do. I did not expect to learn anything, as I didn’t really understand what it was. I did end up learning that this is very frustrating, but also fairly easy once a basic understanding is achieved. The designers of Scratch value the understanding of coding and technology as learning, and understanding. Coding is especially valued in today’s society as most of everything that we do is online. We can see this in the new GE Commercial, when the newly hired programer is expected to be a different kind of thing. It is seen as funny because we understand that it is much more likely for a programer to be hired now than some industrial worker.

Data Day 9

Level 1

Screen Shot 2015-11-12 at 2.02.18 PM

Level 2

I would want to find out why certain things are visualized as larger, and certain ones as smaller, and what the relation is to me and each of the tweets that are actually shown.

Question 1: Why are certain tweets visualized as larger or smaller for your particular shot?

Answers:

  • The number of “Likes” the tweets get. But then wonder why it was “liked”: funny, relevant, WHY?
  • Amanda: has to do with who tweets more, who gets more likes, retweets, more interaction
  • Most likes, notes, more attention
  • Sara: my interaction with those tweets or people
  • Mine: Random algorithm

Question 2: What is your relation to the tweets that are larger vs. the ones that are smaller?

Answers:

  • I keep popping up. @npfannen smaller, more frequent, larger with less frequency
  • Amanda: Bigger ones are images that get more attention.
  • some are about the actual class, thought there would be more of the silly ones
  • Sara: the ones I like or interact with more are larger
  • Mine: none, actually. I am strangely popping up a lot.

Reasoning:

I made these questions because this site REALLY confuses me! It seems like it should have rationale, but it doesn’t seem like it actually does. So, I was interested in seeing what other people’s ideas where.

Level 3

Because often we talk to the people at our table a lot more than we do other tables, so it doesn’t give us a complete picture of what the whole class believes.

There aren’t very many implications that matter. All of the information is just out there, so does it really matter if we are looking at it? I don’t think so. If it was more private information, maybe there would be more problems with it. But as public information, it doesn’t seem unethical to be looking at and researching what we are, and specifically this particular thing.

I don’t really mind people studying me, when I know that they are studying me, and know what it’s for. When I don’t know the researcher, I am much more hesitant about answering because I don’t know what they will really do with the answers.

On Anything

See… the thing is that I already have most (meaning all (meaning WAY too much)) data, and it’s all organized the way I need it to be to make the visualizations, and I’m too lazy to actually make the visualizations yet (you know, since we actually have specific classes set for that….). So there’s not too much more for me to do at this point. I mean… I could make an outline… could… but why do that yet? And I could draft some visualizations…. but… no. So basically, current status: same.

Data Day 8

Level 1-

IMG_6765

IMG_6766

Level 2-

Making and drawing influenced my understanding of discursive practices by pointing out specific examples of how they work both theoretically and practically.

Level 3-

Rhetoric and discursive practices were represented through imagery of “inside” and “outside” as well as actual rhetorical definitions. Through the imagery, I was able to understand how to start thinking about being an insider or an outsider, and through bullet pointed definitions or examples, the idea became clearer. She also used generic colors and did not make anything overly complicated, making it easy to follow her though process and understanding.

Discursive practices of the class were represented through reasons and bullet pointed items that only those who are in the class could understand, and bullet pointed questions or reactions by those who are not in the class.

Her representation is very similar to mine in that we both use mostly words rather than pictures to show the kinds of things that the class identifies with, and outsiders do not understand. It is different in that I make jokes and poke fun at the ideas rather than taking them entirely seriously as she does. Mostly the differences were just in representation, not as much in interpretation or understanding of the ideas. We simply have different styles of representing the same thing.

On Data Collections and Methods

I ended up collecting the dates and tags on posts. I set up a spreadsheet with the tag names on the top, and the dates going down of all of the posts that were tagged with that tag. These tags included not only the ones in the menu, but also categories that the author included even though they were not actually on the menu page. As I collected my data, I discovered that there were some menu tags that did not have posts (at least since Jan 2015 (this is the date I decided on to go back to for my data collection)). These particular tags I will mention briefly, but since they do not seem to be incredibly important (at least important enough to blog about (which is what this research question is studying, so therefore they are not important enough for my purposes)) I will focus primarily on the tags that do have posts since Jan 2015, and especially focus on how many posts each tag has and how that shows what seems to be important for this community.

Since, for the purposes of my research question, it seems that the amount of posts in each tag or category corresponds to how popular that particular topic is to the community, I will use the visualizations to show the correlation between the popularity of each tag to the amount of times it has appeared (and its frequency). There are multiple ways to show the relationship between the frequency (shown by the dates), and each of the tags. Since I am looking for the popularity in correlation between each of the tags, I will probably do something along the lines of larger or smaller bubbles for each tag depending on how many posts they have, as well as perhaps month bubbles that contain the tags in either larger or smaller font depending on how many posts there are in that tag, I might also make a slightly more comedic visualization that would show the correlation between each post or tag and if it relates only to military wives or if it could also apply to civilian wives.

An Update

So, I have collected all of the data that I am going to collect for my research site. I ended up only gathering the dates of posts for each category. This provided me with plenty of data to use, and also reflects even more accurately what kinds of things are important for Military Spouses. I did this by putting the tags in the top row of an Excel spreadsheet, and the dates in columns of the spreadsheet correlating to their specific tags. I also made note of some trends in the posts, or other various observations about the categories under which they fell.

As I went through the site, however, I soon discovered that there are some categories that are highly used, but not on the menu. Because they are so popular and often used, I decided to follow them and include them in my data. I’m happy that I did, because it provided me with 82 more points of data that are very important and I otherwise would not have had.

From here, I only really need to analyze and visualize my data. I plan on doing that in various ways to show the relation between different topics (through tags rather than through title) and their popularity (based on how many posts have been posted in that category since 1/1/15).

The biggest problem that I’ve had with the dual ideal of being a researcher as well as being a member of the community is that I really want to read through all of the posts. Because they all apply to me in some way, I really wish I didn’t have to look at particular things and was able to actually read through all of the posts. But, that would have me ending up with WAY too much data, and although that could be fun… I don’t think anyone wants to sort through it all. Otherwise, there has never been an issue for me in the posts themselves that causes that feeling of alienation.

Data Day 7

Level 1

NAU petition site

Level 2

As part of the actual NAU.edu site, my site is built into campus politics. Similar to WeThePeople, it goes directly to the campus Senate to be voted on and passed/declined. My site is about as user friendly as the actual NAU site, and has the same kind of homepage. This makes the students feel more at home, and like the petitions will actually reach the desk of the president as it is more official.

  • because the site is a plug-in, it would draw administrative attention necessarily, and force them to pay attention.
  • the students/staff/faculty would sign by first signing in with their NAU ID and password, and then proceeding to read more about the petition and sign with their NAU ID and password again. This ensures that there is no fraud happening in the signing process.
  • Students/staff/faculty would learn about petitions through the link on the nau.edu homepage, and could learn more about each petition through the “Current Petitions” button. After signing a petition, petitioners can also share via social media that they signed and what/why they signed. This ensures that the maximum amount of people can hear about it.
  • Any student/staff/faculty can start a petition with their NAU ID and password
  • Each petition needs 10 signatures to be passed onto the Senate who will vote on it from there
  • I chose this approach because in a college setting, I believe that only an official petition would actually be heard by the policy makers. Successful petitioning is one in where the majority vote is acted upon.

Level Three

A faux case could be a petition for more parking on campus. The original petitioner (OP) would click the “Start a Petition” button. From there, they would be directed to an “About” page where they would explain the what and why of the petition and tag it with keywords. From there, the site would recommend any relevant previous petitions and offer a “continue with my petition” button (several pages down, to where they have to click through all of the suggested petitions before being able to continue (this ensures that they know all of the previous attempts and why they failed/succeeded)). After that, they will sign (yes, they sign their own petition), and be able to share the petition via any form of social media. Once 10 people have also signed the petition, the petition will reach the desk of the NAU Senate who will then vote on whether or not to proceed with the process. If they vote no, the OP will be informed that it did not pass, with a brief summary of why not. If they vote yes, the OP will be informed that it did pass, and the next steps for helping NAU achieve the OP’s goals. The NAU senate will then proceed to talk to the president and relevant people to make the petitioned action happen.

Level Four

I learned that designing a website gets easier with practice. Bright colors are not always the best thing, nor is bold font, but rather a balance that draws the viewers eye to what you want them to see. I learned that it could potentially be really easy to be a clictivist, but at the same time, depending on the user friendliness of the site, it could also be very difficult. If you are doing it in an attempt to be better informed and actually change the world for the good, even slight blocks in the road could be seen as learning tools; however, if you are doing it in an attempt to simply be a whistle-blower and don’t really want to follow through or accept the consequences of your actions, learning more through roadblocks is just a frustration. Online petitions all work differently, but could potentially be a good idea. In the case of WeThePeople, I think it is one of the best ways that the White House could hear the voice of the people. But, in the case of Change, I think that I would categorize “petitioning” under whistle-blowing. Everything matters in politics, and petition sites could be a big influence in politics of the future. Large petitions that have many votes should gain attention, but should also be weighed against costs and be tested to see how much of the population agrees with it.

On Implications and Ideologies

The implications of the method that I will be using to research Army Wife 101 are vast, but there are two particular ones that I will be focusing on: there are particular topics that milspouses are interested in, and the titles give the keywords to finding those topics. From these two assumptions, many other implications and complications happen, however we will only be focusing on those two for now. First, there are things that milwives are interested in. This comes from an understanding of the community as a whole: there are certain things that only us poor milwives have to go through and deal with, and sometimes we need advice. These are the particular topics that find themselves in the menu, and mentioned through keywords in the titles of posts. This leads us to the second assumption: the titles of the posts have keywords to help the reader understand what the post will be about. While not all people would be able to initially read every title on her blog and understand what major topic it falls under, after some research, almost everyone could start to connect the dots. This assumption is what leads me to only look at the titles of the posts for my data collection.

The implications and assumptions of my research method shine a light on how to understand this community. As I said before, there are particular topics that are discussed in this blog. While they do seem to be specialized to a particular audience, they also seem very limited, even for that audience. I discovered this as I began researching this community and discovered a variety of other topics that could be of interest to the community that are never discussed. This does help my research though, in that it narrows the topics that I have to look at. Another thing that my research unveiled is that there are very few comments on the posts, thus implying that it is more about learning from the author than it is creating a community and discussing problems. This is why I narrowed my research to only titles-topics relation, there were not enough comments to be able to look at.

I, also have a particular mindset coming into researching this blog. The main two things that give me selection bias are: I am also a milwife, and I also have a traditional understanding of marriage, family, and wifehood. These are the main assumptions that the author makes about her audience, and I fit perfectly into that category. This being said, any post that does not have to do with helping the reader to better understand or help the reader be better at any of these particular things, will not necessarily appeal to me.

Everyone on this community comes to this community to learn how to do what they do (being a milspouse) better. The author decides what “better” means, based on very traditional understandings of many things (because frankly, that’s pretty much the only way it works with a spouse in the military). And, clearly, (based on above sidenote) I typically approach many of the topics the same way.

Data Day 6

Level 1

Question: What is your favorite cartoon?

Level 2

  • Sarah- The Lion King
  • Kim- Spongebob
  • Vicky- Xin pu Sen
  • Damian- Pokemon
  • Tarran- Jo-Jo’s bazaar adventure
  • Amanda- Steven Universe
  • Josie- Tom and Jerry
  • Kayce- Powerpuff Girls
  • Brittany- Pokemon
  • Kirk- yu-gi-oh
  • christine- avatar- last airbender
  • chelsea- calvin and hobbes
  • nate- steven universe
  • kayla- meet the robinsons
  • nathanial- adventure time
  • nicole- Dispicable me

Level 3

http://prezi.com/6qd5kil7nxjh/?utm_campaign=share&utm_medium=copy&rc=ex0share

Level 4

  1.  I chose to put each kind of cartoon into separate bubbles, so we have three: Disney, Cartoon Network, and Foreign Cartoons. Each bubble had a different color, and each line and personal bubble correspond to the color of the particular kind of cartoon. The lines are also thinner or thicker depending on how well I know/how often I talk to that person. The personal bubbles are also related magnetically closer or further away from me based on the same thing.
  2. The lines are the same color as the kind of cartoon that each person likes, they are also thinner or thicker based on how close my relationship is with each person (relatively).
  3. One can infer that I have very little connection to foreign cartoons, and most of the people who like them. One could also infer that I like Disney much more, and Cartoon Network somewhere in the middle. My relationships with the people who like those kinds of cartoons are also reflective of my liking or disliking of the cartoon.
  4. My map reveals that we have very limited interests. Since all of the cartoon preferences can come down to three main genre’s.
  5. I don’t know very much about other people’s connections other than simply my connection with them. This is evident in the fact that I did not put any lines between other people. Even my connections with the people are limited mostly to this class, so I am not able to make very good judgement calls on how far away or close to put their personal bubble to mine.

On My Research Method

The research question that will by guiding my data collection and analysis is: What topics are Army Wife 101 interested in?

From this question, I will be gathering data from the blog post titles, looking through posts in each category for more information regarding each topic, and the general content on the blog posts as well as the time/date stamp on each post.

I will be collecting this data by looking through posts from the last 6 months. I will collect and visualize the frequency of posts in each “Menu Category.” Collect and visualize if there is a seasonal peak in posts (of a certain kind, or in general). Collect and visualize if there are trends of posts surrounding particular “news” events (larger events such as elections, deployments of different units, etc.). All of this data, I will collect by looking through the posts, then mapping keywords/topics onto an Excel spreadsheet.