He Talks the Talk but Does He Walk the Walk?

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Tuesday during the beginning of class Prof. Sample specifically mentioned his desire to move around the class more in order to keep our attention. The above is his movement for class on March 31st (Tuesday) during lecture time. As we can see Prof. Sample went on 2 long trips around the room, but most of his time was spent going between his main desk, his podium, and the 2 front whiteboards.

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Thursday we had a lot less lecture time, so as expected their is less movement from Prof. Sample during his lecture. Again we see 2 larger trips around the room, but this time without his podium he spent more time going between the two front whiteboards, and spent a little more time near the rear one.

Are We Happy?

For my observations, I attempted to gauge the class happiness, by tracking and analyzing smiles. I chose to observe the class in 15 minute intervals, and count the number of people that were smiling during each of these checkpoints. I also noted what the class activity currently was, to see if I could find any trends in my data.

Smiles on Tuesday, March 31

1:40- 3 (Lecture)

1:55- 0 (Lecture)

2:10-1 (Lecture)

2:25-9 (group work)

2:40-0 (death discussion)

2:55- 4 (legos)

Total- 17

Smiles on Thursday, April 2

1:40-0 (Lecture)

1:55- 3 (Lecture)

2:10- 7 (group work)

2:25- 3 (Lecture)

2:40- 4 (group work)

2:55-2 (presentation)

Total- 19

After analyzing my data, it was apparent that there were slightly more smiles on Thursday. However, there were trends that existed in my observed data. First, it became clear that there were more recorded smiles during group work periods than lecture periods. Given that we have no assigned seats, most people sit with there friends. Group work gives students a chance to interact with there friends, leading to more smiles occurring. We had more extensive group work time on Thursday, so this could be a reason for more Thursday smiles.  Also, during a tuesday lecture, we spoke about the media mourning and death for about a twenty minute period. Given this somber subject, nobody smiled during this time interval, another reason that Tuesday smile counts were down. It is very evident that class activity and content affects smile count and potential happiness.

How Do We Argue?

This week I paid attention to and tracked some of the rhetorical tactics people used in class.

  • Tuesday, March 31
    • Similes and metaphors: 5
    • Allusions/Examples: 14
    • Counter-arguments: 2
    • Theses/Statements: 2
  • Thursday, April 2
    • Similes and metaphors: 2
    • Allusions/Examples: 9
    • Counter-arguments: 1
    • Theses/Statements: 4

Some notes:

  • I can’t claim that this data is even remotely accurate. It was actually quite difficult to catch all of the comparisons, references, etc. that people make, and I’m sure I missed plenty. These tactics are so deeply ingrained in how we converse with one another that they seem less a distinct device and more a natural extension of our vocabulary.
  • I counted comparisons like “it’s kind of like a gigantic database” as similes/metaphors, and comparisons like “it’s like when X writes that it’s a gigantic database” as Allusions/Examples.
  • One thing is clear, though we like to argue in the sense that we voice our opinions, but we certainly shy away from arguing with each other. I only counted a few examples of people responding to an argument with their own rebuttal or counter-example, and I think all of these belonged to Dr. Sample, actually.
  • Theses/Statements just refers to any broad conjectures or conclusions people made; e.g. “the data collected about us forms a ‘data double'”

Data about Ourselves

target

John Foreman’s article about data surveillance and machine learning as a means of gaining information about consumers took a unique approach to a topic in which we have previously discussed in depth. I personally really liked the article and the approach hat Foreman took, and his perspective on the issue.

When he started out with the example of the Disney World tracking device, it seemed as if he was trying to ease in to his argument with a somewhat funny and more lighthearted example. However, it divulges into one of his main points of corporations using data for profit. When Disney can track the different time and places that people do activities, it allows them to use this data to best attempt to maximize their profit and work to the needs of the consumers.

I also really liked how he differentiated between businesses using and NSA using personal data, and how he argued that we should prefer the NSA’s use, as he views the NSA of giving citizens more credit than the businesses. This was an interesting perspective, as the NSA spying my seem less obtrusive than simply tracking shopping habits at first glance.

Overall, I believe that extreme use of machine learning will begin to threaten humanities creativity. Speaking for myself, I am a creature of habit. I tend to frequent the same restaurants, vacation spots, and am a very routine oriented person. Will machine learning tracking everyone habits to the T, it will even lower the probability that people will branch out and push their creative boundaries. Although this can be very useful for company market campaigns, I believe that humanity must continue to evolve in a natural way.

http://www.corporate-eye.com/main/wp-content/uploads/2010/07/target.jpg

Old Information Systems and New

saint chapelle gold

KONICA MINOLTA DIGITAL CAMERA

Built in the mid 13th century, Saint-Chapelle, the windows of the of the church were probably the first “clock” that the Parisians had ever seen. Though lacking in precise accuracy, the displays were able to change the color of the chapelbetween night and day, and the shifting seasons. This is a great example to see just how far our information systems have gone. As we saw them boast in class, Apples’ new Apple Watch claims to be accurate to within 50 milliseconds of the actual time. Not only is it accurate, but it can sync with your phone to receive texts, calls, app notifications and much more. The advancement from old information systems too new is highlighted by speed, precision, and size. The old information systems were public, accessible to anyone anytime. As information systems have grown more complex, they have held more personal data. Data people may not be comfortable being public. It is in this struggle that people debate the privacy rights of information system users. What rights does Apple have to the information that is loaded up on their devices? Are they allowed to store that information? Can they sell that information?
eniac

ENIAC, completed in the early 1940’s by the U.S. army is credited for being the first computer in the world. This was just a general-purpose computational machine. It wasn’t until 1969 that information could actually be sent and received by different computers. The first nodes were UCLA and Stanford, only a few blocks apart from each other at the time. Now today, we have the ability to instantaneously send more information than ENIAC could process in a day any where across the globe. Health monitors implanted in peoples chests, camera’s that monitor eye movement for advertisers, and cookies downloaded onto my computer by websites track every moment of my life and are analyzed almost immediately. It is for these reasons that I agree with John Foreman that the mysterious humanity is destroyed. With enough many spent anybody can learn more about a person than the person probably knows about themselves.

 

Background of ARPANET: http://www.webopedia.com/TERM/A/ARPANET.html

Manufactured Happiness

Amidst all of the doom and gloom that Foreman generates in his discussion of data modeling, collection, and privacy, he repeatedly falls back on ‘happiness’ as the only benign product of invasive technologies and marketing strategies. He claims that at the same time that companies like Google and Facebook “increase our happiness”, they are also “destroying humanity as we know it.”

At the risk of making Foreman’s piece read even more negatively than it already does, I would argue that even the so-called “happiness” that we feel from walking about with Macbooks in our messenger bags or Disney MagicBands on our wrists is just as manufactured as the machines themselves.

Foreman talks quite a bit about how modern advertisements capitalize on our emotional vulnerabilities to make us associate, say, a cheap hamburger with sex. But he then seems to take at face value the ability of products to genuinely heighten the quality of our lives. Would he and his kids really have enjoyed his trip to Disneyland any less if Mickey hadn’t known which rides they’d been on? Alternately phrased, are the MagicBands actually a useful device, or are we just led to believe this because of Disney’s use of buzzwords like “effortless”, “magic”, and “enjoying the fun”? (These were all taken from the MagicBand FAQ page: https://disneyworld.disney.go.com/faq/bands-cards/understanding-magic-band/)

Coca Cola may be the most egregious offender here. In two words, they manage to create a causal relationship between opening a bottle and feeling better. If what they’re saying is true, I don’t know why more doctors don’t just prescribe Coke to treat depression.

If a company can convince us that their product will make us a happier human being, then the battle is already over. Trading personal data for happiness is suddenly an appealing enough swap – assuming we give thought to the transaction at all.

It’s certainly not an easy trade to conceptualize – “happiness” and “data” are two of the most nebulous and ambiguous commodities out there. I remember hearing in the news a few years ago when Google launched a service called Screenwise a few years ago that literally pays users to forfeit all of their Chrome browsing data. It got some buzz when it was first announced, but then promptly fell off the radar. Screenwise is still around, though it certainly isn’t one of Google’s top products or priorities.

I have a feeling that this is because Google recognizes that consumers are more willing to make sacrifices when the compensation is of a similar nature. In other words, people are OK with trading some tangible thing for another tangible thing (e.g. selling an old Nexus device for credit toward a new one) or something intangible for something else intangible (e.g., data for happiness). But people are less interested in swapping something vague (browsing history) for something concrete (Amazon gift cards). As soon as consumers are told that their data has real, monetary value, they become less willing to give it up.

Hashtags and Tagging

fb-tagging

This response will pay particular attention to the blog post “Harnessing the Power of #Hashtag”. This post was a very interesting and straightforward response to Mejia’s chapter on “Computers as Socializing Tools”. This post focusses on the implications of Hashtags in modern social media, paying particular attention to Twitter. This post describes various benefits that hashtags offer social media users.

I really enjoyed how the post divulged into how hashtags served as a “beautifully simple way to sort through and classify information on a particular topic”. I strongly agree with this, as hashtags truly make information on a certain topic easily accessible to everyone, as they organize the information into particular categories. Also, I agree that the small percentage of mistypes in hashtags are a small price to pay for all of the benefits that tagging can create. In my opinion, when most people use hashtags, they are careful to use the exact wording as the original hashtag.

The argument that hashtags help to bridge the gap between celebrity and “regular folks”is another intriguing point. Although this is true that hashtags enable everyone to have their tweets or posts categorized in the same place, it is very hard for a hashtag about a non-celebrity function to get much traction. So in my eyes, hashtags do not strongly help the celebrity/regular folk divide.

Also, I believe that photo tagging is a very important topic in the field of social media information exchange. The ability to make oneself known and tagged in someone else’s photo is a very helpful resource enabling people to widen their net of people who can view their photos.

The world of tagging and hashtags truly do serve as simple and efficient resources that enable information to be effectively categorized and spread.

 

http://media02.hongkiat.com/quicktips/fb-tagging.jpg

Observed Data

Since I was not in class all last week, I collected data this week to make up for it. My data is presented below:

The first thing I observed was the total number of minutes someone was looking at their phone or computer screen when Dr. Sample or someone else was talking (I did not take data when we were supposed to be looking at the computer and a few minutes after as well).

Tuesday
Person 1 Person 2 Person 3 Person 4 Person 5 Person 6 Person 7 Person 8 Person 9 Person 10
3 9 5 3 5 2 8 1 7 2
Thursday
Person 1 Person 2 Person 3 Person 4 Person 5 Person 6 Person 7 Person 8 Person 9
3 7 22 1 7 14 2 5 4

Although you do not have the data, it is interesting to note that the majority of the time spent browsing on phones and computers happened towards the latter half of class.

The next thing I observed was different on Tuesday and Thursday. On Tuesday, I observed the number of times Dr. Sample furrowed his eyebrows…it was 20 times during the class. On Thursday, I observed the number of times Dr. Sample flicked his hair…it was 2.

The last thing I observed was the number of times someone looked confused during class. On Tuesday, it was 94 and on Thursday (it was much harder to tell since faces were focused on the TV screens) it was 42.

As with all data, I could give you my interpretation of my data but then it would influence the way you looked at the data. However, one thing to think about after you look at my data…Is it meaningless data or is there more to it?

Tracking the Flow of Class Discussions

I made a couple of visualizations of this week’s class discussions. I’m tempted to just throw them up here without any explanation, like Dr. Sample did with that French popsicle image, but I don’t think mine are spiffy enough to make any sense unless I provide at least some info.

  • The black rectangles are the doors of Studio D.
  • The red circles are occupied seats.
  • The green rectangles are Dr. Sample’s perches, i.e. where he spoke from (his desk, where he stood).
  • The orange-ish rectangles and squares are the tables.
  • I used a different method for each of the visualizations.
    • If you think of the flow of conversation as passing a ball around the room, then Tuesday’s image assumes that the ball is thrown back to Dr. Sample after each comment unless you’re directly responding to someone else.
    • Thursday’s, on the other hand, assumes that the ball is thrown from student to student, and never given back to Dr. Sample after he “kicks off” the day’s discussion.

That’s all I’ll say! Hopefully you can make some sense of out them. Maybe you can even come to a meaningful conclusion. Maybe not.

 

Tuesday

tuesday

 

Thursday

thursday

 

Procrastination Nation?

Ever since I was a kid, procrastination has been an awful habit of mine. Whether its waiting until the last minute on an assignment or turning it in well after the deadline, I’ve done it all. So for this weeks observation, I decided to look outside the classroom to see if any of my fellow classmates share my love for procrastination based on their posting times for the course blog. Because the “responders” often comment on posts, it was difficult to track their posting times. Thus, I only looked at the posting times for the “readers” the past 4 weeks and the “observers” the past 3 weeks (my group not included). According to our syllabus, the due time for the readers is 10pm on Monday before class and the due time for the observers is 5pm on Friday. The posts fall into 5 categories: 12+ hours before the deadline, 12-7 hours before, 6-4 hours before, 3-0 hours before, and past the deadline. The data is as follows:

ReadersObservers

According to this data, it can be assumed that the Reader groups share my addiction to pushing assignments off, as a majority of the groups posted between 3-0 hours before the deadline. The Observers however, seem to favor posting past the 5pm deadline. This could be due to the fact that the assignment is due on Friday afternoon and the weekend is an inevitable force drawing students away from work. Nonetheless, it was interesting to learn how the posting times differed between the Readers and the Observers over the past few weeks.