Category: Observers

In class on Thursday, I recorded the duration of every response to each question asked in class.  I did this, because I thought it would be interesting to see if the response time increased as we got more in depth with a particular subject, and then when the subject changed, the response time would drop, and then slowly build back up again. The data indicates that this theory is not correct, because the response times do not appear to have any clearly visible trend. This is likely because as we go more in depth with a topic, the questions do not necessarily require an longer answer. In fact, some of the introductions to new topics might have required the longest answers, because it was necessary to describe a large amount of information to the class.

Summary Statistics (Time Spent Talking in Class)

This indicates that approximately 10 and 1/3 minutes of our class time was spent by students responding to questions.

Plot of Response Times

Plot of Difference in Response Times

Plot of Whether there was an Increase or Decrease in Length of Response (1=Increase)

 

 

During Thursday’s class (3/12), I aimed to track the fluidity of conversations across different tables. Specifically, I wanted to see which tables participated most frequently and which tables most frequently interacted with each other. I listed the names of one person at each table to allow the viewer to orient the room. Additionally, I connected the lines at central nodes, when the conversation was shifting across tables. I define conversation as a continuous exchange between different tables. The single lines represent when only two tables talked, most often seen when Dr. Sample asked a question and a student responded. The longest conversation occurred when discussing the topic: What does connected mean to you?. There are some limitations to my observation: 1. I did not account for two people at the same table speaking consecutively 2. I did not track the time in which conversations took place. 3. There is a lot of “noise” because of the number of conversations shown and the size of the paper on which I tracked the conversations.

Through tracking class attendance this week, collecting attendance from prior observer posts and looking up weather reports from this past month, I was able to create graphs that looked at the percentage of students present in class through the month of February with respect to the day’s weather and and high temperature.

There were 4 different descriptions of the weather on days we had classes.  Clear, Scattered Clouds, Rain, or Snow.  I averaged the percentage of students present on each type of weather days.

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?

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

 

Thursday

 

My observing this week consisted of tracking the number of times Dr. Sample used some particular words, related to our overall idea of “Data Culture”, while he was talking to the class. I was thinking about also tracking frequently used words that people said for people who answered questions in class, but I forgot to count a couple on Tuesday’s class starting out, so I decided to abandon that idea. It was surprisingly easy to forget to count particular words that Dr. Sample spoke to the class so I may be off by one or two words, and also because a few words I started counting when Dr. Sample said them two or three times, though I think I did a good time of remembering how many he said before I started counting.

The particular words I chose to count were related to topics in our class, and specifically for the topics we talked about in class on Tuesday and Thursday. I wrote down some words before class that would be relevant to count, such as Data, Visualization, Information, Powerpoint, Maps, etc., but then added some more as I noticed Dr. Sample used them frequently in class. Here are the two tables for Tuesday and Thursday.

Tuesday’s class

Word	Number of times Dr. Sample said the specific word
Map(s)	75
Data	3
Visualization	6
Cartography/Cartographer	2
Information	6
Lie/Lying	8
Timelines	2
Graphically	1
Scale	8

 

Thursday’s class

Word	Number of times Dr. Sample said the specific word
Data	10
Network	3
Visualization	13
Information	9
Powerpoint	9
Timeline	3
Maps	11
Timechart	1
History	13

A couple of patterns stand out. First off, the number of times Dr. Sample said map or maps on Tuesday’s class was sizable compared to other words he said in class on both days. On Tuesday, we focused on maps for almost the entire class, and the map exercise where we had to draw maps from Studio D to Nummit, the Davidson Pizza Co, and Chipotle, was one part of class where Dr. Sample used “map(s)” frequently while speaking. “Visualization(s)” was a common word across both class, as well as “information”, which makes sense since they are relevant to our broad idea in class, talking about data. Also, Dr. Sample spoke the word “history” frequently in class on Thursday, since we spent a part of class on the Chronozoom where “history,” such as human history, featured prominently, and Powerpoint,  because we read the article about Powerpoint and discussed Powerpoint towards the end of class.

Overall, counting particular words that Dr. Sample said in class is interesting because it can display the length of discussion we spent on topics for the most part. On Tuesday, almost the entire class we discussed maps, and therefore Dr. Sample used “map(s)” frequently. The words do not give any context behind the discussion, for example, words such as information, data, visualization(s), scale, etc., can be utilized in a variety of discussions, but they give a decent picture overall of the topic of discussion.  Words were frequently used together with one another, such as “data visualization” or “data bars,” but I decided to keep the words separate to make the collection of data easier and more clear.

In class today and Tuesday, I attempted to plot the overall class attention in relation to time, using a variety of indicators. Every 10 minutes, I recorded the number of students who were making eye contact with Dr. Sample, or with the student speaking, and the number of students making eye contact with either their computer or the class screen.

I quickly realized that these were poor groupings, with the potential to limit the data, as some of the readings discussed. I may have imposed my own demonstrations of attention on the entire class, who may have various learning styles. Further the data may be tainted due to both the non-traditional nature of the class, in which we are often working in groups or working alone, or if a person is staring into space in Dr. Sample’s direction, or staring at their phone. It further does not take into account people leaving, having their own conversations or falling asleep at their desks, which could be other indicators of attention in class.

That being said, it is clear from the graph from today’s class when we are working on activities involving screens, 1:50, 2:20 and 2:40. Focus also appears to be more inconsistent on Thursdays, as would be expected. It is more continuous, and decreases with time on Tuesday.

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:

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.