Observations

Tuesday

I was in the A-K document. These observations were recorded there.

  • A total of 14 people edited the document
  • Only two people made use of the chat feature
  • There were 10 people signed into the chat box at the end of the editing period. This number fluctuated throughout.
  • The chat was left a total of 10 times.

Thursday

  • It was raining on Thursday. A total of 13 people wore rain jackets to class, 6 of whom kept them on during class, 7 deciding to take them off.
  • The four large screens were on(including screen saver) for a total of 1:07:33.
  • Dr. Sample was talking to the entire class for approximately 29:33. This accounts for 39% of class time
  • Students were addressing the entire class for approximately 17:20. This accounts for 23% of class time

Number of Responder Per Question

This week I looked at the number of responders per question. For Tuesday, I only was able to record the number of people I observed editing questions. The results are below:

Google Doc Class, Tuesday.
Question 1: 9
Question 2: 4
Question 3: 7
Question 4: 6
Question 5: 6
Question 6: 7
Average: 6.5

Thursday.
Question 1: 5
Question 2: 1
Question 3: 3
Question 4: 3
Question 5: 3
Question 6: 2
Question 7: 8
Question 8: 4
Question 9: 3
Question 10: 4
Question 11: 1
Average: 4.2

Class Participation

Similar to Emily’s observations for this week, I tracked the classes’ participation throughout the time spent on the Google Doc and in class on Thursday.  I used a  a lot less efficient method of collecting data during the Google Doc session and every 5 minutes I counted the number of sentences in the document.  It’s only a rough estimate of the progress of the document, it would take me up to two or three minutes to count the sentences that were being added simultaneously.  On the graph, the total number of sentences is divided by 10 for scaling purposes. The second line is the approximate rate sentences were added each minute throughout the session.  There is a spike at the 65 minute mark after I added the post-question discussion.

Google Doc Participation

Throughout Thursday’s class, I kept track of the comments or questions made by Dr. Sample or a student within 5 minute time periods.

Classdiscussion416

Participation vs Time

image

I observed class participation as a function of time to see whether or not we are more active at certains times of class. I believe that someone else may have done this before. I thought it would be interesting, however, to compare the trends of our participation in class with our participation in the google doc session. Using the revision history of the google doc, I had a perfect record of the number of edits per minute. I am unsure, however, of what counts as a revision. As you can see from the graph, the number of edits of the google doc is much greater than the number of class participations. This is expected because on the google doc everyone can revise simultaneously and disjointedly instead of following the progression of class. The graph indicates that for the most part, the number of comments and edits is pretty stagnant throughout the class period. There is one major dip in the middle of the revisions history of the google doc. I assume that this is when everyone had finished major edits or paragraphs and took some time to catch up on what everyone else had written before adding to other peoples’ thoughts.

The Frequency of Characters

In my observer post, I am doing something similar to what I did in my first observer post awhile back, but I think this observer post offers some different perspectives for a number of reasons, and with the unique class on Tuesday, comparisons that can be made. I counted the frequency of times that each character in The Circle was mentioned in the GoogleDocs for L-Z on Tuesday and then kept track of the number of times anyone said a character in class, whether Dr. Sample mentioned a character or a student mentioned a character.

On Tuesday, the frequency of characters followed the questions on the GoogleDocs, with Mae brought up frequently, not only because she is the main character but also because of the first question of her being a “stand-in.” Also, Alistair is remarked on because of his connection to Mae as she adjusts to the Circle and also because of the question on the “totally optional” activities.

On Thursday, Mae is once again featured prominently in our discussions but also since we read the section on Mae’s superficial action on the activities in Guatemala, Ana Maria and Tania were read aloud. Mercer was a part of our discussion as we talked about his perceptions versus Mae’s as well.

Overall, I think looking at names instead of topics as opposed to my last post allows us to examine the difference between discussing multiple articles and a novel. Discussing a novel definitely feels more personal and we can utilize examples that are, in some ways, more relatable. Tuesday’s class was online and was solely devoted to answering the questions, so the frequency of names of characters brought up focused on the questions while Thursday we were present in Studio D to discuss a variety of topics and characters.

Frequency of Names Brought Up on Tuesday

Mae 30
Alistair 7
Annie 3
Ty 1
Dan 1
Renata 1
Gina 1
Francis 1

 

Frequency of Names Brought Up on Thursday

Mae 32
Alistair 1
Tania 5
Ana Maria 9
Mercer 13
Eamon Bailey 3
Kalden 3

 

He Talks the Talk but Does He Walk the Walk?

Screenshot (5)

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.

Screenshot (6)

 

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.

Observations: Eye contact with Dr. Sample by table

Tuesday March 31

Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7 Table 8
1:45 0 0 1 0 0 1 0 1
1:50 0 1 0 2 0 0 0 0
1:55 1 1 0 1 2 0 0 2
2:00 0 2 2 1 1 0 0 0
2:05 0 1 1 2 0 0 2 0
2:10 0 0 0 0 0 0 0 0
2:15 0 0 0 0 0 0 0 1
2:20 1 0 0 0 0 0 0 0
2:25 1 0 1 0 0 0 0 0
2:30 0 0 0 0 0 0 0 0
2:40 0 0 0 0 0 0 0 0
2:45 0 0 0 0 0 0 0 0
2:50 2 0 0 0 0 0 0 0
Total 5 5 5 6 3 1 2 4

 

Thursday April 1

Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7 Table 8
1:45 0 2 0 0 0 0 0 0
1:50 0 0 0 0 0 0 0 0
1:55 2 0 3 0 3 1 0 0
2:00 1 1 0 2 1 0 0 0
2:05 0 0 0 0 0 1 0 0
2:10 1 0 1 0 0 0 2 1
2:15 0 0 0 0 0 0 1 1
2:20 0 1 0 0 0 0 1 0
2:25 3 2 2 1 1 2 3 4
2:30 1 1 2 0 2 0 0 0
2:40 0 0 0 0 0 0 0 0
2:45 0 0 0 0 0 0 0 0
2:50 0 0 0 0 0 0 0 0
Total 8 7 8 3 7 4 7 6

For my observations I separated the tables into groups and measured how many students at each table were making eye contact with Dr. Sample at 5 minute intervals. The Table Guide shows what number correlates to each table in the classroom. The purpose of these observations was to measure attentiveness to Dr. Sample and the lecture. I should have recorded more intervals if I wanted to get a true reading on which table pays the most attention to Dr. Sample because recording every five minutes does not provide enough data. In addition this is a flawed methodology because I am measuring attentiveness based on eye contact, so this method assumes that if you aren’t making eye contact than you are not paying attention to the lecture, which isn’t necessarily true. From the observations, I can’t reach any substantial conclusions on which table focused the most and least on the lectures of March 31 and April 1.

 

 

 

 

 

 

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'”

Observations for 3/24 & 3/26: Participation by Outfit

Building off of previous observations from Drew Gill, I decided to track class participation based on outfit. I recorded what each person in class wore for their shirt and their pants. Shirts were divided into four categories: Short-sleeve/T-shirt, Polo, Button-Down,  and Long-Sleeve/Sweatshirt. Pants were also divided into four categories: Shorts/Skirts, Khakis, Jeans/Other, and Sweatpants. These categories were designed to be a bit vague, mainly because men’s and women’s apparel can vary quite a bit in certain situations.

ObsvWk324

Proportions seem to align, more or less. The largest percentage disparities have been bolded and changed to red. On Thursday, March 26th, Jeans/Other underperformed when it came to participation in class, accounting for 25% of the number of people in class, but only 9% of the participation. There were other large disparities as well, but that specific one was the largest.