Category: Observers

For my observations this week, I decided to record the amount of attention given directly to Dr. Sample based on his movement. For those who are constantly in class, we see that Dr. Sample has a preferred area of movement and does not venture out far from this area and its surroundings. I noticed particularly that his movements were varied in our Tuesday class. I sat on the far end left side, and he came over to my side and began writing on the white board, something which is not very common. When the class began, about 16 people were looking directly at him when he spoke from his usual speaking location. Once he ventured out farther from this circle of speaking, more students began to actively look at him. Granted some people still looked at their laptops wile Dr. Sample spoke, but there was a wider range of students paying attention when he moved. From what I could see, his number jumped from 16 to 21 once he began to move around the classroom. I later compared these numbers to the eye contact people gave him at the end of class when most of the class, except for 1 or 2 people, were looking at him. The numbers fluctuated according to Dr. Sample’s position throughout the class. When he stayed in his area, the numbers ranged from 15-19 at one time to 21-24 when he moved. From these observations, one can tell that the movement Dr. Sample employed was effective in gathering mass student attention. This also leads one to wonder why more people pay attention when there is greater movement than usual. Perhaps we as humans are so accustomed to having objects stay in place (due to technology such as phones and televisions), that we are more alerted by moving images. This observation definitely raises points about actions Dr. Sample could take if he wants to maximize the amount of student eyes on him.

Since the weather has been warming up, and all other observation options that I could think of had been exhausted, I decided to use the same set of observations I used last time. I.e. What people were wearing, laptops out, etc.…The fact that I was sick on Thursday and could not get all of the data didn’t help much either. Anyway, the things that I found were what I figured. There was a spike in the amount of shorts that were worn on Tuesday and the number of boat shoes such as Sperry’s increased from 0 to a little over a third of the class. These findings were what were expected, but like I said there weren’t many options for observations that have not been used. The full list of observations that I made can be found below.

February 10, 2015

Temp: 61

People in class: 28

Drinks: 7

Sweat shirts/Jackets on in class: 4

Collared shirts: 5

Long Sleeves: 3

Short Sleeves: 15

Glasses: 5

Hats: 0

Nike shoes: 10

Boat shoes/Sperrys: 10

Laptops: 21

Things Written on board: 5

Khakis: 3

Sweatpants: 1

Shorts: 20

Jeans: 3

Leggings: 1

Davidson Apparel: 5

DIG 210 is my third class in Studio D. As we all know, the room functions pretty differently from other classrooms on campus: moveable desks, a laptop cart, whiteboards around the perimeter, and four monitors make the space pretty flexible. Whereas most rooms contain rows of chairs that face in one direction, Studio D’s design makes students constantly change who (and what) they are looking at. For my observations this week, I focused on the room’s monitors: I know that I don’t have my “go-to” screen in the room, and was curious if other students switched the screens they looked at as well.

The diagram below shows the four monitors in the classroom. On Tuesday, there were three moments in which students were asked to look at a screen: the first and second related to blog posts, and the third related to our discussion on annotating the Apple Watch website. On Thursday, Moment 1 represents a review of the course syllabus, Moment 2 represents pre-Gephi discussion, and Moment 3 occurred after the Gephi demonstration concluded. The following results describe the screens students focused on for each of these moments:

Moment 1: Screen 1: 7, Screen 2: 4, Screen 3: 3, Screen 4: 3, Personal Laptops: 10

Moment 2: Screen 1: 6, Screen 2: 6, Screen 3: 3, Screen 4: 3, Personal Laptops: 9

Moment 3: Screen 1: 5, Screen 2: 2, Screen 3: 2, Screen 4: 0, Personal Laptops: 18

On Thursday, The following results describe the screens students focused on for each of these moments:

Moment 1: Screen 1: 1, Screen 2: 1, Screen 3: 1, Screen 4: 3, Personal Laptops: 20

Moment 2: Screen 1: 2, Screen 2: 0, Screen 3: 1, Screen 4: 10, Personal Laptops: 13

Moment 3: Screen 1: 2, Screen 2: 2, Screen 3: 2, Screen 4: 7, Personal Laptops: 2

Thus, it becomes clear that students in Studio D do not focus on a single screen; instead, they alternate the direction toward which they pay attention. I’m not sure whether this is a good or bad thing- or neither. It certainly does contrast sharply with typical classroom settings, at least. Tuesday saw more students looking at the screens across the classroom’s walls than on their own devices.

Since last time being an observer, I decided to continue with a set of data in the following classes. The set includes the number of times that students spoke up in total, the number of students who spoke up and the most times that a student spoke up in each class. I tried to separate asking questions, or very brief conversational responses from a legit speak up. Thus, in my definition of speaking up, it should be a student that explains concepts, demonstrates thinking or answers questions. Any conversation that involves very short sentences or clarifications of certain words is not counted.

However, the data is heavily influenced by many possible factors. For instance, the attendance of each class could affect all three types of data. Also, the content of the class could affect the data, such as the “fish bowl” on Feb. 10th. Since “fish bowl” is not normal speaking up in class, the discussions are not counted into the data. Also, on Mar. 17th, the topic involves Apple Watch in which many people have strong interest. The total times of students spoke up is considerably high.

Nevertheless, the most times of a student spoke up in class doesn’t vary much. An assumption is that people tend to not to speak when they think they have already spoken up enough many times. From the data, the limit seems to be around 4 or 5 for people that usually speak up.

The data is incomplete since the collecting process was not consistent. Also, the counting process is performed by only me. I could possibly miss one or two times while taking notes or listening to anything that had my attention.

Since we’re in the middle of March Madness, I decided to track class participation in a bracket-style tournament format. A student advanced each time he or she directly addressed Dr. Sample in class discussion, or if Dr. Sample called on them to answer a question. One could only advance if there was an opponent available to defeat (therefore, someone who answered five straight questions would only win one matchup, not five). Although I could have played the role of the selection committee and “seeded” the field, I chose to use the list of names from the blogging groups page in the order they are listed.

The results from Tuesday’s class are below:

Unfortunately, we ran out of time on Tuesday to determine a single winner. Also, the Gephi workshop on Thursday prevented the bracket format for serving as a good measure of participation (since we spent more time discussing in tables rather than as a whole class).

Although the bracket exercise was certainly amusing, it doesn’t appear to be the best way to determine the “best” participants; a lot of people who were bounced in the first round ended up speaking a lot later in the class, often more than the people who originally eliminated them. Perhaps the same critique could be applied to the NCAA basketball tournament as well.

This week I collected data to see what clothes people who participated in class chose to wear. Continued collection of data like this could investigate to see if there is a correlation between the clothes people wear and whether or not they participate frequently in class. However, I only collected the data for Tuesday due to the workshop nature of Thursday’s class, so it will only provide a snapshot of class participation.

Tuesday, March 17:

Leg-wear
5 people wearing pants –> 6 participations
21 people wearing shorts –> 13 participations

Shirts
9 people wearing long-sleeves –> 6 participations
15 people wearing short-sleeves –> 7 participations
2 people wearing no-sleeves –> 5 participations

This week, I observed how students’ posture and positioning affected their overall class participation. I looked at the correlation between characteristics, such as slouching forward/back and straight sitting to the amount of questions answered in class. I also collected data on whether the student was facing Dr. Sample and if they were cross legged. The data is as follows.

Total Amount of Questions Answered: 28

Slouched Forward: 13 (46.4%)

Slouched Back: 9 (32.1%)

Straight Sitting: 6 (21.4%)

________________________________________

Facing Professor: 12 (42.8%)

Cross Legged: 8 (28.5%)

Based on the data, it seems like students who were slouched forward as well as facing the professor were more likely to answer questions. In addition, I found it interesting that students who were slouched back were more likely to answer questions than those sitting up straight. I would guess that this is most likely because people usually do not practice perfect posture anywhere.

From Class Thursday:

It seems plenty of people took advantage of the nice weather. I counted 11 people wearing shorts on Thursday and 5 wearing button down shirts, including Dr. Sample. At least 9 people were not, unfortunately, wearing their fitness bands; it’s undetermined how many of these absences were due to faulty equipment. Finally, Dr. Sample coughed an average of 1.27 times per minute during class over a 30 minute period.