Participation by Row in DIG 210 (Group 2 Observers)

There is a common belief that students that sit in the front rows participate more than people in the rows further back.

We chose to observe the class to see if this hypothesis is true. We recorded our data by giving tallies whenever a student in a row participated in the class discussion. One on one discussion with the professor or other students only counted as one tally, no matter how long the discussion went or how many exchanges occurred. Our recorder also noted the total number of students in each row to be able to normalize participation across the rows by the number of occupants in each. We also recorded how many of the total number of students in the rows participated to see if the entire row was adding to the discussion just a few.

There is a lot of error in our data however. This data is only recorded for one class period at one school. It did not take into account the shape of the room, class size, subject and school environment.

This study does not even accurately measure this class due to the small sample size.


However, for this specific class period, it seems that the hypothesis is not true. The discussion was dominated by the third of four rows.

WE ISSUE A CHALLENGE! In light of our findings, we want to challenge Dr. Owen Mundy to anonymize an average participation grade by row to see if grades matched frequency of participation (not quality of participation).


Data Culture Group 1- Responders

The readers bring up a good point from the article “Big Mother is Watching You” about making all of our personal data useful.  People may collect this data because they want to be healthier and more knowledgeable, but they also do so for the rewards, the control, and their ego. These are all valid reasons why people should want to track their personal data. However, the data they collect should not be overused and overanalyzed.  Yes, personal data can be useful in industries like healthcare, but we should remember that in other cases, like sleep and fitness tracking, we must take the data for what it is.  It is clear from this article that sleep tracking and other forms of personal tracking can be an unneeded additional source of stress and anxiety in people’s lives.  When an individual sees that he/she is waking up multiple times during the night or that he/she is not getting nearly enough deep sleep, this can cause inadequate sleep in future nights as well.  The author’s sentiments, “Instead of liberating the self through data, these devices could only further restrain and contain,” speak to the various issues that can arise from data tracking.  In addition to the modern-day anxieties that can emerge from data tracking, one has to wonder, like the readers point out, how involved and attentive people will be with their daily lives once a device can perform most routine actions for them.


DIG 210 – Responders – Group 5 – Week 10

In class on Tuesday 10/24, we discussed the uses, as well as the pros and cons, of personal data.  In many cases, we agreed that we can act as the ‘author’ of this data.  For example, when tracking sleep, we may be inclined to skip the nights where we are out late or won’t be sleeping much to keep the data looking as it should.  In this sense, personalized data is not always spot on simply because we are able to manipulate it.  So why are we so interested in having devices that track our personal data, if we are going to manipulate it ourselves?  In Anne Helen Petersen’s article,  “Big Mother Is Watching You: The Track-Everything Revolution Is Here Whether You Want It Or Not”, she mentions that the wearable bio-sensing market is expected to push 30.2 billion in sales by 2018, showing an ever increasing interest in access to personal data.  

In many ways this data is fuel for our ego.  Our human nature makes us inclined to seek our rewards and progress, and our personal data allows us to do so.  Since the tracking of this data can be automated, all we have to do is make a small goal of how many steps we wish to take, how much sleep we want to get, or even our heart rate throughout the day.  We seem to crave this information so that we can feel a small sense of accomplishment on a frequent basis. On the flip side, there also exists technology such as Mark, the data tracking glove, which we discussed is more limiting than empowering for individuals or specifically employees.  Mark is used by employers to track the manual labor of their employees.  It is extremely interesting to consider that data is  limiting and empowering – especially when it is both at the same time.

DIG 210 – Observers – Group 5 – Week 9

DIG 210 Student Sleep Analysis
According to our email survey, which was conducted on 17 October 2017, the currentDIG 210 students sleep on average seven hours per night. However, according to the results depicted in Graph II, the survey participants believe that they should sleep, on average,  one hour more than they do. Interestingly, the DIG 210 students regard themselves, thus, as sleep-deprived, which, if this survey is regarded as representative, implies that the students at Davidson College are unsatisfied with the amount of sleep they currently get.
Nonetheless, this survey should not be interpreted as a representation of Davidson’s student body due to the sample’s selection bias and lack of observation.
The question remains: what can this survey data tell us? In our opinion, not a lot can be learned, in terms of its quantitative nature. Despite its empirical insignificance, however, this survey exemplifies how data can be used to make targeted improvements in Davidson students’ daily lives. If, for example, a follow-up survey was conducted that considers the sleep levels of every student on campus and the results would have been similar, then Student Health Services should consider changing teaching methods or implementing new, school-wide strategies that  alleviate the students’ sleep deprivation (e.g. first class starts an hour later, mandatory bedtime hours). Since we have discussed the far-reaching threats that data can pose,  it is important to point out its nearly infinite ability to also drastically improve all of our lives. But only if it is being correctly collected and applied in an environment of checks, balances and legally binding morality.
Our sleep data provides a strong example of the contrasting effects data collection can have on divisive or “hot-topic issues”. There are a lot of benefits that can come from collecting data from students and creating strategies that may improve their lives on campus. On the other hand, it can be easy to poke holes in data that is not representative of the entire student body. As well as considering that each student has different sleep patterns and needs.
DIG 210 Student Major and Career Path

This week, I was curious of the majors of Davidson students and what careers they desire to go into after college. I was particularly interested in this topic pertaining to our class because in today’s society data is applicable to almost every career. Therefore, I predicted that there would be a large variety of majors and career choices within our class.

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It seems as though this idea is accurate with seven different majors represented and nine different career industries with two “unknowns.” I was somewhat surprised however that the majority of students in the class are economics major and wish to go into financial services, while only three students are computer science majors and wish to go into technology.

I think an interesting next step for this analysis would be to survey classes in different departments with the same questions and compare them to our classes results.

Group 1- Observers

As the observers this week, we took a poll of majors, minors, and class enjoyment.  The most popular major in our class is economics, followed by political science and computer science.  The most popular minors are economics and data science.  We also polled class enjoyment on a scale from 0-10, 0 being that you do not enjoy your classes, 10 being that you really enjoy your classes.  5 students said that their class enjoyment was an 8.  This was the most popular choice.  Only one student said that their class enjoyment was below a 5, at 3.  With the data we gathered we were not able to correlate class enjoyment and major, but we did find that majors that are popular at Davidson are also popular in our Data Culture class.      


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Data Culture — Readers

Our current discussion on big data has revealed that there are pros and cons to gaining more targeted insights into humans. Anne Helen Peterson’s BuzzFeed article “Big Mother is Watching You” explores this idea in the context of tracking software and devices. The ability to monitor our every movement – sleeping, walking, eating – and use that information to make our lives better and easier has potential benefits, especially in healthcare and the workplace, but can we actually make that data meaningful?

Tracking software and devices have the potential to improve the quality of life. In fact, tracking has already made lives for many Americans easier. The pharmaceutical startup PillPack ships a patient’s pre-sorted medications right to their doorstep, allowing them to manage their medications in a simple way. By eliminating the need for the patient to go to the pharmacy or the doctor to refill a prescription and using packaging and a mobile app that reminds the patient which medications to take when, PillPack hopes to reduce the high percentage of Americans who take their medications incorrectly. Outside of healthcare, data tracking could be used in human capital management to help large companies make strategic decisions and make people more successful at their jobs.


The BuzzFeed article highlights the sheer volume of data collected by tracking apps and devices. How can we find ways to make this data meaningful so it’s useful to consumers? Users seem to want tracking apps that will make life easier by taking decision making power away. The fewer decisions they have to make, the more effortless life will be, so they want an app that will tell them how many more calories they can eat and whether or not they have a UTI. Seamlessly transferring power over to a device, however, may have unintended consequences. At what point, if any, can a human decide they want to regain control and start making those decisions for themselves, or will automation have taken over completely? As the tracking revolution evolves, we must grapple with the challenges that efficiency may bring.

Data Culture: Ethical Injustice in Data Visualization

In both group 3’s blog and Tuesday’s class we identified and examined bad graphs,  and although it was implied, we did not explicitly discuss the ethical injustices that accompany them.  For example, one group showed the class this graph:


With such a misleading graph, the repercussions could be devastating.  If the “After” graph rendered nonexistent and only the incorrect graph stood, then the Florida Department of Law Enforcement has just wildly misinformed the public that their controversial, pro-guns vigilante law was working, when it in fact proved to have just the opposite effect. To snowball this mishap to the macro, these types of misleading statistics empower people to want to protect their right to acquire arms and tragedies like Las Vegas and Orlando occur because of the lenient gun laws.

Another similar graph that could provoke an ethical abuse would be this:


The truncated Y-axis makes it appear as though there is an immense amount more people on welfare than employed.  The precarious ethics occurs when people could use this graph to argue that welfare programs could be cut because people are taking advantage of the system, which has repeatedly proven untrue.

For class, our group encourages the class to engage in discussion surpassing just pointing out what is wrong with the graph and talk about why they are so problematic.