When I started this course, I had very limited knowledge of digital humanities. In fact, I think I wrote about that in some of my early reflections and spoke about it during the class sessions. But it was not just the digital humanities that I did not have great level of familiarity with already, it was the humanities, in general. Being a mathematician, I had not, in my academic studies, spent much time on humanities courses, so there was a lot to get used to.

Over the course of the semester, while completing a lot of the readings I felt like I understood the concepts and the materials, but, at times, it was really difficult for me to translate that into something I could conceptualize. I actually recall writing an entry about how the digital humanities needed to focus more on the fun aspects, so as to not dissuade the layperson from being interested in the field. As I was working on my final project, I thought it would be useful to go back to the sections of the curriculum that most closely aligned with the type of project I was working on. My project was mainly a data visualization project, to be completed via mapping, incorporating some text mining. To get myself mentally prepared to handle the project, I went back and reread some of the assigned readings, and I must say, it was a far more enlightening experience after completing the course than it was on the initial go round. Working on the praxis projects, engaging in class discussions, and seeing how far the digital humanities field could really be stretched over the course of the semester gave me a different perspective about the ideas in the texts. In particular, the concepts discussed by Manovich and Drucker resonated far more deeply with me as I was looking to infuse some of their theories into my approach. Ultimately, I think my newfound greater appreciation for the works that we explored early on are an indicator of just how far I have come as a digital humanities student, and how impactful all of our sessions were in furthering my understanding of a subject matter that was, at the time, a very unfamiliar landscape.

I recall in one blog post stating something along the lines of how I felt educated but not necessarily entertained while completing the readings. And, coincidentally, for my workshop session, I attended one on game-based learning and gaming in educational design. I love playing video games, so this session resonated with me very much. I certainly did not see that coming. That session really helped bring things full circle for me and gave me a better understanding of just how broad reaching the digital humanities field can be. What I will take from this class is not only a familiarity and understanding of new concepts but also, at a minimum, introductory skills with a number of tools that at some point down the line can serve some purpose in the research I am conducting. What I really appreciated about this course was how it gave me an opportunity to really take an interdisciplinary look at some of my research interests that are primarily centered around mathematics. Within a digital humanities framework, I was still able to focus on something I am really passionate about, but I had to configure my interest to the tools and ideas at our disposal in this class, and that is an exercise I think will serve me well in the long run.

Professor, I sent this to you separately when it was due, but I am posting it here as well.

According to their website, The Math Genealogy Project (MGP) aims to “compile information about ALL the mathematicians in the world.” , and for any individual who has received a doctorate in mathematics, the website shows:

• The complete name of the degree recipient
• The name of the university which awarded the degree
• The year in which the degree was awarded
• The complete title of the dissertation
• The complete name(s) of the advisor(s) (Math Genealogy Project)

The MGP is a digital tool that appeals almost singularly to PhD-level mathematicians. The website, however, is not well-funded and, as such, has limited personnel support and limited technical functionality. As it currently stands, to use the tool, one must use either of two search methods, a quick search, or an advanced search. The quick search allows a user to only search by name—first name, last name, or full name. The advanced search feature allows a user to search the following fields: first/given name, middle name, last/family name, name of school, year of degree, thesis keyword, country, and math subject/class. Once an individual is located on the website, one can click that PhD recipient’s advisor’s name to then find that person’s advisor. Each click of a name takes a user to a new page that displays that PhD recipient’s advisor, lists the PhD recipient’s students,  and the number of descendants (at least second generation) of that person. A user can keep clicking names, travelling backwards chronologically, until a PhD recipient has no known advisor listed.

My proposed project aims to accomplish two things as it relates to the MGP; better connect the data that comprises the MGP base, and extend the capabilities of the MGP to map genealogical connections across the globe, highlighting key metrics along the way. Other than printing a poster, that displays one’s math genealogy in a format that looks very similar to a hierarchy chart, the MGP has no capability to display connections graphically. Even creating the poster is done outside of the website and is unavailable to be viewed until it is sent to the purchaser. The sample posters that were created can look very messy (see here), due to how many ancestors a PhD recipient may have in their genealogy. With access to the raw data, which is entered one entry at a time by visitors to the site not uploaded from a database, I could map each entry onto a map of the world.

Though relatively simple in concept, these mappings would allow one to easily trace, though time, mathematical influences, and their impact on the study of higher-level mathematics. Moreover, using the subject class codes, one could chart the genesis, intermediate history, and contemporary standing (or not) of certain mathematical subjects over time, and across the world (as ancestors branched out from their bases). Most math PhD recipients, if they become professors at all, do not teach post-PhD where they earned their degree, so this mapping would illuminate the transience and transferability of mathematical concepts and foci.

I was very excited to participate in the Intro to Educational Game Design seminar on 10.28.21 as I was once an avid gamer. To date, one of the life moments I am most proud of is winning the NBA Street Vol. 2 tournament hosted at my college freshman year. I still brag about that to my college friends to this day. Yes, I am serious.

As the facilitator, Zachary Lloyd, mentioned, games provide “intrinsic rewards”… because they are meant to be a fun experience. When gaming, even losing sometimes can be fun if one feels they are progressing in some meaningful way, or there is a competition factor. I can vividly recall how bad I was first time I played NBA Street and how one of my friends said I was horrible, and he was right…at the time. However, games unlock a competitive desire. I started to play more not only because I thoroughly enjoyed the game, but also because bragging rights are social capital, and social capital is valuable in the right settings.

Zachary discussed the distinction between gamification and game-based learning in an educational setting. My understanding of the distinction between the two is that gamification embeds gaming into traditional pedagogical approaches, and game-based learning is teaching that is tailored around games that are specifically designed for educational purposes.

Zachary also highlighted 5 of James Gee’s (2013) principles of game-based learning:

1. Identity
2. Situated meanings
3. Well-ordered problems
4. Risk taking
5. Pleasantly frustrating

Of these, I consider risk-taking and pleasantly frustrating to be the most important principles used in games in an educational setting. Because the stakes are generally low in games, students can take risks that have minimal consequences which, in turn, encourages deeper and broader exploration. Also, it is ok to be frustrated by a game as long as the student is learning and funneling that frustration into advancement and productivity. Games are great in this sense because, by nature, they are somewhat detached from the seriousness that comes with typical curriculum but can still contribute to learning. Failure in a game likely doesn’t feel as bad to a student as failure in some variety of formal assessment. Moreover, the concept of gaming tends to imply one will receive multiple chances at success, whereas traditionally learning typically gives you one chance. In essence, something about calling an activity a game implies it is ok to be wrong. Additionally, both gamification and game-based learning can unlock a competitive desire that did not previously exist in the classroom—where individual performance can oftentimes be siloed, and students do not necessarily know where they stand with respect to their peers.

We wound up spending time experimenting with two games from the 1990s, Oregon Trail and Rex Ronan: Experimental Surgeon. This section of the presentation brought back memories of sitting in class in elementary school playing Oregon Trail for a full period (I suspect this was neither gamification nor game-based learning back then). Interestingly, by the time I reached middle school, I thought I hated role-playing games (RPGs), but I eventually grew to love them. In the educational setting, I cannot think of any game genre better for sparking intellectual curiosity and imagination.

My research focuses on the impact on educational landscape of an historically complacent approach to fundamental mathematics education in the U.S. Reinforcing literacy skills in children at an early age, has always appeared to supersede reinforcing numeracy and, as a result, English literacy is viewed as a defining characteristic of true Americanism. Numeracy, on the other hand, was for a long time considered more of a supplementary ability than a foundational one. Considering what the possibilities of Google Ngram are, and the vast corpora of literature available to Google, I want to explore the historical comparison of the mentions of numeracy and literacy in American literature. 

Before I started using Ngram, I had to first figure out how it worked. Ngram uses over 8 million books, which contain over half a trillion words (Pechenick et. al, 2015). The books have been scanned by Google and, based on the words you enter into the search bar, Ngram informs you of that n-gram, what percentage of them contain the specific term you entered (Google).

Before I started searching, I had to decide on baseline parameters for my Ngram searches. I decided on the following:

1. Since my focus is on American history, I would use the American English 2019 corpus which is defined as ““Books predominantly in the English language that were published in the United States.” (Google)
2. I would search without case sensitivity. It makes no difference, for my purposes, if ‘numeracy’ or ‘Numeracy’ is written.
3. I would use a smoothing of 3 (which essentially outputs averages over 6-year ranges). Any smoothing smaller than that shows too many fluctuations (I realized from this why they use the term smoothing because the graphs look really rigid with low smoothing numbers), and  since I am looking at the data over such an extended period of time, that is a sufficient range.

Now, I recall reading during my research that the terms numerate and numeracy were not widely introduced in America until the 1950s, so I wanted to see how accurately that is reflected in the literature mentions (Cohen, 1982). Based on Google Ngram, this historical note appears to be accurate. 

Furthermore, since the term numeracy had not yet been coined early in American history, I found myself looking for analogues of components of literacy, preferably unigrams (so that they are compared against the same corpus), as compared to numeracy and its components to make comparisons. Comparing the terms literacy and numeracy, illuminates my point, but does not provide much useful data as the mentions of literacy significantly outpace those of numeracy.

Considering how infrequently numeracy was mentioned during the first two centuries of the American republic, I decided to focus my searches on the following terms.

Literacy: reading, writing, dyslexia

Numeracy: mathematics, arithmetic, algebra, dyscalculia

Early in my text mining, I realized I had a big problem when it came to the mentions of ‘math’ and the mentions of ‘mathematics’. For all intents in purposes, we know those two words to mean the same thing in America and, as such, they are used interchangeably—with ‘math’ generally being used for brevity. Let’s take a look at the mentions of ‘math’ and ‘mathematics since 1700. Somehow, around 2013, mentions of ‘math’ exceeded those of ‘mathematics’. I was not able to develop any theory for why that is the case other than it is shorter to type.

As I mentioned earlier, the mentions of literacy far outpace the mentions of numeracy so using one of the advanced usage features of Google Ngram, I sought out to compare the ratios. Moreover, I wanted to compare the convergence of the uses of ‘math’ versus ‘mathematics’ in 2013 to ensure the advanced usage feature functioned properly. As such, I used the “/” composition which “Divides the expression on the left by the expression on the right, and is useful for isolating the behavior of an Ngram with respect to another” (Google). I also used the “+” composition, combined with the “/” composition to demonstrate two things: how the word ‘math’ has increased in usage compared to ‘mathematics’ since the 1950s and how the word ‘numeracy’ has increased in usage compared to ‘literacy’ since the 1950s.

Even considering the learning disabilities associated with numeracy and literacy, dyscalculia and dyslexia, respectively, the latter is easily more recognizable in the American lexicon – pedagogical or otherwise. Let’s see what Ngram says about this.

Lastly, I wanted to take a look at which terms are most closetly associated with the word numeracy so I used the “*” function. The “*” function substitutes the most common words that follow a word you enter into a search.

Overall, Google Ngram supports my theories around the emphasis of literacy over numeracy in America. Numeracy was hardly mentioned prior to the 1950s. Furthermore, even before that, in the early years of the American republic, mentions of the components of literacy far exceed those for numeracy.

 [TS1]confirm

Update: the images would not show on the Commons website, so the link to my map is here:

I changed my initial project because I decided I would do something a bit more personal. As such, I was determined to map my math genealogy. According to their website, The Math Genealogy Project aims to “compile information about ALL the mathematicians in the world.” , and for any individual who has received a doctorate in mathematics, the website shows:

“

• The complete name of the degree recipient
• The name of the university which awarded the degree
• The year in which the degree was awarded
• The complete title of the dissertation
• The complete name(s) of the advisor(s)”

I can’t recall where I first heard of the website, but I know it was one of my previous math professors who mentioned it to me. Note: as this website lists any individual who has earned a doctorate in mathematics, I am not actually on the site since I only have a master’s degree in math, so this map does not include where I attended college. On the MGP website, after you enter the name of, say, your advisor, you keep clicking on successive advisors and move backwards chronologically. Because of the functionality of the website, I had to create the data set myself based on each entry, which was somewhat tedious. I initially tried a different math advisor, but the scope of their genealogical locations was not broad enough to make a compelling map and there were too many duplicate locations. As a result, I moved on a second advisor to create the data set I used for the map. Upon initially using this website years ago, I was surprised to learn that my math lineage included both Laplace and Poisson, two famous, brilliant, and influential mathematicians. Knowing this made me feel like I descended from math royalty—if there is such a thing. My math genealogy started in Paris, France and had stops across America, but none farther west than Austin, TX—ending where my advisor earned his PhD, in Ann Arbor, Michigan. In all, it includes two countries and 8 cities. It spans from 1735 to 1980, when my advisor earned his PhD. If I achieved a PhD in mathematics today, I would be the 125,812^th descendant of my mathematical apical ancestor.

When I first uploaded the data set, the only location field I had was “City”. Unfortunately, that was not sufficient to map all the locations because clearly some city names are duplicates. I should have thought of that before. Ultimately, I went back to the data set and added “State” and “Country” fields so that Tableau could works its magic and more easily narrow down the location. This worked and my locations were mapped with points at each city.

I then had each dot on the map labeled by city name and the number of descendants from the doctorate in question underneath the city name. At this point, I thought the map was decent, but I figured I could add more depth to it.

From the original map I created, unless I added the year as a label, there would be no way for anyone to determine the chronological order of events, so to make the map more visually appealing I wanted to created arcs that appeared in reverse chronological order from one city to the next. Well, first, I wanted to include arcs and then I wanted to animate them, so they popped up from one city to the next in chronological order, so I set out to try to animate my map. Creating the arcs proved more difficult than I expected. I followed directions I found online very specifically, and I found it incredibly cumbersome. I had to completely revise the structure of my data set which left me very annoyed. Instead of creating arcs between the points like what I saw in the example, it mapped everything to a single point in Canada (note: none of the locations in my data set are in in Canada). I was very confused. I concluded that some of the duplicates in my data set (where the advisor and doctoral student earned their degree from the same institution) may have been messing up the pathway I created so I tried to remove the duplicate rows (which didn’t affect the route because the city was repeated to see what happened). This somehow worked! My genealogy was routed on the map – though without any points at the cities, just apexes where the arcs met. I then figured out how to create two maps, one with the points and the other with arcs, and combined them to make my final map.

Ultimately, I was able to figure out how to animate the map, but only by using filters which was not what I want. Ideally, I would like the map to open and for the viewer to see a point bouncing on arcs from city to city. I am not quite there yet.

Though what I describe above only took a few paragraphs to explain, I spent a significant amount of time learning how to generally operate Tableau and constructing my data set so that it had optimal functionality when I uploaded it. Putting the finishing details on the map at the end took the most time out of everything.

Note: Due to the storm last Thursday, I was unable to post prior to class on 9/23 so I am combining thoughts from the last two weeks of reading.

I went into the section of the curriculum about mapping very nervous about what I could produce and how I could use any available software. In short, I did not think my skillset lent itself well to this project. The article on Finding the Right Tools for Mapping really helped me narrow down my selection and I was ultimately able to download Tableau. I have not actually mapped anything yet so let’s see how that goes.

With respect to mapping, coincidentally, I had two recent experiences prior to reading the section that indicated how little thought I put into reading maps. While conversing with a friend of mine who hails from Ireland, I was surprised and confused to learn that Ireland, a nation I had viewed on maps for decades, was both smaller than the state of Indiana in both geographic square footage and population. Then, on a recent trip to St. Lucia for my friend’s wedding, my first venture into the Caribbean, I was surprised to hear a gentleman state that the population of the nation-island was about 180,000. I immediately thought I misheard, and he must have been referring to the population of the capital or something. I was wrong. Upon hearing both pieces of information, I thought, “How is this possible?” Though I cannot confirm this with 100% certainty, I always felt like the size of those two countries on maps were not appropriately proportioned to the larger countries. In fact, I didn’t even know Ireland was small country – in geographic area or population. Based on the reading, it’s clear I did not understand the concept of distortion—namely scale and projection. This begs the question for me of what purpose a map serves.

Should maps carry representations of sovereignty? Of course, I think, if that is the purpose of the map. If one is strictly looking for directions from point A to B or analyzing international scale, I doubt sovereignty is of much importance at that time.  The utility of maps as it relates to sovereignty, however, does not devalue the importance of how sovereignty can be represented on maps. The truth is, sovereignty is not one of the common reasons people look at maps. And, more often than not, folks incorrectly presume the borders and boundaries that enclose nations on maps represent some level of sovereignty.

Moving on the data visualization section, I was optimistic that the readings would resonate more with me as a mathematician. After reading all articles for this section, I felt educated, but not entertained. For me, the balance between education and entertainment is important as it relates to engagement with the subject matter. The academic space seems to take the fun out of data viz. Without question, there are important social issues to address via data viz, but exclusively doing that narrows the reach of the field and can make it less appealing to the layperson. With that said, what purpose does the value of data viz truly serve if it only appeals to those who are already thoroughly familiar with its use and function? Strictly focusing on social science or humanities matters creates and insular and exclusive data viz community of similarly minded individuals who are already aware of the powers of the visualizations. This seems to limit the potential exposure of these concepts to broader audiences.

My earliest experiences with large data sets and their representations started in high school when I was taking AP statistics and had to create several projects. At the time, I was heavily interested in sports statistics, box office grosses, and billboard sales—none of which hold any significant social capital. Through these avenues, I began to memorize, cross-reference, compare, and visualize different sets of data related to batting averages, opening weekend grosses, and first week album sales that I remember to this day. Nothing about those experiences felt academic and I knew of no data viz jargon (as it likely did not exist at the time), but the same concepts of reduction and spatial variables resonated with me then—even if I didn’t know it at the time. Reading these definitions, in many ways, erodes the experience as it takes it from something interesting and fun to something pedantic and stuffy. In my opinion, it should be clear that the applications of the digital humanities concepts that are discussed transcend the academic landscape.

Since first watching the movie Jerry Maguire in middle school, I have a fear of manifestos. I understood manifestos to have negative consequences based on the repercussions on the titular character after he wrote one, and anytime I hear the word, I am automatically mentally presented with potential negative ramifications. From my first viewing of the movie, many quotables, that still resound in contemporary evaluations of the best movie quotes, were seared into my brain. However, also seared into my brain were the consequences of an impromptu, stream of consciousness, manifesto. In this regard, contrary to Jerry Maguire, I believe Lisa Spiro is well-grounded as she deliberately identifies what she considers to be to the most pertinent values of DH: openness, collaboration, collegiality and connectedness, diversity, and experimentation.
Each of the projects evaluated this week incorporate at least one of the values mentioned by Spiro. The Torn Apart project is the epitome of openness. The transparency of the financial benefits received by certain entities, and how it is displayed on a distinctly outlined map, is a clear DH representation. The juxtaposition of the Congressional districts, Representatives, and largest profiteers leaves nothing to the imagination. Without question, the cartographic depiction is more effective at illuminating the geographic impact of ICE’s financial entanglements than any other medium. The CCP and ECDA project put an emphasis on access – allowing anyone the opportunity to view obscure exhibits and artifacts that would otherwise be inaccessible in the absence of the digital curation and forum. The ECDA project illuminates collegiality and connectedness by reappropriating contributions by European publishers to aggregate the histories of the Caribbean. Rather than dismiss the European contributions to this collection, the ECDA recenters them in a manner that puts Caribbean experiences, as opposed to European interpretations, at the forefront.
Even after all the reading I completed before this blog post, I am not yet sure how to classify or define Digital Humanities. Unlike ignorance in most matters, I do not yet find this troubling as I am slowly linking the conversation around interspersing theory and practice to my interpretations of DH. Considering the ease and ubiquity of digital access in almost all aspects of life, I am not sure of what does or does not fall into the DH bucket. The “DH community” is referenced often in the readings, but who are the members of said community? Better yet, who are not members at this point? Furthermore, who makes these decisions? Perhaps I will have a better understanding when I enter the social media world and create my Twitter account.