Looking Ahead to #SBLAAR 17: the Augustinian Refugee

It looks as though finally registering for the 2017 Annual Meeting of the American Academy of Religion paid off: my name is now included as part of our panel! No longer shall I be known as “Unregistered Participant.”

My presentation this November will represent a bit of a departure for me. Whereas I usually focus on Augustine’s place in the history of the philosophy of time, this time I’ll be zeroing in on what exactly Augustine means when he writes of peregrinatio or (as it’s usually rendered) “pilgrimage.”

Here’s the short version: the problem with translating this term as “pilgrimage” is that it too readily evokes the image of devotion to the cult of the saints (e.g., a pilgrimage to the relics of St. Stephen, etc.). But the term’s origins go back further than that, more commonly denoting the experience of any migrant, exile, or (at times) even tourist.

My question is: What if we re-translated peregrinatio as “migrancy” in key texts of Augustine’s, such as City of God I? Out of that, further questions grow: Would we get a better sense of what it means to be a (so-called) “pilgrim” in this life? Would we better understand the resonances connecting the migrant experience to the Christian sensibility expressed so memorably in the words of Augustine?

It is my earnest hope that the answer to that last question is: “Yeah, pretty much!”

[H/T to the heroic organizers of the Augustine & Augustinianisms section at AAR, Dr. Matthew Drever and Dr. Paul Kolbet. Great guys, the both of ’em!]

Digital Pedagogy Post #1 Featured by Humanities Commons

In a rather pleasant turn of events, the powers that be at Humanities Commons have seen fit to feature my post on quantified student feedback to digital pedagogy!

You can find it on the Humanities Commons homepage, alongside some really excellent exemplars of what this platform is capable of, not least of which is Dr. Meredith Warren’s site (one of the best I’ve seen on here).

Hopefully we can all keep the momentum up as the summer carries on…

What Do Students Actually Think About Digital Pedagogy? (Pt. 2)

Following up on my earlier post on quantifying student feedback in the digital humanities, here are some rough-and-ready charts that aim to visualize some of the feedback data I’ve collected regarding the efficacy of digital pedagogy in the classroom. In general, it tends to demonstrate that the properly calibrated use of digital resources can see real results in a humanities setting.

This dataset communicates feedback findings drawn from a roughly 40-student session of History 101 (“Foundations of the Modern World up to 1500 CE:” a modest topic!).  22 of the 40 students responded to the questionnaire. For reference, here are the seven questions to which the charts supply quantified responses:

  1. Overall, I found completing the digital assignments to be:
    1. Educational: 22/22 = 100%
    2. Confusing: 0/0 = 0%
    3. Pointless: 0/0 = 0%
    4. None of the Above: 0/0 = 0%
  2. Before the digital assignments, my sense of the digital humanities was:
    1. Quite Good, Actually: 5/22 = 23%
    2. Limited & Incomplete: 8/22 = 36%
    3. Non-Existent: 9/22 = 41%
  3. These assignments improved my grasp of what “digital humanities” means:
    1. True: 22/22 = 100%
    2. False: 0/22 = 0%
  4. The difficulty-level of the digital humanities assignments was:
    1. Too Hard: 0/22 = 0%
    2. Too Easy: 8/22 = 36%
    3. Just Right: 14/22 = 64%
  5. If these kinds of assignments required deeper engagement with digital resources, that would:
    1. Be More Fun: 12/22 = 54%
    2. Be Too Burdensome: 5/22 = 23%
    3. Not Change Anything: 5/22 = 23%
  6. What is your attitude about the idea of “digital assignments” generally?
    1. It Was a Refreshing Change: 19.5/22 = 88%
    2. It Sounded Easier But I Didn’t Really Get Much Out of It: 5/22 = 7%
    3. I Would Prefer to Just Write a One-Page Reading Response: 1/22 = 5%
  7. Which digital assignment did you find most useful?
    1. Digital Mapping (DA #1): 4.5/22 = 20%
    2. Timeline Creation (DA #2): 5/22 = 71%
    3. Textual Analysis (DA #3): 2/22= 9%
    4. None Were Useful: 0/0 = 0%

Because numbers never tell the whole story, here’s a selection of student comments regarding the use of digital resources in History 101:

  • “No a million times over to short written assignments!”
  • “Timeline creation was most useful, followed by digital mapping and then textual analysis.”
  • “DH does make history a little more engaging.”
  • “It would be interesting to see other types of digital assignments, as well.”
  • “Some assignments were a bit confusing, but after a bit of time it was fine.”
  • “I think DH helps us to understand history much more easily.”
  • “I think it would be helpful to have a little more direction in where to find historical sources.”
  • “I enjoyed the assignments. However, it would be nice to have one written assignment. It would help with greater understanding.”
  • “I just really enjoyed the textual analysis! It was interesting. J”
  • “It was interesting in the sense that it enlightened me to a subsection of history I have not heard about before. However, the assignments were very easy, specifically the textual analysis. The map and timeline were good.”
  • “The digital assignments were educational to a certain extent.”
  • “I do like reading responses as a way to explore the material. However, the digital assignments were very beneficial and enjoyable. I use mapping and timelines a lot for studying, so this really helps me expand my toolbox.”
  • “The mapping and timeline assignments were helpful for understanding how the things we were studying related to each other. The textual analysis assignment seemed less helpful for that purpose, and it didn’t seem to relate.”
  • “The timeline and mapping assignments helped cement my knowledge of the material. However, the textual analysis really didn’t help with my understanding even though it was interesting.”
  • “I felt that if the assignments were a little more comprehensive I would’ve enjoyed them more. Since I thought they were super-easy, I sort of blew them off and then did the bare minimum at the end. I would’ve liked it to be harder. I loved them though!”
  • “I loved the mixture of assignments in this class. They did seem a bit easy or a lot of it was plug and play. But it was nice not having to write sixteen billion papers for this class.”
  • “Thanks for teaching this class! You made it fun and enjoyable. Keep up the good work.”
  • “I think you should drop Voyant [textual analysis tool], because it is not educational and doesn’t improve understanding of the course material.”
  • “I thought the digital assignments were interesting. I enjoyed them and I did learn some things. They helped me remember things about historical events and places.”
  • “The mapping and timeline assignments were useful. They put things into perspective. I didn’t get much out of the textual analysis assignment.”
  • “I think the mapping and timeline assignments were very helpful for the course. They helped me study and visualize. The textual analysis assignment, meanwhile, didn’t do much to increase knowledge of the course. It was interesting to do, just did not have the educational aspect that the mapping and timeline assignments did.”
  • “I would have liked to do maybe one or two more different ones for a little more engagement.”

For now, this remains mostly a snapshot of raw data, with a few bare-bones visualizations thrown in. But, when these numbers are combined with comparable datasets from other courses, I’m cautiously optimistic that more sophisticated and usable conclusions will result. Stay tuned for Part 3!

Notes on Macroanalysis

Strictly speaking, macroanalysis has to do with the analysis of textual data on the, well, macroscopic level. Turning to large-scale computing can allow us to make sense of datasets that would be beyond the ken of the average researcher making use of traditional methods.

For basic primers on this kind of work and what it can achieve, take a look at Matthew L. Jockers’ MacroanalysisDavid Berry’s Understanding Digital Humanities, or Witten, Frank, & Hall’s Data Mining: Practical Machine Learning Tools and Techniques. If you’re looking for online readings, I’d recommend Ted Underwood’s “Seven Ways Humanists Are Using Computers to Understand Text,” Scott Weingart’s “Topic Modeling for Humanists: a Guided Tour,” and an e-book by David Easley and Jon Kleinberg, Networks, Crowds, and Markets: Reasoning About a Highly Connected World.

The purpose of pursuing this kind of work is to find new ways of recognizing patterns and spotting anomalies across an impressively wide range of sources. Within the context of intellectual history, for example, macroanalysis might allow us to trace the transmission and even the development of key terms and concepts over increasingly long stretches of time.

When it comes to digitizing texts in the first place, we can choose from a number of effective tools. While the best digitization tends to be done by technologically well-endowed research libraries, there are also programs that anyone can use to get engaged in similar kinds of work. Optical Character Recognition (OCR) programs like Abbyy and AcrobatPro can prove to be especially powerful resources, allowing us to swiftly translate bare script into encoded text. Once that’s done, we can turn to tools like OpenRefine in order to clean our data up and make it usable for analysis–although the human touch is usually needed to ensure that the text-based dataset is as clean as it really needs to be.

For a figure like Augustine of Hippo, we could also draw upon existing digitized versions of his corpus in order to get a better picture of how major pieces of the Augustinian vocabulary–from confessio to distentio and beyond–fall into place over a period of decades. Vital resources here would include the digitization efforts undertaken by Belgium’s CETEDOC (Centre de Traitement Electronique des Documents) and the careful, prescient work of James J. O’Donnell (Confessiones online).

Once we have a clean corpus of data to draw from, we can manage our data so that it can easily translate into polished final products. In addition to the obvious (Excel), there are more advanced resources out there that can help streamline our data management, from RStudio (which puts the statistical language R to work for any number of projects) to Stanford’s capacious Palladio, which also includes an NEH-funded visualization program.

Getting even more macroanalytic, scholars can even turn to larger-scale database systems like MySQL, SQLite, MariaDB, and PostgreSQL. A great example of what databases are capable of in the context of the Humanities can be found in OCHRE, hosted by the Oriental Institute at the University of Chicago.

Again, while gathering up all this clean data is already a significant achievement, it is also a prelude to the fun part: actually analyzing the data and seeing what we can discover. There are a number of ways we can approach this stage of macroanalysis. Language processing, as exemplified by the Stanford Natural Language Processing Group and SAMTLA, can harness the power of technology to rapidly decode and parse texts from all over the globe. Markup tools and topic models can then help us give structure to our textual data and collaborate with others who may be analyzing the same datasets. MALLET provides us with a way of modelling word-clusters in order to draw conclusions about terminological trends and semantic trajectories, while (for something completely different) MARKUS stands as an excellent example of how a markup tool can deepen our study of an immense literary corpus (in this case, that of Chinese).

Lessons learned from methods in macroanalysis can also bear fruit in what we might call not-so-macroanalysis. (‘Microanalysis’ would seem to undersell things.)

For example, even taking a relatively constrained dataset–such as the digitized text of Book XI of Augustine’s Confessions–can allow us to track the use of key terms more precisely than any eyeball test. We don’t need language processing tools to tell us that Book XI deals with the themes of time and temporality, but those same tools can indeed help us determine how exactly Augustine chooses to deploy time-related words (tempus, tempora, distentio, etc.) over the course of the entire book.

Translating that processed data into easily interpretable visual media can then offer us a straightforward way to inform others about the terminological breakdown of Augustine’s writing. There are a range of advanced resources aimed at visualizing data in the most analytically responsible way possible: think here of D3, Gephi, and NodeXL. Yet even sites like Wordle or Jason Davies’ generator or (my fav) Voyant can give us the chance to create a visualization as simple as a wordcloud (see below), which can help people see the intensity with which Augustine pursues the topic of time over the course of Book XI, oftentimes better than would a paragraph of explanatory prose.

While constructing a wordcloud like this may seem like a rather straightforward enterprise, it can actually raise a number of thoughtful questions about ‘data-mining’ ancient texts. When it comes to grammar, for example, a language like Latin offers up some challenges that we seldom face when the object of our language processing is English. Trying to account for the cases of various nouns, for example, can add new layers of complexity to the process of turning raw text into usable data.

Digitally analyzing a text like Confessions XI can also raise questions having to do with rhetoric (as many scholars are already demonstrating in the field of stylometry). One of the most obvious steps we must take when cleaning up textual data is to get rid of all of the ‘stop-words:’ terms that recur so frequently in a language that they become almost statistically irrelevant. (In this case, think of non, et, de, and so on.)

But what about a word like te (you; accusative or ablative singular)? It would seem to be a statistically irrelevant term in many settings, and yet in Confessions XI that may not be the case. Throughout this work of his, Augustine frequently refers directly to God in the second person: tu, tibi, te… And so what is the burgeoning young digital humanist to do?

Regardless of what we decide to do with such data-points, the fact remains that the very exercise of trying to translate Augustine into analyzable digital data can give us reason to reflect on the grammatical, rhetorical, and perhaps even conceptual content of the text proper.

macro corpora.png

HathiTrust Digital Library

ECCO: Eighteenth-Century Collections Online

Project Gutenberg

Google Books

Google Ngram Viewer


What Do Students Actually Think About Digital Pedagogy? (Pt. 1)

One of the first questions that comes to mind when one hears the troublesome phrase “Digital Humanities” is an exceedingly basic one: “What does Digital Humanities *mean,* anyway?”

Sometimes, this question is asked in a spirit of honest humility and with a desire to learn more: “What does Digital Humanities mean? What kinds of tools and objects and practices count as DH? What can I learn from DH methods?”

At other times, the question seems to be asked with a bit more of an edge to it: “What even *is* DH, anyway? Does the term mean anything at all? Are we simply dealing with empty buzzwords here? What ever happened to good ol’-fashioned book-learnin’?” (And so on…)

In my experience, most students tend to pose the question in the former sort of way, while some of my fellow instructors prefer to opt for the latter. This is not a hard and fast rule, of course. There are a few students who could be described as hardline anti-DH-ers, while we all have a number of colleagues who either hold a genuine interest in DH or are actively engaged in doing DH work. But, in my admittedly anecdotal experience, it does seem like students–especially younger undergraduates–can be more open to exploring the potential of what DH could mean for them.

If we want to provide straightforward, concrete examples of what great DH work can look like, we could simply point toward huge collaborative efforts like Orbis and Pleiades, or toward specific scholars who have established themselves as preeminent pioneers of DH in their respective fields. In the world of classics and late antiquity, for example, I tend to point students in the direction of scholars like Dr. Sarah Bond or Dr. Jennifer Barry, both of whom run DH workshops for the North American Patristics Society.

When it comes to my meagre corner of the academic world, however, I usually focus in more closely on Digital Pedagogy. Here the operative question becomes: how can we best go about using digital resources to augment students’ own attempts to engage with the historical cultures they’re studying? (In my case, once again, the most relevant historical epoch is antiquity, although I also teach courses in medieval and early modern history.)

If that’s our operative question, the next step should be to ask students themselves what they think about the digital tools we use in class. It’s one thing to experiment with digital pedagogies in a course; it’s another to gather instructor-side data about new assignment-models; but it’s yet another thing to actually go out there and listen to what students have to say about the whole experiment in all of its unwieldy facets.

To that end, I’ve spent the first year of my position here at MacEwan compiling student-side feedback about a wide range of digitally tinged assignments. In a first-year world-history survey, we talked timelines and textual analysis; in a second-year medieval Europe course, we made multiple mock-up maps of the Mediterranean world in the Middle Ages; and in a fourth-year seminar, we worked to incorporate both podcasts and apps into our discussion of some challenging material from antiquity. In every case, students voluntarily submitted feedback info that I was then able to preserve as data and, in many cases, visualize for popular consumption.

Part of my goal with this site is to get some of my data on digital pedagogy out there, so that from its rudimentary roots something more substantive might grow. There are any number of other scholars and instructors out there who have delved deeper into these issues than have I, but I’m hopeful that offering up my feedback as open data will be a not-entirely-pointless exercise.

To that end: in future posts, I plan to share some of the data I’ve collected, as well as the contextual information needed to make sense of the raw numbers. As I continue to experiment with digital pedagogy in the months and years to come, I’ll aim to tweak my approach both to assignment-design and data-collection, so that a fuller picture of the capability of these new resources will begin to come into view.

Twilight of the Textbooks: Smashing Idols through Classroom Dialogue

To complete the trifecta, here’s the other piece I’ve contributed to the University of Chicago Divinity School’s Craft of Teaching blog:


Rather than figuring out the finer points of digital pedagogy or reflecting on the status of academic labour, this post aimed to think through the potentially alienating but ultimately rewarding experience of alternating between great-books-style teaching and survey-style lecturing. My position at MacEwan allows me to teach in both styles, which (as I’ve learned) can be really beneficial both for me and (more crucially) for my students.

Scholarly Labour & the Fantasy of Self-Fulfillment

Here’s something I wrote in light of recent debates about the role that unionization can play in the recognition of academic work as labour:


If you’d like to read more about the advances of graduate student unionization, check out the work being done by Graduate Students United at the University of Chicago.

On the other end of the spectrum, if you’d like to read about the perils of poorly strategized unionization, take a look at the collective bargaining situation at the University of Calgary.