Then and Now
I have been in Rome this semester, which has been an extraordinary experience. I have been working especially hard on my new book, which should be finished soon. I’ve been invited to speak about it in Paris on December 12 (in English!)- if you are in the area, I’d love to see you!
I recently taped a long interview with Dr. Yeshaya Gruber of the Israel Bible Center on my book, How the Bible Became Holy. I enjoyed our conversation together. You can check it out here:
During one of my low points during COVID, I read Martin Seligman’s book, Flourish. The book was a bit uneven, but I was intrigued enough to take the online VIA (=Values in Action) Survey of Character Strengths. According to the test results, “Curiosity” was one of my top personality traits. On some level, of course, I knew this, and I knew that one of the ways that COVID was hard on me was limiting my opportunities for interacting with people and having new experiences. Yet seeing an actual computer test confirm this, and reading Seligman’s advice to lean into character strengths (just calling it a “strength” was a bit of a reframing this trait) was motivating. It really helped me focus on finding an arena to exercise my curiosity, that involved getting off the couch and maybe even out of my house.
This next part might sound odd to many, but that arena, I decided, was going to be organic chemistry. I took orgo in college, as a requirement for what I thought was going to be my major, biochemistry. Chemistry had long fascinated me; the ways in which chemicals interact – again and again and again – eventually leading to the support of life and consciousness filled me with wonder and awe. Note to the young, though: There is no wonder and awe in university courses on organic chemistry. I was intrigued by the course material during this year-long class, but it never came close to the questions that really motivated me. Working through a seemingly endless list of one-off reactions was not awe inspiring, at least not in my book. My motivation slipped, my effort slipped, and my interest waned. The truth is that had there not been grade inflation – even then – I would have been in danger of failing that course.
There was also a separate lab section and, alas, I fared no better. I would enter the lab once a week in the afternoon, and emerge some four hours later into often the rainy or cold dark, having conducted an unsuccessful experiment and still able to smell the chemicals on me. The atmosphere of the lab itself (and the class) was more one of desultory panic than wonder. By the end of the year, I had practically given up, reducing the explanations of my failed experiments to something along the lines of, “The flask was probably dirty.” That I passed at all was an act of compassion (or more likely, now that I am better immersed in life from the other side, instructor laziness). My interests drifted elsewhere, as did the course of my life.
Now, over thirty years later, I decided that I wanted a redo. I am not sure about what exactly drove this desire – maybe some combination of masochism and pride, but it is not something I wanted to devote a whole lot of self-reflection to. In person classes at Brown were just beginning again, and I decided to attend the second-semester organic chemistry class. (I am fully aware of and acknowledge the extraordinary privilege that I have in being able to do this, without cost). The professor, a wonderful man named Paul Williard who had thought that he had seen it all, was bemused. I didn’t take the tests for grades (although I did work through some of them on my own), but I did the homework and regularly attended the lectures. My constructive role in those lectures, as I saw it, was to ask the stupid questions that the undergrads were afraid to.
The teaching of organic chemistry has not changed much, at least not the class I took. My headspace, though, had. I allowed my interest and curiosity to drive me, not requirements or grades. “Fun” might be a strong word, but the material did keep me engaged and interested. Still, I realized that actually learning this stuff in this the classroom was not quite enough for me.
This is the part where Professor Paul goes from wonderful to extraordinary. He invited me into his lab. This was lab work as I never knew it, far from my college chemistry class. Tedious and full of failure and frustration for sure, but also full of wonder and discovery. I spent months just picking up basic lab techniques (and I have many more to learn). A failure led to database research, new knowledge, and a new approach, over and over until we finally got it. “It” was usually quite small, just a piece of the process that allows us to move on to the next step. Over the next two years I tried to carve out time each week to spend in the lab. The lab became, as I put it to a befuddled colleague, my “happy place.” During the day I was often preoccupied with my lab results, sometimes rushing off to my computer in the evening to research a result or new approach.
I have not lost interest in ancient Jewish history. I am beginning a sabbatical this semester in which I hope to substantively complete a book on “lived religion” in antiquity – that is, how “ordinary” people thought about and cultivated their relationships with supernatural beings, often to the chagrin of the religious elites who sought to draw boundaries around their “traditions”. I also remain deeply involved in several digital humanities projects, including my Inscriptions of Israel/Palestine database. But I am already looking forward also to returning to the lab in January.
My goal in retaking orgo was simply to retake orgo. The idea that I would end up in a lab, and that that work would become so interesting for me, was not even a glimmer. The thought not only that I would get to do lab work but even get results that I could publish was inconceivable. But there you have it – my first published organic chemistry paper. The discovery is of minimal importance and the journal is not “top shelf” (although it is peer reviewed), but simply being able to get to this point and to make a new contribution to human knowledge – however tiny – is deeply satisfying.
We all know the platitude that the journey is often more important than arriving at the destination. For me, on this journey, this represents a rest stop. I can hardly wait to get back on the road.
One of my goals when I started the Inscriptions of Israel/Palestine project was to reach a point where we could not only make inscriptions relevant and exciting, but also that we could use them for digital analyses. I am happy to report a first attempt to do this. I co-authored, with Daiki Tagami (who did the lion’s share of the work), a paper on “Machine Learning Techniques for Analyzing Inscriptions from Israel.”
Abstract:
The date of artifacts is an important factor for scholars to get a further understanding of culture and society of the past. However, many artifacts are damaged over time, and we can often only get fragments of information regarding the original artifact. Here, we use the inscription data from Israel as a model dataset and compare the performances of eleven commonly used regression models. We find that the random forest model would be the optimal machine learning model to predict the year of inscriptions from tabular data. We further show how we can make interpretations from the machine learning prediction model through a variance important plot. This research shows an overview of how machine learning techniques could be used to resolve digital humanities problems by using the Inscription of Israel/Palestine dataset as a model dataset.
The full paper can be found here.
This last semester I taught a new course, “Happiness and the Pursuit of the Good Life,” the goal of which was to put positive psychology and religious texts into conversation. The class was overwhelmingly popular. I ended with around 400 students, and had to turn away many others. But was it a success?
Every semester, in every course, I struggle with this question. I have found it useful to break this question into a series of other, more specific questions:
That last question specifically, and ironically, is perhaps the easiest to answer. Ironically, because it is in many respects the least important in thinking about the “success” of a class: we are not always in the best position to evaluate our own learning (or the success of one’s own classes). There are too many confounding cognitive biases at work. Moreover, actual student evaluations are famously biased and problematic for many reasons. Hence, in nearly all of my courses I require a separate final reflection paper in which students frankly assess their own learning and places to grow. This paper plays no role in their evaluation. Far from perfect, but I have found it useful and think that many students have as well.
I actually read all 400 of the final reflections for this class, but they are hard to get a handle on. Particularly good and critical ones stand out, but how is one to assess the larger bulk of them? This made me think about “distant reading”, and whether digital tools can be profitably applied to a large set of papers like this. That set me off on trying to write and deploy a Python program that does two things: (1) creates a topic model from a set of student papers; and (2) also creates a Word Cloud. I am not sure that doing so for these final reflection essays really told me much, but I am still intrigued by the potential usefulness of the technique and want to spend the rest of this post sharing the method. In my next post I’ll return to discuss the Happiness course.
The Python program I wrote is available here, as a Jupyter Notebook on Github. It does not require much familiarity with programming, although a basic knowledge (e.g., how to set up and run a Jupyter Notebook) is necessary. We use Canvas at Brown, so the first step, prior to using the program, is downloading the Zip file of the papers, all of which are in docx format and then extract these into a separate folder. The program converts the individual docx files into txt files, and then merges the documents into a single txt file.
The next step is the most annoying and iterative. The text has to be preprocessed, which means stripping out punctuation and other useless text, including a prepackaged list of “stop words”, words like “and”, “but”, and “or” that are common but not useful. This list, though, has to be expanded in line with the particular papers. A set of papers, for example, might include the name of the course and the professor, both of which would throw off the processing when repeated in every paper. Additionally, I have found the preprocessing throws off a lot of junk words, that need to be identified and then added to the stop words. So each set of papers requires its own process of looking at the results, adding new stop words, and rerunning to get new results until something more useful is achieved.
I then topic model the set of papers. This is a process that identifies distinctive clusters of words that appear together. I set the number of topics that I want, a parameter determined through trial and error and that will change between sets of papers. For this set of 400 final reflection papers, I chose to create five topics that looked like this:
[(0, ‘0.010*”religious” + 0.008*”feel” + 0.008*”learned” + 0.006*”work” + ‘ ‘0.005*”many” + 0.005*”need” + 0.005*”found” + 0.005*”different” + ‘ ‘0.004*”even” + 0.004*”journaling”‘),
(1, ‘0.009*”learned” + 0.007*”people” + 0.007*”much” + 0.006*”happy” + ‘ ‘0.006*”self” + 0.006*”elephant” + 0.005*”lot” + 0.005*”take” + ‘ ‘0.005*”things” + 0.005*”gratitude”‘),
(2, ‘0.007*”work” + 0.006*”learned” + 0.006*”readings” + 0.006*”things” + ‘ ‘0.005*”much” + 0.005*”found” + 0.005*”take” + 0.005*”self” + 0.005*”lot” + ‘ ‘0.005*”different”‘),
(3, ‘0.008*”learned” + 0.007*”feel” + 0.006*”learning” + 0.006*”things” + ‘ ‘0.005*”even” + 0.005*”learn” + 0.005*”found” + 0.005*”much” + 0.005*”lot” + ‘ ‘0.004*”enjoyed”‘),
(4, ‘0.007*”elephant” + 0.007*”take” + 0.006*”things” + 0.006*”still” + ‘ ‘0.006*”work” + 0.005*”feel” + 0.005*”improve” + 0.004*”xmlmn” + ‘ ‘0.004*”learning” + 0.004*”questions”‘)]
There is still some junk in the words (“xmlmn”) that I was too lazy to go back and strip out (one finds that junk is often replaced with more junk). It is less clear whether I would have gotten more useful results if I stripped out words like “feel” or “learned”, but in the end I felt like they told me something so I kept them. The program then produced a word cloud, above.
Maybe the most important takeaway from these data is that students tended to report that they learned, that the course helped them to think, and that it touched them. The journaling component of the course was effective for many of them, and many felt more aware of the power of gratitude. Of limited value, but it’s a start.
For this post, though, more important is the method. If you are able to deploy this and use it with better results, please drop a comment!