As I said in a recent post, I’ve been using a mixture of captioning (also known as CART) and interpreting services for various Berkeley-related events. For my two classes, I decided to forgo interpreting services in favor of captioning. Part of this was out of a desire to try something new, but I think most of it was because when I was at Williams, I experienced enormous frustration with my inability to sufficiently understand and follow technical lectures with interpreting services. (I had to rely on hours of independent reading before or after the talks for the material to make sense.)
This isn’t a knock on the interpreters, or a criticism of Williams. I’ve said before and will gladly continue to say that I was very happy with the accommodations Williams was able to provide me, and how my interpreters have put up with me for four years as I consistently enrolled in the classes that they hated the most.
The problem is the technical term dilemma that continues to plague my experience in the classroom.
In the best case scenario, using captioning services would let me focus primarily on the professor talking, and if there was something I missed, I could fall back on the captions to catch up on a few sentences. To make it clear, the way CART usually works is that the captioner will type on a laptop with the text small enough so that I can quickly look at the screen to see what was being said 10 seconds ago. With interpreting services, one can’t go “back in time.”
The other advantage I was hoping to gain from CART pertained to preserving the spelling of technical terms. An interpreter can’t really sign the word Gaussian, but a captioner can at least type out that word correctly once the professor has said it often enough (or has written it on the board).
To top it all off, I was told during my first meeting with the Disabled Students’ Program (DSP) that CART would be able to capture content with 99 percent accuracy.
Unfortunately, theory hasn’t matched with reality and, if anything, my experience in Berkeley classes so far has been more frustrating than with my Williams classes.
I’m not trying to criticize Berkeley as a school, which so far looks like they’re excellent with regards to accommodations (no issues that have shown up in other schools so far!). This article is more of a holistic frustration at the whole education system.
Let me be a little more specific about what has happened so far. This semester, I’m taking two graduate-level computer science classes, natural language processing (NLP) and statistical learning theory (SLT). The former is an Artificial Intelligence course that’s heavy on programming, and the latter is a math course with problem sets. At the time of this writing, I have sat through eleven lectures for each class.
Natural Language Processing
One interesting wrinkle is that I have remote captioning for my NLP class. This means for each lecture I bring a microphone hooked up to my laptop, and a captioner in a different area (perhaps at her own house) will connect to my computer through Skype or Google Hangout and type what’s being said. I see the captions via another program that lets me see the captioner’s computer screen on my laptop. It’s pretty cool, actually. (One student in the class thought it was a sophisticated automatic speech recognition system.)
Berkeley had to provide remote captioning because there were too many requests for CART during the class time slot. I was fine with it because, well, why not?
Unfortunately, I didn’t anticipate there being multiple factors that would result in a tiring and frustrating classroom experience.
First, my NLP class moves at a very fast pace. (Since it is a graduate level computer science course, I expected that it would move quickly, though perhaps not as fast as it has so far.) As a consequence, my captioner has had a hard time keeping up with the conversational pace. It’s common for her to type in all the sentences for about thirty seconds, then to take a five second break, and then to come back to captioning. I can’t blame her — it’s impossible to type nonstop for the eighty-minute lecture, but it does throw a wrench in my plan to try and understand everything from the transcript, because there’s so much that could be missing.
To be fair to the professor, we do have a lot to discuss, and the students here are skilled enough so that most can absorb plenty of knowledge even when it’s coming at a fast pace. So while I do feel like the lecture rate is a bit too high, I know it’s not something that can be addressed easily without causing some additional problems. I’ve already talked to the professor about possibly slowing down the lecture rate, and he was happy I brought it to his attention and would see what he could do without reducing the material we cover.
My other frustrations in the class stem from the remote connection. The microphone that Berkeley’s DSP gave me is powerful, but when other students ask questions in the class, their voices are often too quiet for the captioner to pick up. As a result, most of the time when students ask questions, the captioner has been forced to write down “(Inaudible)” which is the standard way of marking down unknown comments, so I don’t understand the flow of conversation between the students and the professor. And knowing what the other students are saying was one of the major benefits of having interpreting services! In a classroom setting, the professors are much easier for me to hear than the other students, even if those students are physically closer to me. I haven’t been asking the professor to repeat what the students have said, which is my fault — I need to start doing that!
My other, and perhaps most significant frustration with the remote captioning service, pertains to the logistic and technical difficulties we have experienced. The first lecture was fine, but the second was not. I had an on-campus captioner act as a substitute for the remote captioner, but the substitute didn’t get the right room assignment because the professor had to change the room (due to over-enrollment), and I didn’t update it with DSP because, well, a remote captioner doesn’t need to have a room number.
After emailing the substitute about the new room, she was able to find it thirty minutes into lecture, and by that time I was lost since I spent more time worrying about the captioner rather than the lecture material. And even when she was there, it’s hard to catch up on the last fifty minutes when you’ve missed the first thirty.
The third lecture was much better, even if the captioner had trouble typing in some of the technical terms — I sent her spellings some of the terms to make things easier. For the fourth lecture, though, I had a substitute remote captioner who needed to use Google Hangout to connect to me (I had used Skype earlier, as was the default). And we ran into a problem: even after connecting ourselves with Google Hangout, she couldn’t hear anything that was going on in the class.
We finally resolve the issue thirty minutes later — she installed Skype, and I removed the microphone I had been provided with and relied on my laptop’s internal microphone, and suddenly that worked. I have no idea why. But that class was a disaster. For the first thirty minutes, I was constantly on Google Chat with my remote captioner, trying to fiddle around with settings on my laptop to get her to hear what was going on in the class (I bet the students sitting near me were wondering what I was doing). And again, when you miss the first thirty minutes of a lecture, it’s hard to catch up on the last fifty.
Fortunately, I don’t think I will have connection issues in the future. I had a meeting with the primary remote captioner and we spent an evening trying to resolve our technical difficulties. Berkeley’s DSP also provided me with a more powerful microphone.
The fifth, sixth, and seventh lectures were okay, but technical problems continued during lectures eight and nine. In both cases, the captioner ran into problem with her own computer, so I wasn’t able to get captioning shown on my laptop until 28 and 12 minutes after lecture started (respectively). And while the tenth and eleventh lectures were free of notable problems, I’ll still be carefully monitoring any future technical difficulties (as I have been doing so far) and will send Berkeley’s DSP a report on it at the end of the semester, when I will re-evaluate whether I want captioning services at all (and if so, whether they should be remote).
So I guess the point is, while remote services sound pretty cool, be wary of technical difficulties that could happen, along with heightened difficulty of knowing what other students are saying.
Now let’s talk about my other class.
Statistical Learning Theory
As I mentioned earlier, SLT is a standard mathematics and statistics course. The professor lectures by writing on a chalkboard (we have no slides) and assigns us biweekly problem sets. I sit next to my captioner in the front of the classroom.
It might be hard to believe, given my description of NLP earlier, but captioning for SLT has been perhaps even less effective, thought this time it’s largely due to the material we cover in class.
Consider how captioners do their job. When captioners type, they type based on sound cues, and their special machines combine those cues together to form common English words. Captioners do not type word by word on a QWERTY keyboard like most of us do, because that would be too slow and introduce numerous typographical errors.
By now, you might see the problem: their machines are designed to recognize and auto-complete common English words. By typing in several sound cues, a captioner can quickly print phrases or long words on the screen that are automatically spelled correctly. With a technical class, however, these phrases or words suddenly aren’t that common, so the screen doesn’t auto-fill their text because advanced statistics terminology isn’t in its dictionary. The way to get around this is to pre-assign words to sound cues in the machine. For instance, my captioner has assigned the word Gaussian to the spell-checker so that it will print it out according to the appropriate sound cues, rather than print text like “GAU SAY EN” on screen. (Note to anyone who’s taking CS 281a: you’ll be playing around with Gaussians a lot.) But it’s still a problem in my class because new and old advanced terms are thrown around every lecture.
And to make matters a little worse, not everyone in the class has great articulation (according to my captioner).
Putting it All Together
There’s a common factor to both of my classes that might be a reason why I’m not getting the most out of the lectures: I’m not used to CART. So maybe there’s a bit of an adjustment period as I determine the optimal combination of looking at the professor and looking at the computer screen.
But I don’t think adjustment can explain all the difficulty I’m having in my classes. At the start of the semester, I sat through one of Maneesh Agrawala’s lectures on visualization, and my captioner had no problem at all (and I understood what was going on). In fact, I think that she did obtain around 99 percent accuracy in that lecture. Maneesh has a remarkable ability to speak at a reasonable pace and he throws out pauses in judicious locations. It shows that one’s experience with captioning can vary considerably depending on the speaker and other factors.
That doesn’t change the fact that, so far, I feel disappointed that I haven’t gotten more out of class lectures. I do make this up by spending a lot of my own time reviewing. Every few days, I will spend a full workday, 9:00am to 5:00pm, just reviewing lecture material. I don’t mind doing a lot of this work on my own, but I’m worried that if I have to keep doing this, it will take away time from my research. I don’t want to be consumed with classes, but I also have minimum GPA requirements, so I can’t slack off either. The better thing would be to do a lot of reading before the class, which admittedly I’ve been slacking off on due to giving priority on research and homework, but if I’m not getting much out of my classes, I’ve got to change my strategy.
Overall, being in my classes has been an incredibly frustrating experience for me, as I’ve had to spend several full days reading my textbooks about concepts that I think most other students got right out of lecture. This has been a major factor in what was an unusually brutal September for me, though again, to be fair to Berkeley, last September was arguably less stressful for me than September of 2013.
Nonetheless, I do feel like I am learning a lot, and I do feel like things will improve as the semester progresses. But in the meantime, I know there’s only one thing that can make this easier: doing a ridiculous amount of self-study. Do the readings, find online tutorials, do whatever it takes to learn the stuff discussed in lecture, ideally before the lecture occurs. Doing a ton of reading before lectures has proven to be a rock-solid learning strategy for me.