Final Reflection-- Why Vital Ideation isn't over for me.

Summary: I am still not sure about myself as a designer in the context of having gone through all of vital ideation’s lenses, because feel like I still haven’t tapped into a lot of the value of this course. So, I’m going to keep taking it, on my own. :)
Vital Ideation has not really felt like a course to me until this point. The course itself was a Spring 2008 course organized by Olin students, and the was the idea of viewing the world through different "lenses" to influence design. The reason I signed up for the course in the first place was largely instinctive. I was a part of the early meetings when students talked about their ideas for “student-led courses” when vital ideation came up as an idea, but the impulse for actually taking the class was twofold. The first thing to note was that I often told myself: “I would probably love that class,” or even “I’m going to end up taking that class, as busy as I may be.” The second thing of note was other people, who, especially later on would tell me “You know you’ll love this class” or “You know you’re going to take this class.” Well, I signed up, and I am very glad I did.
Now the semester is over, I’m supposed to be done, but I’m not. In fact I’m sitting in the middle of a lounge in the middle of the night trying to synthesize this course, trying to find what personal value is buried for me, and I can’t really find it. I can’t for the life of me think about how this course has made me truly different, in a truly factual and accurate way. That is not to say that I don’t think I’ve learned much from the course, but I feel like its true value is still not known to me. I really do think that vital ideation isn’t over for me at all. The semester has ended, yes, but the class is definitely not over. In fact, the class feels like it is ready to begin, as corny as that may sound. I feel very strongly that vital ideation for me has been nothing more than a springboard for something of personal value that I can’t discern yet. I’ve spent something around 50 hours this semester, with and without others thinking, reading, writing, but mostly talking about a lot of different topics. It is easy to say that the course was about a variety of design lens, and that the end result is a dozen students who are now more aware about their ability to apply lenses to design, but that’s not so true for me.
In the end, I didn’t spend too much time this semester brainstorming and ideating around specific topics like designing for fun, or ecomimicry, or anything remotely close to designing for the next guy. All those were topics for vital ideation, but none of these things hold any real value to me as design lenses. I actually felt like all of our talks were simply an opportunity to engage in discussion with other students and faculty about a variety of topics, and much of the value for me was found in generating the (few) blog posts I did, reading other’s posts, and spending hours on the web finding what other people have written about similarly to myself and others. More value was found in our evening discussions, how they came up in different forms later, how they added to reflection from the UOCD course, and how all of it together made me somehow a bit different. Right now I don’t feel closure when it comes to this class at all. I’ve written about the things I feel strongly about, and started writing about the things I didn’t really care about so much, and then stopped. The most interesting thing for me about this class is how well it has connected with other things, and it is these examples I would like to reflect a bit more on.
First of all, I kept a notebook for a week or two, then spent hours writing about how much carrying design notebooks was a silly fad, and then stopped keeping the notebook. It wasn’t really intentional in that my notebook was buried under a pile of books and left there, but I certainly didn’t care enough anymore to look for it. It is interesting to see how the notebook changed for me, not physically as in what I wrote in it. Rather, it is my interaction with this notebook that changed for me. At first keeping a notebook was a “man, I should do that” sort of thing, but it soon turned into a “well, I’m doing it now, right?” sort of thing, where I couldn’t really seem to sync myself to having and carrying a notebook. It felt so artificial to me, that I stopped writing notes in it, except for very sparingly. I stopped “ideating” with it altogether very quickly and then turned it into a personal notes book for tasks, work, and any other thoughts that I might multitask by writing during a boring French class or ten. Eventually, as I mentioned already I left the notebook behind, and I’m glad I did. I found that pretending to keep an up to date record of my thought processes was not realistic. I have learned this semester that I think mostly out loud. I tend to talk a lot, and most of it is on the fly, not really knowing what comes five words later. Sometimes the most insightful things I feel I say I don’t actually understand until 5 minutes after I’ve said it. The result of this is that I wouldn’t record things accurately in a notebook, since I felt like I was recording meaningless things. Also, much of my thought process is dependent on clearing my mind and just thinking about something, which didn’t match up too well with recording my every thought on paper for a course, or even for myself.
The second point of connection that this course has had for me also syncs up nicely with another one of my blog posts, which originally was written halfway through the semester. I was a part of a one-credit education research project this semester which involved going on a trip to a high school in rhode island called the MET. We spent a lot of time reading and talking about the school, as well as an entire day at the school and many meetings afterwards to debrief on our experience. This course is also something I think hasn’t really gotten the closure I normally feel when a class ends. There is a lot more about this MET school trip and experience left uncovered. My second blog post was about connecting social networking elements with education and the school environment, something that was a very easy connection to make having seen students interact with both during school, even myself. I won’t get into the specifics here, because I want to reflect at a bit of a higher level than this, but overall I found multiple areas of this notion of connecting social networking and pedagogy that paralleled my experience at Olin, the MET, high school, elementary school, as well as through my siblings and others in general. In fact, I hope to ask a handful of students to actually read my blog post, because I know it will spark a discussion that will be infinitely more useful and valuable to me than that blog post was, even though I felt like I did get a lot out of the thought that went into that post.
Another point of connection for me was actually the art and engineering discussion that we had over the course of the semester. I was never able to put this talk and discussions we had about the talk into a blog post however. I always felt like I hadn’t really thought about anything interesting enough to connect to outside of what I had written about for one of my UOCD design reflections, which was as all about how design is a very akin to art. I spent a lot of time talking to my team about UOCD and how it was presented to others in our class. Without a doubt UOCD is one of the most polarizing courses at Olin as far as student reactions and feedback for the course. I personally loved the class, but I am still quite confused about how students seem to feel that the course should have been much more structured and deterministic. It seems like telling students that UOCD was an art class might have made it a lot more digestible as a course for a lot of people. I could try to explain my thought process here a bit more, but I’ll jump to another topic now.
There really is only one thing left for me to say about this course. I’ve decided that I’ve written about everything that I felt I really connected with well this semester. The only exception to this was the design for fun module, which I read other people’s posts for. I’m aware that the course description says that we needed something like 8 blog posts, but I think I’m going to stop at my four right now. (My first post was meant for both sticky ideas and notebooks, hence the length.) I’d much rather spend another 15 hours reading other people’s posts and hoping they spark future interesting discussions that write about topics I currently feel I have nothing new to say. So, after finishing this blog post I think I’ll be spending a couple hours over the course of the next week continuing to take Vital Ideation, but for myself, not really for credit. In the end it doesn’t really matter if I get credit.
Summary: I am still not sure about myself as a designer in the context of having gone through all of vital ideation’s lenses, because feel like I still haven’t tapped into a lot of the value of this course. So, I’m going to keep taking it, on my own. :)

Teaching + Digital Communications = Multidisciplinary Fusion!

On the subject of radical interdisciplinary design, we discussed in class the fusion of music and engineering, something I’m sure we will see posts about in the upcoming days. What I would like to talk about is the fusion of two disciplines which I have personally spent a lot of time thinking about before vital ideation, and am really excited to share with others. At first glance pedagogy and digital communications seem to have little in common. In fact, the notion of pedagogical research going hand in hand with network signaling and digital communications research seems outlandish and foreign at best. The thing is, any two disciplines must overlap in potentially powerful ways, and these two are no exception.

You can see from Figure 1 that the transmitter/receiver model is at least at a very basic level analogous to a teacher/student model where a lesson is transmitted to a student via some sort of signal. In this model a student’s receptivity to lesson X is based off their receptivity to specific teaching techniques. These techniques are used to varying degrees by a teacher, which can be depicted as the power spectral density of said teacher/transmitter’s transmit power, which in turn represents the amount of time teachers spend using a specific type of teaching technique. Figure 2 is a visual way to represent this last paragraph. You can see from the graph on the left, which shows professor “transmit” power as a function of the amount of time (shown on the vertical axis) they spend covering any material using different teaching techniques (shown on the horizontal axis.) Likewise the graph on the right shows student receptivity to different teaching techniques.

What a mouthful. To attempt to explain how this model might be useful, we consider the simple case of 30 students and a choice between two possible teaching techniques, Q_a = Auditory Learning and Q_b = Visual Learning. You can plot a student’s receptivity to these two teaching “techniques” on a two dimensional grid, where one axis is Q_a and the other is Q_b. The axes would range from 0->1 for each technique, where 1 is the hypothetical scenario were you as a student understand EVERYTHING that you learn using a specific technique. The ideal student would of course have a receptivity of 1 for both these values, but that wouldn’t make our model useful. The vector G₁ would represent the 2-D vector representing these two student receptivities. We can create 30 students with randomly generated receptivity vectors [G₁, G₂, …G₃₀] such that each student’s total receptivity||G|| is within an arbitrarily-defined range such as 0.3 < || G|| <>

Now, in the digital communications world you represent the power spectral density of a signal by multiplying the transmit power and channel receptivity. For us this means that in order to determine amount learned we can multiply the learning technique time distribution vector (in our 2-d example) for a professor by a student’s receptivity vector (for the two techniques) to give the amount “received as a signal” by a student from each “technique” The sum of the area under this student curve is the “amount learned”!

I will conclude the following post with an explanation of the following figure, which shows the example case of our 30 randomly generated students and the imaginary teacher who hypothetically could teach them any possible range of two specific techniques. What this translates to in the end is the following choice for a teacher: How much time do I spend say, watching videos as opposed to lecturing? Now, this case is obviously ideal because we can’t assume that we will know the exact “receptivity” of each student to a specific technique, and that this directly translates to amount learned, but in this end this is just a model of a teacher/student system. Every model is broken right? The only perfect model of a classroom environment is the classroom itself!

Each parabola shown in the figure above represents the projected learning efficiency E for a given student across the different mixes of auditory and visual learning techniques output by a professor. The vertex of each parabola corresponds to the student being taught by a teaching style that most perfectly matches his(her) receptivity G_n This value corresponds to the mix that represents that student’s highest learning efficiency. If we were teaching only one student, we would therefore choose the Auditory-Visual mix to coincide with the student’s vertex in this plot. However, we must teach to the entire class; so how can we select the direction of T_x? If our goal were to teach at a rate that did not exceed any student’s learning efficiency, the plot above implies that we should select about an equal mix of Auditory and Visual techniques, and we should choose a teaching rate (i.e. learning efficiency) of approximately 0.27. Qualitatively speaking, this corresponds to the highest learning efficiency in the plot that is below every parabola (or, the teaching rate below every student’s maximum learning efficiency). Instead of choosing this “lowest common denominator” approach, we may elect to forgo the few students with the lowest receptiveness in order to increase our teaching rate. Based on our understanding of the figure, any intelligent selection will exist at either the intersection of two parabolas or at the vertex of a parabola. We therefore limit our search to these points. You could envision a graph where you highlight only these two types of points and as you eliminate students you would move further up on the graph and around to different points to maximize the learning efficiency for the remaining N students.

The process for choosing this path up the graph can also be described intuitively as follows. Imagine you pressed a single finger up from the bottom of the last image shown. It would naturally center itself at the highest vertex or parabola union, which corresponds to the teaching technique mix (and the maximum learning efficiency threshold) that you would use to teach all 30 students. Next, say you wanted to exceed this threshold; you would effectively “ignore” one of the lowest parabolas and move to the next highest available point. This would be akin to teaching above the maximum learning efficiency for one of your students. This allows your technique mix to adjust itself to find the next maximum point. We can also plot the learning efficiency threshold height as a function of the number of students above the threshold; as discussed above, you can expect the maximum available threshold to increase as the number of students above the threshold decreases. With one student, assuming of course that our professor can transmit something perfect to that student’s receptivities, you achieve a learning efficiency of 1. With a decreased number of students under the threshold you achieve a lower perfect teaching efficiency for those students.

You could envision a scenario in this model where you chose your teacher signal at a point along this “path” up the vertexes and parabola union points that would maximize the overall amount of learning in the classroom. The “perfect learning efficiency” model up to this point hasn’t taken into account the fact that in the end the choice of a teacher’s T vector will lie somewhere on the 2-D student receptivity space for each student, and that on this space each student has their own perfect learning receptivity point. A teacher wanting to optimize his signal output would first decide an acceptable threshold for student learning, and then determine which of these points minimizes the distance between all the students he(she) is trying to teach for.

I welcome others to come up with two other seemingly random disciplines and merging them together somehow! I'm sure you can think of something in a quick 10 minutes. The basic idea for this post came about after a discussion asking the question "What if all knowledge could be plotted on a n-dimensional grid?" Without meaning to this successfully put linear algebra and digital communications in the same discussion space as pedagogy and learning. There are many more questions where this one came from, the real question is, do you ask yourself silly questions often enough to find real value in some of them?