Time is Knowledge

I’ve been thinking about metaphors. I blame it on the philosophical tome that has taken me over two months to finish reading: Philosophy in the Flesh, by George Lakoff and Mark Johnson. I previously blogged about it when the authors got me thinking about how we navigate living in a mesocosm, and how we use metaphor not just in everyday communication, but to advance learning and knowledge.

 

Today’s metaphor: Time is Knowledge. In the higher education business, we have long used a proxy known as “seat time” coupled with grades to quantify learning. I will discuss both seat time and grades in a moment, but first let’s talk about why learning is quantified with such seemingly simple proxies. Before the massification of education, there was no need to reduce learning to a mere number. Everyone would quickly know if you’d learned something or not – ignorance is not so easy to hide in a smaller community where you all know each other. You can quickly figure out who is full of hot air and who is actually capable. That’s still true today: Think about the people you work with or the people you hang around – if you’ve known them for a while, you’ll know what I mean.

 

But in the modern era of increased mobility and the flattening of hierarchies (including entrenched ones), it’s not so easy to tell. Hence, the rise of credentialism and wanting to appear fair. Who should you hire? Who should be admitted to your prestigious program? Who should be awarded a merit scholarship? Do cover letters or references letters from people you don’t personally know help separate the wheat from the chaff? Do you even have time to read the mountain of text from an applicant’s materials? If only we had the impartial Goblet of Fire! Screening has even been outsourced to bots. I’m glad that didn’t happen back in the day I was applying to stuff. The short answer is that when faced with a mound of “data”, you use proxies as shortcuts. This is a very natural thing to do. We do it all the time, throughout our day, often without realizing it even from the simple act of speaking with metaphors we no longer notice.

 

Say I’m looking at an applicant’s file and trying to assess if he or she has requisite background knowledge, whatever that might be. A student’s measure of university learning (at least in most U.S. universities) is measure by seat time. I won’t go into the history of the Carnegie credit-hour, but suffice to say that it follows the expected evolution of a descriptive model self-reinforcing itself into a prescriptive model – a large topic for another day. Instead, let’s cut to the chase. Here’s my version of how to think about the credit hour in the natural sciences:

 

A three-credit-hour class, your typical “lecture” class meets three hours per week. There’s an assumption that you should be doing six hours of work outside of class to support your learning (the 2-to-1 rule of thumb) although my guess is few students actually do this unless you’re giving regular homework and problem sets that actually take a substantial amount of time. A one-credit “lab” course often meets once a week for 3-4 hours. Back in the day, when labs were more cookbook, there was little outside of class work, hence, it’s only worth about a third credit-wise. Nowadays, as lab and lecture courses are often taught separately by different instructors, there’s usually some outside of class work associated with the lab. (I personally favor a 1-to-2 rule of thumb: For a four-hour lab, students should expect to do two hours outside of class work to support their learning – and we should make the lab worth an additional half or one credit hour if one were keeping count.)

 

For example, in your G-Chem 1 semester-long course, we’ll count your time spent as 9 hours for the lecture and 6 hours for the lab per week. Thus, a semester long lecture is 9 x 15 = 135 hours, and a lab is 6 x 15 = 90 hours. Ignoring the physics and math requirements for the moment, a student majoring in chemistry might be required to take 10 lecture courses and 8 lab courses. The total time spent is 1350 + 720 = 2070 hours, which we can round down to 2000 hours. We award you a degree in chemistry if you took these classes (with passing grades) as a measure of your knowledge in chemistry. What did we do here? We substituted something that is not so easy to measure (knowledge, learning, etc) with something easy to quantify – the credit-hour seat-time proxy.

 

Going a little further, if you wanted to jive with the “10,000 hours makes you an expert” myth (which I don’t personally believe), you can think of a full-time job in chemistry (or perhaps graduate school) as spending 8 hours per day for 250 days per year (excluding weekends and holidays) which equals 2,000 hours per year. Do this for four years, add your undergraduate learning time, and ta-da – you’ve achieved your 10,000 hours!

 

But how well do you know your subject material? Are you (A) excellent, (B) good, (C) meh-okay, or (D) poor at it? We have another proxy: the letter-grade, which is usually determined (at least in the sciences) quantitatively from some total number of points spread across exams, homework, lab reports, and whatever else was in the syllabus. To put a finer point on it for the reader of your application who doesn’t know you personally, we’ve even computed a single number – the GPA or grade point average – to sorta quantify your knowledge. So there’s seat-time mediated by a quantitative proxy of quality, as oxymoronic as that sounds.

 

The current fad in education is to move away from test scores and “traditional” exams as assessments to something more “holistic”. This is for a variety of reasons – you’ll hear different ones depending on who you talk to. But the root of the problem has not been addressed: how do you differentiate the worthy candidate to hire, to admit, or to award – when you don’t know all or any of these people personally in a small community over a period of time. Every system of assessment has its built-in biases. Quantification tries in some way to reduce a number of biases, but it has its own biases which are not necessarily mitigated by qualitative approaches that introduce their own sets of biases that could well make the overall bias worse – if such a thing could even be measured on some sort of a scale, that sorta implies at minimum a form of quantification. See how slippery this topic can be!

 

I expect to continue giving final exams in my G-Chem and P-Chem courses as the main mode of summative assessment. Over the years I’ve given a lot of thought to the pedagogy within these courses, where the students are developmentally with regard to chemistry as a subject matter, and considered (and tried) various alternative assessments. My exams in these courses are primarily short-answer questions that sometimes require a calculation, and often ask the students to demonstrate they know something conceptually. The instruction to “explain” often accompanies the exam question. For other courses that I occasionally teach (e.g. special topic or elective courses, research methods), I use other modes of assessment depending on the course goals. I have yet to try a Reverse Final.

 

And yet after all that, how much have my students learned? Does their seat time and assessed grade capture the complexity of knowledge acquisition? Probably not. It’s a proxy. A metaphor. Time is knowledge, broadly speaking. The more time the students spend thinking about chemistry and struggling through the homework and getting the concepts to ‘stick’ in their minds, the more they have likely learned. But it’s a crude measure at best. So are letter grades and the GPA. And until the Carnegie system is overhauled, this metaphor will continue to reign. But will its successor be any less oppressive a system?