Via Audrey Watters, there’s a great article out today about how startup culture, with its preference for “failing fast” instead of doing research, is killing innovation. It’s worse than that, though, in MOOC world, where startup culture is wasting the time and good intentions of millions.
Here’s an example of an actual question that my boss found in a MOOC she was taking:
This diagram illustrates the important ideas behind the Malthusian model of population. The green curve represents the total population, and the beige line represents the total amount of food available.
If the Malthusian theory is accurate, why do the curves in the figure not follow the paths shown?
- All of the other choices are correct
- After the point of intersection, the curved line should not exceed the straight line.
- The curved path will actually become a constant
- The flat path will increase exponentially
- Before the point of intersection, the curved line actually matches the straight line
No matter what your friends may tell you, a well-constructed multiple choice question can be a wonderful learning opportunity. That’s not the case here. This question is a hot mess. The question, as phrased, asks why the curves on the graph do not follow the paths of the curves on the graph.
So, if you’re like most people, you sit and parse this out. I’m a former Literature and Linguistics major who eats complex constructions for breakfast, but it took me two minutes just to parse out what the question means. None of that effort, by the way, increased my knowledge of either the English language or Malthusian economics. After parsing it, it takes another minute or two to walk through the options, which are oddly structured.
There are dozens of ways in which this question is wrong.
- Its “All of the other choices are correct” option is bad.. Experts in multiple choice testing will tell you to avoid this option at all costs, as it causes processing issues and tends to reduce the validity of the test.
- Its answers should be parallel in structure. They’re not.
- It includes incomprehensible and meaningless distractors to get the answer count up to five, when best practice in these constructions suggests three plausible distractors is a better route than putting nonsense up.
- It references “curves”, but most students will only see one curve and one straight line – seeing these both as curves requires a vocabulary the student doesn’t necessarily have.
- Its question is not comprehensible without the answers. The answer any intelligent person would formulate reading just the question “Why do the curves not follow the paths shown?” is “Because we run out of food.” But that is not actually the question the answers answer.
The larger issue here is it is unclear what this question actually tests. I suppose it is supposed to see if students understand the fundamental insight of Malthus, but if it is, it feels a bit sloppy. It looks like an application question, but it really is just a confusingly worded recall question.
I didn’t come up with these critiques on my own. A group of people at Brigham Young University has spent decades researching how to create effective multiple choice questions. They’ve boiled their research down to a four page checklist you can find here.
You can use that to reformulate the question this way:
This diagram illustrates projected trends in population and food production. The green curve represents projected total population, and the beige line represents projected food production.
Malthusian theory says this graph is an incorrect prediction of the future. If Malthusian theory is accurate, what will actually happen at point T?
- The population line will change, staying under the food line.
- The food production line will change, bending upward to keep up with population.
- Both lines will change, the food production line bending up, and the population line bending down.
I’m not sure my version completely captures the objective or disciplinary understanding here (I’m not an economist). But that’s a five minute rewrite based on the BYU checklist that will increase the question’s effectiveness both as a learning tool and an assessment.
Now Coursera or others might tell you the beauty of Big Data is that questions like these will be pegged by algorithms and improved over time. This is part of being proud of “failing fast”.
But let’s do the math on this. Say this course has 10,000 students that spent four minutes each on a question that has no validity as an assessment and teaches them nothing but how to read absolutely tortured English. That’s 667 total student-hours wasted on a question because the producers of this course couldn’t be bothered to do a five minute rewrite. That’s 667 hours that students who want desperately to learn are spending not learning. It’s 667 hours they could be spending with another product, or with their family, or volunteering with their community. Or just sitting out on the back patio enjoying the weather.
Multiply that by the number of questions like this in a typical MOOC product. Then take that number and multiply it by the number of MOOCs. You start to see what “failing fast” and “solving problems through Big Data” means in human cost. It’s about cavalierly taking millions of hours away from customers because someone (either Coursera or the university partner) can’t be bothered to pay for an instructional designer.
I get why I see questions like this in local university classes. We don’t have the scale to put an instructional designer in every class. But if the large-scale production of course materials is supposed to solve anything, it’s exactly this problem. The whole point of producing course materials at scale is that we can finally afford to do this right, and tap into the research and professional knowledge that can make these things better. “Fail fast”, when used as an excuse for shoddy work, makes a mockery of the benefits of scale, and treats student time as worthless. And all the Big Data in the world is not going to make that better.