Category Archives: Education

I love cumulative exams.

In a recent Faculty Focus post, Maryellen Weimer argues in favor of cumulative or comprehensive exams, which most of my students aren’t particularly fond of.  [grin]  Put very simply, she says “Students don’t like cumulative exams for the very reason we should be using them: they force regular, repeated encounters with the content.

This post is a reminder to me to include more of my old test questions.  They are ideal for in-class discussion, formative and summative assessment of student understanding for a course unit, and even to help me identify vague or confusing questions.  (There have been a couple of those lately–so I’ve resolved to punt them off the exam and use them in class next semester so I can figure out how to fix them.)

I also love the ideas to bring up older course material as a way to instill better cumulative/comprehensive study habits.  Almost all the courses I teach have at least some long-term scaffolding to them (shouldn’t we be designing our courses that way???), and I need to do a better job of cultivating these skills in my students.  This was spring break week on my campus, and Monday I think I’ll open with a pop quiz based on her last item:  “Your friend Leo wasn’t in class last week.  He texts, asking what happened in class.  Text Leo a short answer and don’t tell him ‘nothing’.”

On scaffolding and learning outcomes

About 10 minutes into the first class meeting…..”I expect all of you to come to class prepared…”

Yawn.  *clicks back to Facebook tab*

Prepared to do what?  Listen to a lecture for an hour every day?  How do I need to prepare for that beyond making sure my laptop battery is fully charged?

Or perhaps, we should ask (and expect) our students to show up prepared for an active class period full of questions, problems, what-ifs, discussions, and ultimately some answers.  So how do we do that?  I try my best to follow Robert Talbert’s guide to creating learning objectives.  Seriously, that little blog post may be one of the best explications of how to design effective, active classrooms that’s ever been produced

To provide an example, I’ll return to the “difference between HP and LP supercells” outcomes I wrote about last fall.  In a course unit on supercell archetypes where we want students to “know the difference between LP and HP supercells,” we might come up with these:

“At the end of this section, students will be able to:

– state the physical differences between LP, classic, and HP supercells;

– recognize the different overall structure between the conceptual models of the three types;

– identify which types are more/less likely to produce tornadoes, strong “straight-line” winds, and large hail;

– describe how environmental wind shear plays a role in modulating supercell type;

– describe how atmospheric water vapor and/or cloud base height play a role in modulating supercell type;

– sketch and label archetypal models of LP, classic, and HP supercells, including cloud and precipitation extent, updraft location relative to precipitation, surface outflows, and the most likely location of a tornado;

– differentiate between supercell modes using radar data; and

– differentiate between supercell modes in photographs and/or videos.”

Wow.  That’s a lot to accomplish…probably multiple classes!  I think these are listed roughly in order of increasing difficulty–which is the point–and it would be very easy for me to just sit back and put together 80 or 90 slides that cover everything, and then test students on what I think is important.  But I’d rather have them do some Preparation H (homework!) and come to class armed with some basic vocabulary and knowledge.  That way, we can get further down the list and spend our class time really diving into and interpreting the radar data, pretty pictures, and complex videos.

So here’s my approach.  Before the class period(s) on supercell types, students are expected to be able to perform the first three, maybe four, tasks on that list.  Yes, I expect them do be able to do these things before coming to class.  The activities for this unit would probably involve reading (Ahrens Extreme Weather and Climate pp. 311-5), looking at additional material (this nice and quick summary by Zach Roberts), and answering short questions online before class (the Just-in-Time method; my example questions for both days of this unit are at the bottom of the post).  If students have done the requested assignment, and spent time digesting it and trying to understand it, they should be able to answer these questions easily and will be well prepared for two productive and fun class periods.

Class time would begin with a follow-up on anything that students identify as still unclear, and a review of their answers to the pre-class questions.  This typically only requires 10-15 minutes; after that, we move on.  For the students who didn’t answer the questions, and didn’t show up prepared, they are going to be seriously lost for the rest of class.  It is imperative that students realize their success in class depends on coming to class prepared.

It’s then on me to select the right kind of in-class activities to achieve the last 4-5 objectives in the list.  Just a few ideas:

  • I might draw three different vertical shear profiles on the board, and ask students to discuss and then draw in what the Cb would look like — barely tilted in weak shear, somewhat tilted in moderate shear, and very tilted in strong shear.
  • Provide a printed color copy of reflectivity and velocity data for a classic supercell, to let students draw on and place the mesocyclone relative to the precipitation.  (And then identify and label the “hook echo.”)
  • Giving the customary supecell schematic with most (or all) of the labels missing, and ask students to label pertinent features.
  • Display Figure 7 from Rasmussen and Straka 1998, and ask students to identify which bar graphs correspond to LP, CL, or HP storms.
  • Or others, depending on how students respond to the pre-class questions.

This set of learning outcomes, arranged in approximate order of difficulty, is precisely what I would give students as a “study guide” for these class meetings.  Exams would include questions that test a range of these outcomes.  The ‘A’ students will successfully use photos and videos and radar, the most complex tasks we do; the ‘B’ students will be able to relate supercell types to environment and to label their models; the ‘C’ students may only know the LP/CL/HP differences and the tornado risk from each.

I’ll close with Talbert’s last sentence, which is a superb summary of this process:  “This is far from a perfect system, but it’s a reliable way to align learning objectives with the actions you want students to perform and the means you want to use to assess them, and it gives students a key ingredient for self-regulated learning: A clear set of criteria that will tell them what they need to know and how to measure whether or not they know it.”

– – – – –

Students submit answers to these questions online before the first day:

1. The textbook omits large hail from the list of hazards for one of the three supercell types.  Which type is less likely to produce large hail?

2. Name two differences you see between the HP and LP photographs in the online reading.

3. Radars can be helpful in determining supercell type (LP/classic/HP).  Why do you think that’s the case?

4. Name one thing about either reading that was unclear, confusing, or that you would like clarification on.

Before the second day:

1. The author of this video has declared this storm to be an LP supercell.  Do you agree?  Why?  How would you compare it to the photo and schematic you’ve already seen?

https://www.youtube.com/watch?v=BF8XManeSn0

2. Name one thing that was unclear about yesterday’s discussion that is still unclear, confusing, or that you would like more clarification on.

Turbulence and mixing at the power plant

Hot, moist exhaust from the campus power plant stacks mixed with frigid air this morning to produce some great turbulent flow and even some “mixing clouds.”  (Video is 90 seconds; I zoom in a bit at 60.)  My favorite part: notice that the interior of each plume remains somewhat clear–that’s the part of the “updraft” that entrains ambient air last, so in this case, it stays clear the longest.  Neat!

As for the clouds, this is the same process that causes airplane contrails, and your breath to appear in winter.  For more information, here’s the AMS Glossary definition of mixing clouds and here’s a quick explanation using temperature and vapor pressure from the Hong Kong Observatory.

I’ll look for a couple of good explanations & links related to entrainment and update this when I have the chance.

Why I don’t give extra credit

Since the title explains itself, I can dive right into the reasons.  There’s not always a need for a long, drawn-out introduction!

1. I’m a meteorologist, and we don’t do that.

Put very simply, we don’t get a chance to “make up” for a bad forecast.  We can only learn from our mistakes, move on, and do better the next time.  It’s illogical to think that a forecaster’s mistakes — no matter if they are major or minor — can somehow be washed off his record by doing bonus work.

An example of minor mistakes: the forecaster who continually has a warm bias to his temperatures.  Once you realize what you’ve done wrong, fix it!  Explaining to people why you made the mistake is important, yes — but does not absolve the mistake.  (See #2 below for more on this.)

A major mistake: missed forecasts can have fatal results.  In a recent 5-year period, 17 tornado fatalities occurred without a tornado warning being issued (Brotzge and Erickson 2009).  There is no apology great enough to overcome this one.  I know this all sounds painfully harsh and trite to those who aren’t as familiar with the forecast process.  But put simply, in our field these are the lessons we face every day.  I’m a firm believer that students should experience science as science is practiced, and a no-extra-credit policy is a clear application of that principle.

2. My ideas of sound pedagogy don’t support that model.

Four reasons instructors may offer extra credit are given in this Faculty Focus blog post from 2011.  None of them convince me.

  • “It reduces student anxiety and builds confidence.”

This is the only one that holds some water to me.  But this is exactly what practice assignments and homeworks should be designed to do, right?  Build confidence so that students can perform well on major assessments?  Aren’t we already supposed to be designing our courses to that students are well-prepared for exams?  I don’t understand how something like attending an evening seminar about a peripheral topic (a classic extra credit idea) builds confidence.

  • “If learning is the goal and students haven’t learned important content, extra credit offers a second chance to master the material.”
  • “Not all students ‘get it’ the first time.”

Teachers of college writing know that revision is a key to students improving their writing skills.  In the hard sciences we might use the word practice, especially in meteorology where forecast opportunities are fleeting and revisions to previous work aren’t possible.  I don’t remember but I’m pretty sure my first few forecasts as an undergraduate were awful, and that they improved with repeated practice (to the somewhat less-awful state they’re in now!).  Our assignments and courses should be arranged to give students multiple opportunities to master difficult content before a major assessment takes place.  When we don’t provide this structure, we are less effective teachers.

  • “Students are motivated to do it, so why not capitalize on this motivation by creating a robust learning opportunity.”

It’s a bit cynical but to me the implication here is that students aren’t motivated for ordinary classwork.  I certainly hope that’s not the case!  Every learning opportunity should be robust and motivational.  If it’s not, it doesn’t belong in our classroom.  Why should we relegate our most creative assignments for extra credit opportunities that may get done by only a handful of students?

One thing to point out is that I differentiate between large, formal “extra credit” assignments and the rare “bonus” questions that occur on a quiz or an exam: Michael Leddy offers a nice example and his take here.  Most often, I use those to help me scale exam or course grades to better align with student expectations (I’ll rant about the insistence of a 90-80-70 letter-grade cutoff some other time).  But my students can attest that I do this about once per course and is part of an assessment that already exists.  My bonus questions are always opt-out (right there on the page for you to try), not opt-in (available only if you ask or by doing something else external to class).  I’ll avoid saying much about the ethical issues of opt-in extra credit, too, beyond saying that they terrify me.  Is the extra work only available to students who ask?  Are they allowed to tell their peers?  What if someone can’t attend that special guest speaker’s talk because of their job or family?

So there you have it.  Let’s make our coursework compelling the first time ’round, and let’s create assignments that are not busy work but help students learn what we truly want them to do.  That way, they get it right when the grades are on the line.

Why meteorologists shouldn’t “teach to the middle”

Once every decade, we take the temperatures of the last 30 years, average them together, and refer to this as the “normal” temperatures for a location.  For example, when you see on the nightly weather report that the “normal high for today is 84 degrees,” that’s simply the average of all the highs for that day from 1981 to 2010.

The number 84 is an average.  Very few, if any, days in the record will actually have had a high temperature of exactly 84!

The same goes for our students.  In any given class, the number of “average” students, perfectly in the middle of the distribution, will be quite small.[Footnote 1]  My argument is this: if we teach to the middle, we alienate and bore our upper tier of students (who are our future colleagues) and at the same time work over the heads of weaker ones who may need the most help.  We likely reach those few students who are truly in the middle of the distribution, but overall to me this is a lose-win-lose situation.  Losing two battles every day is not how I want to spend my career.  Furthermore, the standard we “set by teaching to the middle is a standard of mediocrity.”  It’s okay to be average, kids.  Everyone gets a ribbon.

What, then, is the answer?  Is there one?  How can we possibly differentiate learning when faced with 100 students, or even 40 or 50?  Facilitating a classroom that promotes learning already requires lots of work, and most academics I know don’t believe they have any additional time to devote to it.  Here are some rough ideas, certainly a non-exhaustive list but maybe a starting point at least.

1. Variety in course assignments.  Some of our students will be math stars, while others are incredible artists who struggle mightily with college algebra.  Offering different types of work — calculations, concept mapping, figure interpretation, opinion essays, etc. — allows all students to take part.  I like to believe everyone is good at something.

2. Variety in in-class activities.  I pray that the days of lecturing for an hour a day three days a week are dying (an albeit gruesomely slow death, but still dying).  And reading text on slides as they appear on the screen doesn’t teach to anyone, let alone the middle.  In-class activities and discussions can be like #1 above and also varied in level: a mixture of easy concepts, medium concepts, and the occasional mind-bender sets up a class that everyone can get something out of.  Structured group and team-based activities, discussions, or even quizzes (yes, group quizzes!) help also.

3. Structure in assignments and activities.  “You need structure. And discipline!”  In a room of professionals, we could get away with the activity ‘hey let’s pull up today’s 500-mb map and just talk about it for awhile.’  However, this will likely fall flat in a room of mixed majors or gen-ed students.  At least when I’ve tried it, it has.  Even off-the-cuff activities need structure and scaffolding (take small steps: first let’s find the ridges and troughs, and the vorticity, and the temperature advection, and then ask where are the likely surface features, etc.).

The bottom line here is that we have to find ways to involve everyone (or, realistically, as many people as possible) in the room in the learning process.  If “teach to the ____” is just code for “at what level do I pitch my lectures?” the problem goes much deeper.  To me, the room is more about what learning will be taking place, rather than what teaching will be taking place.

We’d be hard-pressed to find a string of perfectly “average” weather days, instead finding runs of hot and cold which both have their own fun and own beauty.  And each of our classes is made up of much more than a blob of “average” students who are the only ones to deserve our attention.  A classroom includes a spectrum of abilities, and everyone learn something when courses are thoughtfully organized for more than just what we believe the “average” student is capable of doing.

Footnote 1:  Some readers will want to start talking about normal distributions at this point.  I ask, are the students that are at +1σ and -1σ at the same skill level?  What’s really the “average” group, then?  +0.5σ to -0.5σ?  That’s now less than 50% of your class.  The bounds get smaller and smaller…

“The Points Don’t Matter”

[TL;DR:  Tthere is not much difference in the average grade for a course if you redistribute the weights for exams, homework, and the like after the fact.]

When students see a new course syllabus for the first time, the first thing many look for is the breakdown of grading for the course.  “What do I have to do to get the grade I want?”  At least I always did.  Every semester, every class.  Not ashamed to admit it, either.  That university curricula are so grade-centric instead of outcome-centric (and have been for decades) is a rant for another page, and has been addressed thoroughly, here, here, and here among probably a dozen other places.

But does the course grade breakdown really matter that much?  That is, do the weights we assign to each category of work truly have a large impact on final course grades?  To find out, I pulled up the grades for an introductory course I taught a couple years ago and recomputed their final grades using five different weight combinations.  There were about 30 students in the course, and in terms of structure it was rather mundane: lecture, homework, quiz, exam.  It was earlier in my teaching career; forgive me!

Here are the breakdowns I tested, using all the assignments we did that semester:

Homework Quiz Exam 1 Exam 2 Final
Option 1 25% 15% 20% 20% 20%
Option 2 40% 10% 10% 10% 30%
Option 3 20% 10% 20% 20% 30%
Option 4 20% 10% 15% 15% 40%
Option 5 30% 20% 15% 15% 20%

Depending on the instructor, I think any one of these breakdowns would be pretty standard for a lower-division science course that doesn’t have much of a team-based or lab component.  But standard as they might be, each of these five would potentially have huge impacts on student perception of the course and the instructor (especially option 4. Brutal!).  And I’d say it’s highly likely that study and work habits would be different too, depending on what the actual scale was.  I know of no way to test how different those habits would be if students had been presented a different distribution up front — we can only look at how grades would be different after the fact.  If you know a better way, please hit the comment box below.

So yes, I’m making a key assumption here:  to make this comparison I have to assume that perceptions and study habits and such would not be different as students complete any given activity, regardless of which of the five breakdowns would be used.  Again, I know this is a stretch.  For each option, here is the distribution of the students’ final grades:

Highest 75th %-ile Median 25th %-ile Lowest
Option 1 99 87 80 70 53
Option 2 99 87 81 67 54
Option 3 98 88 81 70 52
Option 4 99 88 81 68 51
Option 5 99 86 80 70 53

From a class-average point of view, every option gives a nearly identical distribution!  The greatest variability occurs, expectedly, at the bottom of the distributions which includes students who were badly deficient in one of the categories (rarely attended class so had quiz grades < 50%; missed or didn’t turn in key homework or team assignments; poor test takers; etc.).  I also checked the number of students who achieved 90%, 80%, etc., as those would be my rough cutoffs for letter grades.  No surprise: for this course the number in each category changed by no more than one student (out of ~30) regardless of which category distribution was used.

Because it’s much more recent, I won’t show the results from another course, although they are very similar.  To me, it’s clear that as long as the distribution chosen is a reasonable one, the actual percentages simply don’t matter that much to final grades.  We’ll almost always curve a point or two, here or there, to accommodate bad exam questions and grading mistakes and uncertainty and whatnot, and so even the variability in the lower half of these distributions is just in the noise to me.

Have I tried to use this information to the advantage of my students?  Yes.  Given that test anxiety is real and observable, I’ve lowered the stakes on my in-class exams (toward something like option 5 above) so that those assessments count a little less, and the untimed and out-of-class work counts a little more.  Because of the tendency to think of out-of-class work as “grades I earn” and exams as “grades you give me,” students hopefully will take more ownership of their learning when the percentages shift in their favor.

Even though, ultimately, the points don’t matter.  Much.  🙂