Helping Students Ask Questions (Inspired by Peer Instruction)

February 18, 2013 in Assessment, Classroom Life, Inquiry, Things I've improved | Leave a comment

This year I’ve really struggled to get conversation going in class.  I needed some new ways to kick-start the questioning, counter-example-ing, restating, and exploring implications that fuel inquiry-based science.  I suspected students were silent because they were afraid that their peers and/or I would find out what they didn’t know.  I needed a more anonymous way for them to ask questions and offer up ideas.

About that time, I read Mark Guzdial’s post about Peer Instruction in Computer Science.  While exploring the resources he recommends, I found this compelling and very short PI teacher cheat sheet. I was already curious because Andy Rundquist and Joss Ives were blogging about interesting ways to use PI, even with small groups.  I hadn’t looked into it because, until this year, I’ve never been so unsuccessful in fostering discussion.

The cheat-sheet’s clarity and my desperation to increase in-class participation made me think about it differently.  I realized I could adapt some of the techniques, and it worked — I’ve had a several-hundred-percent increase in students asking questions, proposing ideas, and taking part in scientific discourse among themselves.    Caveat: what I’m doing does not follow the research model proposed by PI’s proponents.  It just steals some of their most-easily adopted ideas.

What is Peer Instruction (PI)?

If you’re not familiar with it, the basic idea is that students get the “lecture” before class (via readings, screencasts, etc), then spend class time voting on questions, discussing in small groups, and voting again as their understanding changes.  Wikipedia has a reasonably clear and concise entry on PI, explaining the relationship between Peer Instruction, the “flipped classroom”, and Just-In-Time Teaching.

Why It’s Not Exactly PI

My home-made voting flashcards

My home-made voting flashcards

  • I don’t have clickers, and don’t have any desire for them.  If needed, I use home-made voting cards instead.  Andy explains how effective that can be.
  • I prefer to use open-ended problems, sometimes even problems the students can’t solve with their current knowledge, rather than multiple-choice questions.  That’s partly because I don’t have time to craft good-quality MC items, partly because I want to make full use of the freedom I have to follow students’ noses about what questions and potential answers are worth investigating.
  • Update (Feb 19): I almost forgot to mention, my classroom is not flipped.  In other words, I don’t rely on before-class readings, screencasts, etc.

What About It is PI-Like?

  1. I start with a question for students to tackle individually.  Instead of multiple-choice, it could be a circuit to analyze, or I might ask them to propose a possible cause for a phenomenon we’ve observed.
  2. I give a limited amount of time for this (maybe 2-3 minutes), and will cut it even shorter if 80% of students finish before the maximum time.
  3. I monitor the answers students come up with individually.  Sometimes I ask for a vote using the flashcards.  Other times I just circulate and look at their papers.
  4. I don’t discuss the answers at that point.  I give them a consistent prompt: “In a moment, not right now but in a moment, you’re going to discuss in groups of 4.  Come to agreement on whatever you can, and formulate questions about whatever you can’t agree on.  You have X minutes.  Go.”
  5. I circulate and listen to conversations, so I can prepare for the kinds of group discussion, direct instruction, or extension questions that might be helpful.
  6. When we’re 30 seconds from the end, or when the conversation starts to die down, I announce “30 more seconds to agree or come up with questions.”
  7. Then, I ask each group to report back.  Usually I collect all the questions first, so that Group B doesn’t feel silenced if their question is answered by Group A’s consensus. Occasionally I ask for a flashcard vote at this point; more often, collect answers from each group verbally. I write them on the board — roughly fulfilling the function of “showing the graph” of the clicker results.
  8. If the answers are consistent across the group and nothing needs to be clarified, I might move on to an extension question.  If something does need clarification, I might do some direct instruction.  Either way, I encourage students to engage with the whole group at this point.

Then we’re ready to move on — maybe with another round, maybe with an extension question (the cheat-sheet gives some good multi-purpose prompts, like “What question would make Alternate Answer correct?”).  I’m also a fan of “why would a reasonable person give Alternate Answer?”

Why I Like It

It doesn’t require a ton of preparation.  I usually plan the questions I’ll use (sometimes based on their pre-class reading which, in my world, actually in-class reading…).  But, anytime during class that I feel like throwing a question out to the group, I can do this off the cuff if I need to.

During the group discussion phase (Step 4), questions and ideas start flowing and scientific discourse flourishes.  Right in this moment, they’re dying to know what their neighbour got, and enjoy trying to convince each other.  I don’t think I buy the idea that these techniques help because students learn better from each other — frankly, they’re at least as likely to pseudoteach each other as I am.  I suspect that the benefit comes not so much from what they hear from others but from what they formulate for themselves.   I wish students felt comfortable calling that stuff out in a whole group discussion (with 17 of us in the room, it can be done), but they don’t.  So.  I go with what works.

No one outside the small group has to know who asked which questions.  The complete anonymity of clickers isn’t preserved, but that doesn’t seem to be a problem so far.

Notes For Improvement

There are some prompts on the cheat sheet that I could be using a lot more often — especially replacing “What questions do you have” or “What did you agree on” with “What did you group talk about,” or “If your group changed its mind, what did you discuss?”

There’s also a helpful “Things Not To Do (that seemed like a good idea at the time)” page that includes my favourite blooper — continuing to talk about the problem after I’ve posed the question.

If I was to add something to the “What Not To Do” list, it would be “Shifting/pacing while asking the question and immediately afterwards.”  I really need to practice holding still while giving students a task, and then continuing to hold still until they start the task.   My pacing distracts them and slows down how quickly they shift attention to their task; and if I start wandering the room immediately, it creates the impression that they don’t have to start working until I get near enough to see their paper.

Student Thinking About Abstracting

February 13, 2013 in Abstracting, Student thinking about thinking | 9 comments

My students have recently discovered the convention of describing silicon diodes as having a forward voltage of 0.7 V.  They know that this is not always true — or even usually true, in their experience.  The way they reconciled the difference made for an interesting conversation about abstraction — the verb, not the noun.

After some constructive class discussion about possible approaches, they decided to use the diode’s “turn-on voltage” in predictions.  That’s the smallest voltage at which measurable current will flow — for a rectifier diode using our meters, it’s about 400 mV.  It’s also the voltage that, subtracted from the supply, gives the highest estimate for voltage across the other components and therefore the highest estimate of current.  They thought a high current was the “worst-case scenario” in terms of protecting the diode from damage.  When it turned out in the lab that this made their percent differences unusually high, they were willing to sacrifice accuracy for safety.

So why do authoritative sources say that all silicon diodes have a forward voltage of 0.7 V?  Except for the ones that say it is definitely always 0.6 V?

The students shared their confusion and no small amount of anger.  The problem wasn’t with having chosen some constant value; they got that you had to pick a value to work with when making predictions.  The problem wasn’t the need to abstract information out of the picture; they discussed several reasonable approaches to that problem and chosen one based on their evidence and judgement. Their problem was with sources that never mentioned that a choice had been made at all.

They were irritated, considering this at best a “mistake” and at worst a “lie.”  As I often do, I asked the students “why would a reasonable textbook author do this?”  Here are their answers:

It could be a typo.

It could be a shortcut for the author’s convenience.

Maybe they learned it that was so they put it in their textbook that way.

Maybe the authors are so experienced that they forgot that they made an assumption.

When the students ran out of ideas, I contributed mine: that the author had done this deliberately to make things simpler for students.  They were stunned.  How could anyone think it would be easier to have a “fact” printed in the textbook that was clearly contradicted by their measurements?  How could anyone not realize that it made them doubt their skill, even their perception of reality?  They were describing feeling “gaslit.”

I confess that I was delighted.  It marks a shift in their thinking about science: away from judging reality according to how well it fits their predictions, toward judging predictions according to how well they model reality.  And yes, I called it the “second diode approximation,” and warned them that they would encounter the first and third approximations as well.

But mostly, I was sad about how consistently teaching materials do this. The fact that an abstraction has an official name is not a justification for introducing it first in a curriculum.  I am more and more sure that my students understand more when we start from complexity as we experience it, then move toward idealized concepts only if they help us get closer to a goal.

Brian Frank gives a bunch of examples and helpful exercises for (current or) aspiring teachers, including this quote:

The shortcuts, omissions, and ‘simplifications’, which are meant to reduce complexity are not conducive to understanding; they are specious, and they make genuine understanding extremely difficult. (Arons, “Teaching Introductory Physics”, pg. 24)

Will this always be true?  If not, how could I distinguish  contexts in which it would help to go the other way?  What else can I do to “inoculate” students against these approaches when they inevitably encounter them?

K-12 Engineering: Engineering is Elementary

February 5, 2013 in Engineering, K-12, Lesson plans | 1 comment

International Space Station, courtesy of Wikipedia

I’ve had mixed feelings about some engineering curricula designed for the under-12 set.  There are an awful lot of lesson plans available on-line that have big ideas (space exploration, zero-gravity adaptation) and big words (ecliptic, aphelion) but when you get right down to it, the students aren’t building space suits or improving solar panels; they’re measuring evaporation in a tin pan (I made this example up to protect the hard-working institutions that also sometimes turn out great materials).  Besides the feeling of “bait and switch,” this is also disappointing because it fails to help students or teachers make sense of what engineering is, and why it’s not the same as science.

So I was intrigued to find a link to Engineering is Elementary recommended by Mark Guzdial in response to the post “Teaching Engineering Thinking” at Gas Station Without Pumps.  The contexts are smaller (design an alarm circuit, design a bridge) but in those lessons, students are going to design and build and alarm circuit or a bridge.  They’re also going to assess their creations and improve them based on the assessment.  The language is simple and every lesson’s title start with “Designing a …”, except the ones that start with “Making a …” or “Improving a …”.  There’s a table-top mag-lev system in there.  I don’t know anything about these products — cost or effectiveness or ease of use.  But when some projects for elementary school students make me think “Oh, I want to do that one,” it makes me curious.

If you’ve used them, what are they like?  Could I use them with a Brownie troop (6-8 year olds)?  Could I use them for my adult students when we need something light as a break?  If you try them out, please let me know how it goes.

Analyzing Mistakes: An experiment

February 5, 2013 in Assessment, Grades are not the point, Growth mindset | 5 comments

I expect students to correct their quizzes and “write feedback to themselves” when they apply for reassessment.  The content that I get varies widely, and most of it is not very helpful, along the lines of

I used the wrong formula

I forgot that V = IR

It was a stupid mistake, I get it now.

I was inspired by Joss Ives’ post on quiz reflection assignments to get specific about what I was looking for.  This all stems from a conversation I had with Kelly O’Shea about two years ago, back when I had launched myself into standards-based/project/flipped/inquiry/Socratic/mindset/critical thinking/whatnot all at once and unprepared, that has been poking its sharp edges into my brain ever since:

Me: Sometimes I press them to be specific about what they learned or which careless mistake they need to guard against in the future. It’s clear that many find this humiliating, some kind of ingenious psychological punishment for having made a mistake. Admitting that they learned something means admitting they didn’t know it all along, and that embarrasses them. Does that mean they’re ashamed of learning?

Kelly: How often do you think they’ve practiced the skill of consciously figuring out what caused them to make a mistake? How often do we just say, “That’s okay, you’ll get it next time.” instead of helping them pick out what went wrong? My guess is that they might not even know how to do it.

Me: *stunned silence*

So this year I developed this.

Phases of Feedback

  1. Understand what you did well
  2. Diagnose why you had trouble
  3. Improve

Steps 1 and 3 can be used even for answers that were accepted as “correct.”

This has yielded lots of interesting insight, as well as some interesting pushback. Plus, it gave me an opportunity to help my students understand what exactly “generalize” mean.  In a future post I’ll try to gather up some examples. Overall, it’s helped me communicate what I expect, and has helped students develop more insight into their thinking as well as the physics involved.

Easy-Medium-Hard: One way to generate (and grade) differentiated problem sets quickly

February 5, 2013 in Assessment, confusion, Self-Assessment | Leave a comment

Sometimes I need to have all the students in my class improve their speed or accuracy in a particular technique.  Sometimes I just need everyone to do a few practice problems for an old topic so I can see where I should start.  But I don’t have time to make (or find) the questions, and I definitely don’t have time to go through them with a fine-toothed comb.

One approach I use is to have students individually generate and grade their own problems.  They turn in the whole, graded, thing and I write back with narrative feedback.  I get what I need (formative assessment data) and they get what they need — procedural practice, pointers from me, and some practice with self-assessment.

Note: this only works for problems that can be found in the back of a textbook, complete with answers in the appendix.

Here’s the handout I use.

What I Get Out of It

The most useful thing I get out of this is the “hard” question — the one they are unable to solve.  They are not asked to complete it: they are asked to articulate what makes that question difficult or confusing.

Important Principles

  • Students choose questions that are easy, medium, and hard for them.  This means they must learn to anticipate the difficulty level of a question before attempting it.
  • If they get a question wrong, they must either troubleshoot it or solve a different one.
  • They turn in their questions clearly marked right or wrong.

Advantages

  • I don’t have to grade it — just read it and make comments
  • The students get to practice looking at things they don’t fully understand and articulating a question about it
  • I get to find out what they know and what they (think they) don’t know.
  • Students can work together by sharing their strategies, but not by sharing their numbers, since everyone ends up choosing different problems.
  • It makes my expectations explicit about how they should do practice questions in general: with the book closed, page number and question number clearly marked, with the schematics copied onto the paper (“Even if there’s no schematic in the book?!” they ask incredulously — clearly the point of writing down the question is just to learn to be a good scribe, not to improve future search times), etc.

Lessons Learned

I give this assignment during class, or at least get it started during class, to reduce copying.  Once students have chosen and started their questions, they’re unlikely to want to change them.

Significance: a new axis in our critical thinking space

February 1, 2013 in Assessment, Causality, Critical Thinking, Student thinking about thinking | Leave a comment

My students use the same assessment rubric for practically every new source of information we encounter, whether it’s something they read in a book, data they collected, or information I present directly.  It asks them to summarize, relate to their experience, ask questions, explain what the author claims is the cause, and give support using existing ideas from the model.  The current version looks like this (click through to zoom or download):

Assessment for Learning

There are two goals:

  • to assess the author’s reasoning, and help us decide whether to accept their proposal
  • to assess one’s own understanding

If you can’t fill it in, you probably didn’t understand it.  Maybe you weren’t reading carefully, maybe it’s so poorly reasoned or written that it’s not actually understandable, or maybe you don’t have the background knowledge to digest it.  All of these conditions are important to flag, and this tool helps us do that.

The title says “Rubric for Assessing Reasoning,” but we just call them “feedbacks.”

Recently, there have been a spate of feedbacks turned in with the cause and/or the “support from the model” section left blank or filled with vague truisms (“this is supported by lots of ideas about atoms,” or “I’m looking forward to learning more about what causes this.”)

I knew the students could do better — all of them have written strong statements about cause in the past (in chains of cause and effect 2-5 steps long).  I also allow students to write a question about cause, instead of a statement, if they can’t tell what the cause is, or if they think the author hasn’t included it.

So today, after I presented my second draft of some information about RMS measurements, I showed some typical examples of causal statements and supporting ideas.  I asked students to rate them according to their significance to the question at hand, then had some small group discussions.  I was interested (and occasionally surprised) by their criteria for what makes a good statement of cause, and what makes a good supporting idea.  Here’s the handout I used to scaffold the discussions.

The students’ results:

A statement of cause should …

  • Be relevant to the question
  • Help us understand the question or the answer
  • Not leave questions unanswered
  • Give lots of info
  • Relate to the model
  • Explain what physically makes something happen or asks a question that would help you understand the physical cause
  • Help you distinguish between similar things (like the difference between Vpk, Vpp, Vrms)
  • Not beg the question (not state the same thing twice using different words)
  • Be concrete
  • Make the new ideas easier to accept
  • Use definitions

Well, I was looking for an excuse to talk about definitions — I think this is it!

Supporting ideas from the model should…

  • Help clarify how the electrons work
  • Help answer or clarify the question
  • Directly involve information to help relate ideas
  • Help us see what is going on
  • Give us reasoning so we can in turn have an explanation
  • Clarify misunderstandings
  • Allow you to generalize
  • Support the cause, specifically.
  • Be specific to the topic, not broad (like, “atoms are made of protons, electrons, and neutrons.”)
  • Not use a formula
  • It helps if you understand what’s going on, it makes it easier to find connections

The Last World

Which ones would you emphasize? What would you add?

Data Analysis: Productive talk overheard this week

January 30, 2013 in Causality, Classroom Life, Inquiry | 2 comments

Overheard while the students discussed the difference between I vs. V characteristics of light bulbs and diodes.

 

Facilitating the process:

What else do we know?

Are we going to analyze predictions and measurements?  Or just measurements?

So forward voltage is one category, reverse is another?

So, what have we concluded so far?

Do we have to write down our data?

I’m going to keep writing down the data.

So basically what you had was…

Were you maybe reading it like…

So what should we put here?

 

Seeking Causes:

But it wouldn’t be through the LED.  The voltmeter was shorting out the LED.

So they’re about the same, what’s the reason for that?

 

Holding our thinking to the model:

So this is actually supporting our idea…

One thing I noticed was that as voltage increased, current increased

I thought it always had all the voltage right there.

The current is supposed to go up, according to predictions.

 

Seeking patterns

Was VR1 always 0?

So forward voltage is one category, reverse is another?

Do you have the same figures for positive and negative voltage? [Reply] Well, let’s compare.

So they’re about the same, what’s the reason for that?

I think there’s something wrong there.

So we can’t compare these to each other.

What I did was use Ohm’s Law, that you have to do that for each point individually.

I think the resistance will decrease because…

Diodes are crazy!

It probably works like a switch.

The Payoff, Jan 2013

January 30, 2013 in Classroom Life, Student thinking about thinking | 5 comments

Dan Goldner inspired me to start keeping track of these moments of hope and change.

Jan 18, from struggling student:

“I’ve never thought about things so intently before.  You’re changing the way we think.  It’s really different from what I’m used to.  You really have to understand why you think what you do.  I talk to everyone about it — I’ve been talking to my parents about it.”

Jan 18, from philosophical student;

“Someone at work the other day said ‘I can’t believe I did something so stupid!’ and I said, ‘Don’t disrespect your past self.’ “

Jan 24, From usually-overwhelmed right-answer-seeking student:

“I was all excited, I thought ‘I’m going to be the first person to break Ohm’s Law!’  And then I checked, and Ohm’s Law works fine, but wouldn’t that be awesome?”

Jan 25, from struggling student:

“It’s so different from high school!  In high school it seemed like you always had to know something, you could  never say ‘I don’t know.’ “ME: “Oh.  Was it bad if you didn’t know something?”

Stoic, Silent Student joins in: “Yeah!  That was not OK.”

ME: “I never take that into account enough.  The way I see it, of course we want to talk about what you don’t know.  What would be the point of talking about the things you already know?”

SS: “I think it’s getting better.  People are getting more comfortable just throwing things out there.”

Jan 28:

“Practice makes better!” (Me and student, simultaneously)

Jan 30 (click through for photo):

An offering

The way to a teacher’s heart is through safely, strategically destroyed components

 

Breakthrough: Questioning, scientific discourse, community

January 30, 2013 in Causality, Classroom Life, Things I've improved | 4 comments

Previously, in data analysis sessions since September:

Students were having trouble drawing any conclusions, noticing any patterns, or thinking about cause at all when they broke into groups to analyze data the class had generated.  It was never enough time, had always been too long since the measurements were taken, and they had too little background knowledge.  They floundered and fussed, getting increasingly annoyed and disoriented, while I tried to make them think by sheer force of will, running around steering them away from insignificant details (like, “all the voltages are even numbers”).

Lesson #1: Procedural Fluency

This semester started off the same.  I asked them to characterise the I vs. V response of a lightbulb and of an LED, to look for similarities and differences.  As I wrote previously, most of them were up to their gills just trying to wrestle their measurement equipment into submission.  They finally complete their measurements, but without any awareness of what was going on, what that graph mean, etc.

At my wits’ end, I had them do it again with two other models of diode.  To me, this felt almost punitive — like handing someone an identical worksheet and telling them to start over.  To try to make it a bit more palatable, I seized on their frustration about how “Mylène’s labs are so LONG” and told them we weren’t going to cover anything new — we were just going to do some speed practice, so I could show them some techniques for increasing speed without sacrificing accuracy.

I helped them strategize about how to set up a table for measurements (they were writing their measurements out in paragraphs… yikes).  I also got much more directive than usual, and informed them that everyone was required to used two meters simultaneously (many were using a single meter to switch back and forth between measuring voltage and current… with attendant need to unhook the circuit TWICE for every data point!!).  There was big buy-in for this, as they immediately saw that they were going to get an entire data set in a single class.  I saved a few minutes at the end of class for students to share their own time-saving ideas with their classmates.

What I didn’t realize was that they had internalized so little information about diodes that blue LEDs seemed like a whole different project than red LEDs.  I was worried they would mutiny about being forced to redo something they’d already finished, but I was wrong.  They welcomed, with relief, the opportunity to do something that was recognizable, with a format and a set of instructions that they had already worked the kinks out of.  Moral of the story: it’s the background knowledge, stupid.  (I can hear Jason Buell‘s voice in my head all the time now).

Lesson #2: Distributed Practice

I also realized that asking this group to sit down with some data and analyze the patterns in an hour is not going to happen.  I figured it mostly about having enough time (and not feeling pressured) so I started requiring them to keep track of “what did you notice?  what did you wonder?” while they were measuring.  After they were done measuring, I also required them to write some notes to themselves: explanations of anything in the lab that supported the model, and questions about anything that wasn’t supported by the model or that seemed weird (“When you find some funny, measure the amount of funny.” [Bob Pease of National Semiconductor, probably apocryphal]).

That meant they could take their time, tease out their thoughts, and write down whatever they noticed.  When it was time to sit down in data analysis session, they had already spent some time thinking about what was significant in their measurements.  They had also documented it.

Lesson #3: Expect them to represent their own data

In the past, I’ve made a full record of the class’s data and given a copy to every students.  My intention was that they would come through the evidence in a small group — maybe splitting up the topics (“you look at all the red LEDs — do they all turn on at 1.7?  I’ll check the blue ones”) — and everyone would be able to engage with the conversation, no matter whose data we were discussing.  My other intention was that they would take better notes if they knew other students would read them.  It worked last year … but this year I got extremely tidy notes, written out painstakingly slowly so the writing was legible… with measurements buried in paragraphs.

Last week, I asked everyone to get into small groups with people who were not their lab partner.  They were not required to analyze the whole class’s data — only the data of the people in the small group, who would be expected to explain it to the others.

The students loved it because they were analyzing 4 data sets, not 9.  So they were happy.  I was happy too, because, from out of nowhere, the room exploded in a fury of scientific discourse.  “Oh?  I got a different number.  How did you measure it?”  “Does everybody have…?” “Will it always be…?” “Why wouldn’t it…?” “That’s what we’d expect from the model, because…“

I was floored.  Since I didn’t have to run around putting out fires, I found my brain magically tuned in to their conversations — I filled an entire 8.5×11 sheet full of skillful argumentation and evidence-based reasoning that I overheard.  Honestly, I didn’t hear a single teleological, unscientific, or stubbornly antagonistic comment.    Most days I can’t do this at all — I’m too overwhelmed to hear anything but a buzzing cacophony, and they’re too tense to keep talking when I get close.They didn’t even stop talking when I wandered near their desks — they were all getting their foot in the door, making sure their data made the final cut.

It slowed down a bit when I reminded them that they had to have at least one possible physical cause for anything they proposed (i.e. “the materials and design of the diode cause it to not conduct backwards” is not a cause).  But they picked it back up, with awesome ideas like

  • Maybe the diode acts like a capacitor — it stores up a certain amount of energy
  • Maybe the diode only takes whatever energy it needs to light up, and then it doesn’t take any more
  • Maybe the lightbulb’s resistance went up because it’s a very narrow filament, but it has low resistance.  So when all the current rushes in, there’s no room for more electrons, and that restricts current.
  • Maybe a diode has a break inside, and it takes a certain amount of voltage to push the electrons through the gap.  It’s like shooting electrons out of a cannon — they need a certain force to make it over a ravine.
  • How come electrons in a silicon crystal “bond” and make a pair?  I thought they orbit around the nucleus because electrons repel each other.
  • If a leaving electron creates a positive ion, wouldn’t that attract the same electron that left?

These are not canonical, of course.  But they’re causes!  And questions!   And they have electrons!!  I was so excited.  The students were having fun too — I can tell because when they’re having fun, they like to make fun of me (repeating my stock phrases, pretending to draw from a deck of cards to cold call someone in the audience, etc etc.)

Moral of the story

1. During measurement, you must write down what you noticed, what you wondered/didn’t know.

2. After measurement, you must write down which parts of this the model can explain (students call this “comparing the data to the model.”)  This causes students to actually pull out the model and read it.  Awesome.

3. Anything that can’t be explained by the model?  Articulate a question about it.

4.  If that’s still not working well, and I’m still getting into a battle of wills with students who say that the model doesn’t explain anything about diodes, do the same lab again.  Call it speed practice.

Then, when we share data and propose new ideas to the model, they’ve already spent some time thinking about what’s weird (no reverse current in a diode), what’s predicted surprisingly well by the model (forward current in a diode) and what’s predicted surprisingly badly (current in a lightbulb).  When we sit down to analyze the data, they’re generating those ideas for the second or third time, not the first.

5. Stop making copies of everyone’s data — it allows one strong and/or bossy student to do all the analyzing.  Require that the whiteboards include an example from every person’s data.

6. Watch while they jump in to contribute their own data, compare results and ideas about “why,” facilitate each others’ participation, summarize each others’ contributions, challenge, discuss, and pick apart their data according to the model.

7.  Realize that since I’m less overwhelmed with needing to force them to contribute constructively, I too have much more cognitive capacity left over for listening to the extremely interesting conversations.

PD Opportunity: Research-based instruction in engineering science

January 27, 2013 in Engineering, Professional Development | Leave a comment

I just received a notice from the American Society for Engineering Education about a free online PD project for faculty who teach introductory engineering science.  It’s called  Advancing Engineering Education Through Virtual Communities of Practice, and they’ve just extended the application deadline to Feb. 8. Participants can choose from these topics:

  • Electric circuits
  • Mechanics
  • Thermodynamics
  • Mass & energy balance

I can’t tell if you have to be a member of an engineering department, or if it’s enough to teach one of these topics; I can’t even tell if you have to be American.  In any case, I applied.  From what I can tell, accepted applicants participate in once-weekly online meetings with facilitators who have experience with “research-based instructional approaches” (though they don’t tell you which ones, except for references to “Outcome-Based Education” — which I think of as an assessment approach, not exactly an instructional approach).

I suppose I should be concerned about the lack of details on the website (even the application deadline on the front page hasn’t been changed to reflect the extension), but I’m chalking it up to this being the prototype run, and anyway, the price is right.  The informed consent form makes it clear that this is a research project to explore the viability of the model, which is fine by me.  It’ll be worth it if it leads to any of these things:

  1. Working on instructional changes in a systematic way (rather than the somewhat haphazard and occasionally accidental way I’ve been doing it so far)
  2. Focusing on the specific ways particular instructional approaches play out in circuits courses, not to mention deepening my content knowledge
  3. Having a consistent group to work with over the course of 6 months (and two different academic years).

It seems to bring together the advantages of something like the Global Physics Department, with the bonus that every meeting will be about exactly what I teach, and the meeting time will be a part of my scheduled workday.

The email I received from the ASEE contains details that are not available on the website, so I’m including it below.

NSF-funded project to develop engineering faculty virtual communities of practice

Engineering education research has shown that many research-based instructional approaches improve student learning but these have not diffused widely because faculty members find it difficult to acquire the required knowledge and skills by themselves and then sustain the on-going implementation efforts without continued encouragement and support.
ASEE with a grant from NSF is organizing several web-based faculty communities that will work to develop the group’s understanding of research-based instructional approaches and then support individual members as they implement self-selected new approaches in their classes.  Participants should be open to this new technology-based approach and see themselves as innovators in a new approach to professional development and continuous improvement.

The material below and the project website provide more information about these communities and the application process. Questions should be addressed to Rocio Chavela at r.chavela@asee.org.

If you are interested in learning about effective teaching approaches and working with experienced mentors and collaborating colleagues as you begin using these in your classroom, you are encouraged to apply to this program. If you know of others that may be interested, please share this message with them.

Please consider applying for this program and encouraging potentially interested colleagues to apply. Applications are due by February 8, 2013.

Additional Details About the Program

Format

Faculty groups, which will effectively become virtual communities of practice (VCP) with 20 to 30 members, will meet weekly at a scheduled time using virtual meeting software during the second half of the Spring 2013 Semester and during the entire Fall 2013 Semester. Each group will be led by two individuals that have implemented research-based approaches for improving student learning, have acquired a reputation for innovation and leadership in their course area, and have completed a series of training sessions to prepare them to lead the virtual communities. Since participants will be expected to begin utilizing some of the new approaches with the help and encouragement of the virtual group, they should be committed to teaching a course in the targeted area during the Fall 2013 Semester.

 VCP Topics and Meeting Times

This year’s efforts are focusing on the introductory engineering science courses and the list below shows the course areas along with the co-leaders and the scheduled times for each virtual community:

Electric Circuits
Co-leaders are Lisa Huettel and Kenneth Connor
Meeting time is Thursday at 1:30 – 3:00 p.m. EST starting March 21, 2013 and running until May 16, 2013

Engineering Mechanics
Co-leaders are Brian Self and Edward Berger
Meeting time is Thursday at 1:30 – 3:00 p.m. EST starting April 3, 2013 and running until May 16, 2013

Thermodynamics
Co-leaders are John Chen and Milo Koretsky
Meeting time is Wednesday at 2:00 – 3:30 p.m. EST starting April 3, 2013 and running until May 23, 2013

Mass and Energy Balance
Co-leaders are Lisa Bullard and Richard Zollars
Meeting time is Thursday at 12:30 – 2:00 p.m. EST starting March 21, 2013 and running until May 16, 2013

Application Process

Interested individuals should complete the on-line application at https://www.research.net/s/asee-vcp_application_form. The application form asks individuals to describe their experience with introductory engineering science courses, to indicate their involvement in education research and development activities, to summarize any classroom experiences where they have tried something different in their classes, and to discuss their reasons for wanting to participate in the VCP.

The applicant’s Department Head or Dean needs to complete an on-line recommendation form to indicate plans for having the applicant teach the selected courses in the Fall 2013 Semester and to briefly discuss why participating in the VCP will be important to the applicant.

Since demonstrating that the VCP approach will benefit relatively inexperienced faculty, applicants do not need a substantial record of involvement in education research and development. For this reason, the applicant’s and the Department Head’s or Dean’s statements about the reasons for participating will be particularly important in selecting participants.

Application Deadline

Applications are due by February 8, 2013. The project team will review all applications and select a set of participants that are diverse in their experience, institutional setting, gender, and ethnicity.

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