This research team, let by Jeffry Schinske, wondered if students avoided science classes because they simply couldn’t see themselves as scientists.
“I am this kind of person,” students might think. “Scientists are that kind of person. I’ll just never belong.”
To push back against this false belief, Schinske’s team tried a straightforward strategy. Their biology students learned not only from a textbook, but also from primary sources. By learning course information from a broadly diverse range of scientists, these students expanded their sense of who scientists might be.
That is: they might learn about neurobiology by studying the work of Dr. Ben Barres. In this way, students learned about diseases of the nervous system and about trans scientists. (If you’re interested in Barres’s remarkable story, we introduced him on this blog a few years ago.)
They didn’t learn about biology concepts as a series of abstract truths. Instead, they learned about these topics through the people (Black or White or Asian or Hispanic; gay or straight; cis or trans; on the spectrum; funny or serious) who investigate them.
In other words: Schinske’s team wanted to increase their students’ sense of possible selves by showing scientists who resembled them.
Results?
Sure enough, this strategy worked. A few key findings.
Compared to students in an active control condition, students who did this “Scientist Spotlight” homework…
… thought of scientists in less stereotypical ways,
… felt they could individually relate to scientists as people like themselves (and felt that way for at least 6 months),
… felt more interested in science, and
… got higher grades.
Because of the study design, not all these findings are causal. That is, Shinske doesn’t claim that the Scientist Spotlight caused the higher grades.
But, it’s an intriguing possibility — especially because it doesn’t take additional time for either students or teachers.
In Their Own Words
More than most research, this study includes passages from surveys that the students completed. The students’ own words helpfully communicate the power of this technique. For instance,
For my whole life I … wasn’t exposed to any scientist who was of African American descent. That, as a fellow African American, brought me joy as it shows that African Americans are no longer abiding to the negative stigma we have. She’s representing a powerful positing for us and people have noticed her work. It gave me incentive to push for my own dreams and to succeed.
Or
I found this Ted Talk with Charles Limb incredibly interesting mostly because I am a musician myself who has been trained both classically and in jazz.
Or
Before I learned about scientists in this class, I thought scientists were like “nerds” or what they show in movies. The characters would be very geeky, had glasses, spoke monotone, and thought they were above everyone. However, through all the research I’ve done in this class, scientists are just normal people like myself. They love to learn new things, they have a life outside the laboratory, they are fun … My opinion of people who do science has completely changed thanks to this class.
Clearly, this strategy strongly influenced these (and many other) students.
If you try this out with your own scientists, please let me know what you find!
When we see alarming statistics about gender disparities in STEM disciplines, we quite naturally wonder how to fix this imbalance.
(This hope – by the way – isn’t simply a do-goody desire to sing “It’s a Small World After All.” If we believe that men and women can contribute equally to a scientific understanding of our world, then every girl discouraged is a contribution lost.
In other words: we ALL benefit if boys and girls contribute to science.)
So, how can we encourage girls to participate in science?
To answer this question, we might first answer a related question: what discourages girls in the first place.
If we can undo the discouragement, we are – indirectly but effectively – encouraging.
So, what discourages girls?
Is Science Education Itself the Problem?
Here’s a disturbing possibility.
When students learn about genetics, and specifically about the genetics of sex differences, they might infer that genders have a fixed, absolute quality. All boys (and no girls) are this way; all girls (and no boys) are that way.
It’s in the genes, see?
This set of beliefs, in turn, might reinforce a fixed mindset about gender and ability.
Through this causal chain, a particular science curriculum might itself discourage girls from pursuing science.
Yikes!
Researcher Brian Donovan and his team explored this question in a recent study. To do so, they asked students to read different lessons about genes and sexual dimorphism.
Some 8th – 10th graders learned about the genetics of human sexual difference.
Others learned about the genetics of plant sexual differences.
Others read a curriculum that explicitly contradicted the notion that genetic sex differences directly cause differences in intelligence and academic ability.
Did these curricular differences have an effect?
The Results Envelope Please
Unsurprisingly, students who learned that we can’t draw a straight line from genes to gender roles and abilities believed that lesson.
To make the same point in reverse: students who studied a seemingly “neutral” scientific curriculum – “we’re just talking about genes here” – drew unsupported conclusions about absolute differences between men and women.
Amazingly, this finding held true both for the students who studied the genetics of human sexual differences AND those who studied plant sexual differences.
WOW.
Perhaps surprisingly, students who learned that genetic sex differences don’t cause gendered ability differences also expressed a greater interest in science.
In particular, the girls who studied the “genetics only” lesson expressed meaningfully less interest in a science major than those who got the alternative lesson. (The two lessons neither encouraged nor discouraged the boys.)
But, Why?
Here’s the likely causal chain:
A science curriculum that focused “purely” on genetics seemed to suggest that men and women are utterly different beings.
Students who read this “pure” lesson inferred that some human abilities – like, say, scientific competence – might differ between genders.
This inference, in turn, made gender stereotypes (e.g., “men do better at science than women”) more plausible.
And so, the women who got that seemingly neutral science lesson, discouraged by the stereotype it reinforced, felt less inclined to pursue science.
By this roundabout route, a traditional science lesson might itself discourage students from learning science.
Alternative Explanations
Of course, the topic of gender differences – especially in the realms of math and science – can generate lots of energetic debate.
When I asked Donovan for alternative explanations for his findings, he was quick to emphasize that we need lots more research in this field. His is the first study done on this specific question. As always, teachers shouldn’t assume that any one study has found THE answer.
Some people do in fact argue that math and science ability (or interest) differ by gender because of genes. (Dr. Donovan explicitly rejects an explanation that moves directly from genes to gender differences.)
Here’s a recent book review by Lise Eliot, emphasizing that gender differences in brain regions
a) are often exaggerated and mis-reported, and
b) result from societies that emphasize gender differences.
Donovan’s research suggests that teachers can and should do more to be sure we’re not discouraging some students from particular academic interests and career paths.
For one set of practical suggestions, this interview with Sapna Cheryan outlines several ways we can promote “ambient belonging” in our classrooms.
Exam stress bothers many of our students. Sadly, it hinders students from lower socio-economic status (SES) families even more.
As a result, these students struggle — especially in STEM classes. And, this struggle makes it harder for them to enter these important (and lucrative!) fields.
This research stands out for a number of reasons: in particular, it included a large sample (almost 1200 students). And, it took place in a school, not a psychology lab. That is, his results apply to the “real world,” not just a hermetically sealed research space.
Rozek worked with students taking a 9th grade biology class. Before they took the two exams in the course, Rozek had students write for ten minutes.
One group spent their ten minutes writing about their current thoughts and feelings. This approach lets students “dump” their anxiety, and has been effective in earlier studies. (By the way: this earlier research is controversial. I’ve written about that controversy here.)
Another group read a brief article showing that the right amount of stress can enhance performance. This reading, and the writing they did about it, helps students “reappraise” the stress they feel.
A third group did shortened versions of both “dumping” and “reappraising” exercises.
And the control group read and wrote about the importance of ignoring and suppressing negative/stressful emotions.
So, did the “dump” strategy or the “reappraise” strategy help?
Dramatic Results
Indeed, they both did.
For example, Rozek and Co. measured the effect these strategies (alone or together) had on the exam-score gap between high- and low-SES students.
The result? They cut the gap by 29%.
Rozek also tracked course failure. Among low-SES students, these strategies cut the failure rate by 50%.
(In the control group, 36% of the low SES students failed the class; in the other three groups, that rate fell to 18%. Of course, 18% is high — but it’s dramatically lower than 36%.)
In his final measure, Rozek found that — after these interventions — low SES-students evaluated their stress much more like the high SES-students. The gap between these ratings fell…by 81%.
All this progress from a 10 minute writing exercise.
Classroom Guidance to Reduce Exam Stress
If you’ve got students who are likely to feel higher levels of anxiety before a test, you might adapt either (or both) of these strategies for your students.
The best way to make these strategies work will vary depending on your students’ age and academic experience.
You might start by reviewing Rozek’s research — click the link above, and look for the “Procedure” section on page 5. From there, use your teacherly wisdom to make those procedures fit your students, your classroom, and you.
To answer this question, we might rely on our teacherly instincts. Perhaps we might rely on various educational and scientific theories. Or, we might turn to data. Even big data.
Researchers in Sweden wondered if they could use the TIMSS test to answer this question.
(“TIMSS” stands for “Trends in International Mathematics and Science Study,” given every four years. In 2015, 57 countries participated, and 580,000 students. That’s A LOT of students, and a lot of data.)
3 Math Teaching Strategies
When students take these tests, they answer questions about their classroom experience.
In particular, they answer questions about 3 math teaching strategies. They are asked how often they…
Listen to the teacher give a lecture-style presentation.
Relate what they are learning in mathematics to they daily lives.
Memorize formulas and procedures.
Researchers want to know: do any of these teaching practices correlate with higher or lower TIMSS scores? In other words, can all these data help us evaluate the effectiveness of specific teaching practices?
2 Math Teaching Theories
Helpfully, the researchers outline theories why each of these practices might be good or bad.
As they summarize recent decades of math-teaching debate, they explain that “researchers with their roots in psychology and cognitive science” champion
formal mathematical notions,
explicit instruction where teachers show students how to solve math problems,
practicing and memorizing rules and worked examples.
On the other hand, “researchers with their roots in the reform movement” champion
connecting math to students’ daily lives,
a problem-solving approach,
understanding ideas and connections, rather than memorization.
Doubtless you’ve heard many heated debates championing both positions.
Predictions and Outcomes
These theories lead to clear predictions about TIMSS questions.
A cognitive science perspective predicts that “lecture-style presentations” and “memorizing formulas” should lead to higher TIMSS scores.
A reform-movement perspective predicts that “relating math to daily life” should lead to higher scores.
What did the data analysis show?
In fact, the cognitive science predictions came true, and the reform predictions did not.
In other words: students who listened to presentations of math information, and who memorized formulas did better on the test.
Likewise, students who applied math learning to daily life learned less.
An Essential Caveat
As these researchers repeatedly caution, their data show CORRELATION not causation.
It’s possible, for instance, that teachers whose students struggle with math resort to “daily life” strategies. Or that both variables are caused by a third.
Potential Explanations
“Connecting new math learning to real life situations” seems like such a plausible suggestion. Why doesn’t it help students learn?
These researchers offer two suggestions.
First, every math teaching strategy takes time. If direct instruction is highly effective, then anything that subtracts time from it will be less effective. In other words: perhaps this strategy isn’t harmful; it’s just less effective than the others.
Second, perhaps thinking about real-life examples limits transfer. If I use a formula to calculate the area of a table, I might initially think of it as a formula about tables. This fixed notion might make it harder for me to transfer my new knowledge to — say — rugby fields or floor plans.
At present, we can’t know for sure.
A final point. Although this research suggests that direct instruction helps students learn math, we should remember that bad direct instruction is still bad.
Lectures can be helpful, or they can be deadly tedious.
Students can memorize pertinent and useful information. Or, they can memorize absurd loads of information.
(A student recently told me she’d been required to memorize information about 60 chemical elements. Every science teacher I’ve spoken with since has told me that’s ridiculous.)
And so: if this research persuades to you adopt a direct-instruction approach, don’t stop there. We need to pick the right pedagogical strategy. And, we need to execute it well.
As schools focus more on STEM disciplines, teachers strive to help our students master complex STEM concepts.
After all, it’s hard enough to say “magnetic anisotrophy,” much less understand what it is.
Researchers Dane DeSutter and Mike Stieff have several suggestions for teachers. Specifically, they argue that spatial thinking–essential to many STEM concepts–can be enhanced by appropriate gestures.
This article is the first in an occasional series where I’ll introduce people who will be speaking at an upcoming Learning and the Brain conference.
Dr. Sapna Cheryan, Associate Professor of Psychology at the University of Washington, has been researching the ways that physical classroom environments reduce or increase students’ academic interests.
Specifically, she looks at decorations in Computer Science classrooms, and asks if they create a stereotyped atmosphere that might discourage students from taking those classes.
I spoke with Dr. Cheryan by phone to learn more about her research. (This interview has been lightly edited for clarity and condensed for brevity.)
Andrew Watson:
Dr. Cheryan, your research focuses on stereotypes and the power that they have to shape the decisions that we make—for instance, the major we might choose in school. How did you first get interested in that topic?
Sapna Cheryan:
I grew up a second generation Indian American in the Midwest, and I think that caused me to be interested in race, stereotypes, and diversity. On top of that, I went to a STEM focused high school, so gender was relevant there—for example, noticing who was taking what classes.
I just enjoyed thinking about diversity and how broader societal forces shape attitudes and behaviors.
I got interested in studying gender disparities in Computer Science, which is my main focus, because of an experience I had as a graduate student.
It was the early 2000’s. I was in Silicon Valley because I was a student at Stanford. I had just come from two years at a consulting job on the East Coast, so I had the business background and I thought, “Okay, I’ll get a summer internship.”
I remember going to one of the interviews and it was a really good job at a good company. I got an offer, but I ended up leaving that interview feeling like, “Oh, I don’t really think I would really fit in here with these people. I don’t think I really belong here.” And I drew that conclusion based on the way that the company looked: the physical space.
I remember seeing conference rooms that were named after Star Trek ships, and I drew the conclusion that I probably just won’t fit in with people who work at this company.
Then I went to a different interview. I interviewed at Adobe and Adobe has a much different environment. It was really open and colorful, and there was a cafeteria and a gym.
I only met men in both interviews. I would have had a male boss in both cases. But, for me, I really thought the type of people who work [at Adobe]—men and women—are probably going to be people that I fit in with better. I thought I’d have a better summer.
I ended up choosing Adobe, even though they were offering me a lower salary. But that was less important to me than the social fit (though I did end up negotiating my salary because I knew the research on how women get paid less).
Having just spent a year being trained as a graduate student of Social Psychology, I remember thinking: “I wonder if this is something more broad. If there’s something about this geeky stereotype that might be a deterrent to more women than men.” Perhaps that could help explain gender disparities in the field.
Andrew Watson:
Of course, both stereotypes and decision-making processes operate very subtly. Can you explain how to research something that happens so quietly and subtly deep in the psyche?
Sapna Cheryan:
Stereotypes sometimes operate subtly, and sometimes they’re not so subtle. If you ask people to list stereotypes, especially with something like Computer Science—where there’s no real fear of being seen as racist or sexist—people are pretty willing to tell you what their stereotypes of certain majors are.
We just simply ask people, “Describe computer scientists.”
Even when we’re asking for an accurate description, undergrads will still give us highly stereotypical phrases. They’ll say things like, “Don’t take showers.” “Socially awkward.” “Interested in Sci Fi.” They have all these stereotypes and they’ll tell them to you even if you don’t use the word “stereotypes.”
In our studies, we set up an environment where the stereotype is salient in some way, but participants don’t know that we’re studying that stereotype.
For example, the very first study that I did with gender disparities was trying to replicate my own experience of going to those different [summer internship] interviews.
We took over a small classroom at the Computer Science building in Stanford. We decorated it to look like the first company that I interviewed at. We put up Star Trek posters. We had soda cans that were stacked in a pyramid. We had science fiction books and stray electronic parts lying around.
Students would either come into a room that looked like that, or into the same room but redecorated. Instead of Star Wars and Star Trek, we had nature posters and art posters and water bottles instead of soda cans. General interest books and magazines.
We told the students, “We’re sharing this room with another group. That’s why there’s all this stuff in the room. You can just ignore it. Please sit here at this table and fill out this questionnaire.” We told them we were doing a study on career interests. They thought they were just filling out a career interest inventory.
We asked them how interested they were in majoring in Computer Science as part of that questionnaire. What we found is that the stereotypical room, the one with the Star Trek stuff, caused women to express lower interest in Computer Science than the women who were in the non-stereotypical room.
For men, we didn’t see a difference in that study.
That was one way that we can get at the effect of the stereotypes without the students knowing that we’re studying those specific stereotypes.
In describing the effect that stereotypical objects have on people, you’ve used the phrase “ambient belonging.” Can you talk a little bit more about what that means?
Sapna Cheryan:
We came up with that phrase to describe the sense that people get when they walk into an environment, look around and get a feel for whether they fit with the environment and with the people that they imagine to be in that environment.
We investigated it with Computer Science, but we think that the term can be thought of more broadly.
The example I like to give is when you visit a new city for the first time and you’re driving or walking around. I think a lot of people can get a sense of whether they fit with that city—even without talking to the people, or looking up facts about the city.
They’re doing it by looking at the stores and the restaurants and the cars, and getting a sense of whether this city feels like somewhere they would want to live or whether they feel similar to the people in that city.
It’s the idea that you can walk into a space and get an immediate impression of whether you would fit there and with the people you imagine would be there.
Andrew Watson:
Got it. A lot of your writing is actually quite specific and practical about ways to improve ambient belonging for groups who are typically left out. Can you give a suggestion or two?
Sapna Cheryan:
Our focus has really been on changing the stereotypes and broadening the image of Computer Science so that students don’t think they have to fit this narrow, geeky image to be successful in the field. We targeted three ways to change this image.
One is by changing environments, as we talked about. Companies and departments [should] look around and see what their spaces are communicating. If you are trying to recruit a broader group of people, you should make sure that the physical environment actually fits with the image you want to be communicating.
We’ve also talked about the media. One of the most common ways students learn about different careers, sadly, is from the media. They get more exposure to what a scientist is because of TV shows than they do from any other source. Media depictions of computer scientists and engineers include Big Bang Theory and Revenge of the Nerds. The archetypal Computer Science image has not changed much since the 1980’s.
I’d like to get a new image out there, to give Computer Science a TV show like what ER did for medicine or LA Law did for law. Those two shows actually helped diversify the fields because they presented an image that was diverse: women and men, and people of all races. That helped make the image of those fields more welcoming to more, different kinds of people.
The third [way to change stereotypes] is role modeling. That can mean portraying yourself as more than just the stereotypes. Not only talking about the ways you fit the stereotype, but also talking about your other interests.
Andrew Watson:
Certainly, the first and third of your suggestions are the sorts of things that schools can do to promote social change.
Sapna Cheryan:
Schools do have a lot of power to influence the way their students think about majors. I think mandatory Computer Science classes could help. But, we also have to be very careful how that Computer Science is taught.
I have a personal example. My high school had mandatory Computer Science. I went to a small high school. It was 53 students. In our class of 53, one girl ended up going into Computer Science. Even though we were all very well trained for a field like Computer Science and we were graduating at a time when there was a huge need [in the tech field].
40% of our boys ended up being Computer Scientists. Our class was taught in a way that really reinforced that Computer Science was something that the boys were good at. We went in with stereotypes that the role-playing-game boys were going to do well in the class.
My suspicion now is that the girls probably got higher grades than the boys. The boys were talking a lot in class, using all these buzz words that we had never heard of. We had this impression that they were really, really smart.
Now that I know the research, my guess is that we were probably doing just as well or better in the class than the boys. But nobody ever told us. Nobody ever said, “You’re getting an A. You should consider pursing this.” The class just reinforced our stereotypes going in.
Andrew Watson:
Are there misunderstandings about your research that you’d like to correct?
Sapna Cheryan:
That’s a good question.
First, when I listed three ways to change stereotypes, I want to be clear that you can’t just change one thing. You can’t just go change the posters in your lobby or just add one TV show. If it was that simple, then gender disparities would be decreasing, and they’re not. They’re stagnant in Computer Science. It’s going to require a sort of cultural revolution: some big social movement in Computer Science and other fields, like Engineering and Physics, to really make this change.
Second, I think a bigger misunderstanding is that talking about stereotypes promotes them. Sometimes people say, “Just by researching these stereotypes you are making them real. Students now think that this is how Computer Science is. We should just not talk about it.”
I think that’s a misconception, because students already have these stereotypes in their head. I’m not putting them there. If we don’t talk about it, then there’s nothing to interrupt that or get students to question whether they should have those stereotypes or not.
The [third misunderstanding] is when I talk about “men and women,” I never mean all men and all women.
Every time I give a talk, I mention that it’s not all men like this stereotype and all women don’t. We always get a core group of women in our studies who choose the stereotypical room over the non-stereotypical room.
The first time I gave a talk at Google, first question I got was from a woman who said, “I love Star Trek. It’s why I’m a programmer. Star Trek got me into this field.” It’s a reminder that when I talk about disparities, I’m talking about in aggregate and not talking about any individual.
I’m not trying to kick the geeks out of Computer Science, or say that we should completely change the image to a feminine one. What I’m trying to do is broaden the umbrella. You can identify as a geek, or somebody who likes Star Trek, and get into the field.
But, that shouldn’t be the only type of person who thinks that they can be in the field. We should also welcome people who didn’t start coding when they were four years old. We have to have a broader umbrella.
I think again, medicine is a good example. When I say, “Imagine a doctor,” you might have an image of a doctor and maybe it’s a male doctor. Then I say, “Think of another doctor.” It’s not too hard to think of a doctor who looks different from that first doctor.
But with Computer Scientists, you think of one, and a second one, you think of a third one, they all seem similar to each other. That’s what I want to change. I want to make it so that we think of different types of successful people in the field.
[Editor’s note: Here‘s a TedX talk where Dr. Cheryan elaborates on this point.]
Andrew Watson:
On the blog, we emphasize that in the sciences, whenever people publish new or important ideas, there is always disagreement and push-back. In fact, the criticism and the tussle really improve our understanding of those ideas.
So, my next question is: because you’ve published new and important ideas, I’m sure that people are pushing back. What do you think are the most valid critiques of the work you’re doing? Where do you think skepticism is most justified?
Sapna Cheryan:
Sometimes people say to me, “classrooms don’t really look like this. You don’t see geeky, stereotypical classrooms.” I think that’s fair; a lot of classrooms don’t look like this.
(Although, people have come to me and told me stories about classrooms that do look that: maybe not as extreme, but they do have elements of the stereotypical things.)
I am trying to show that these stereotypes matter. It would be great to go through and catalog what real classrooms look like. How extreme are they on these variables, and how much [stereotypical] stuff do you need in the classroom to have people start drawing conclusions? Can you just have one poster or a few action figures? Is that enough for people to draw conclusions? If not, does that mean we should only focus on the small percentage of classrooms that have an extreme version?
Andrew Watson:
A non-science question for you. Some of the people at the conference haven’t been to Boston before. Are there places you’d recommend?
Sapna Cheryan:
I live really far from Boston, but my best friend lives in Boston, so I visit her on occasion. My favorite place to go is called Sofra. It’s this cool Middle Eastern restaurant and the food is really, really good. I think it might be in Cambridge. We try to go there every time I visit.
Andrew Watson:
I’ll pass that along. Thanks so much for taking this time.
Should 9th graders start music classes–even if they’ve never played an instrument before? Are there academic benefits to studying music? Is 9th grade too late a start to get those benefits? Should my school’s STEM program become a STEAM program?
A recent study by Adam T. Tierney offers some answers to these compelling questions.
The Short Version
Tierney & Co. followed 19 high school students who enrolled in a high school music ensemble, and compared them to 21 students at the same school who started a JROTC program.
These groups started off nicely matched in various academic and linguistic measures. However, at the end of 4 years, the group that had studied music improved in some suggestive ways.
First, the neural signatures of their response to speech changed meaningfully; oversimplifying a bit here, they were “more mature.”
Second, the musicians improved more than the JROTC participants in their ability to distinguish between and manipulate language sounds.
Reasons to be Excited
Tierney’s study gives us several reasons to perk right up.
For example: we’ve known for a long time that life-long musicians have these language processing benefits. Now we have good reason to think that even those who pick up an instrument later in life get them as well.
Another example: this study compares the musicians to the JROTC participants. That is, it does not compare them just to some random collection of non-musicians. Like these new musicians, the JROTC students had a highly disciplined practice schedule, had to function in a structured group, and so forth.
Because the study includes this “active control group,” we can be sure the results don’t come from–say–just being part of an organized school activity.
Most exciting: the students’ improvement in their ability to process language sounds.
This ability–called “phonemic awareness”–gets a lot of research attention, because it can predict success in several essential language skills: reading and writing, to name two.
We test phonemic awareness in many ways. For instance:
“Which one of these words does not rhyme with the others: bell, swell, full, tell.”
“Say the word ‘boat.’ Now, say that again without the ‘b’ sound.”
“How many syllables are there in the word ‘ventricle’?”
If music practice–even music practice begun in high school–can improve students’ phonemic awareness, it just might be able to help them do well in other courses where they have to process language–which is to say: all of them.
Reasons to Remain Calm
Tierney’s study is exciting, but we shouldn’t require all of our students to join band just yet. Here are a few important gaps in this research:
The students enrolled in music class improved their phonemic awareness, but Tierney didn’t measure if that improvement had any impact on, say, their performance in English class; or, perhaps, their ability to learn a new language. That effect is plausible, but not demonstrated here.
Also, Tierney & Co. measured two other linguistic abilities beyond phonemic awareness: phonological memory, and rapid naming. They found no statistically significant difference between the music students and the JROTC students in these two measures.
If one measure out of three shows improvement, that’s good. But it’s not a home run.
And, a point about the research methodology here. These students chose to join band or JROTC; they were not–in the “gold standard” of research–randomly assigned to do so. (Of course: there are many good reasons to let students choose, rather than forcing them into one group or another.)
The differences we see, therefore, might not have to do with the experience of band vs. JROTC. Instead, they might be differences in the kind of 9th grader who wants to be in band vs. the kind of 9th grader who wants to be in JROTC.
In other words: perhaps those band students were always a little better at discriminating among sounds, which is why they were drawn to music in the first place. Tierney’s team did try to rule that out with their various pre-study measures, but perhaps those differences are not captured by the tests we have.
We just don’t know. (Or, better said: we don’t “know” in the way that scientists want to know things.)
A Final Point
I understand why people are attracted to this argument: “students should do art because it makes them better at other things we do in school.”
I am more attracted to this argument: “students should make art because it’s an essential expression of human joy, sorrow, love, solitude, fun, reverence, and hope.”
In other words: I don’t think schools should foster art because it makes people better at STEM. I think schools should champion art because it makes people better at being people.
Regular readers of this blog remember Scott MacClintic’s post about “data informed instruction”; quoting W. Edwards Deming, Scott notes that “without data, you’re just another person with an opinion.”
Of course, gathering the right kind of data can be very tricky. What should we gather? How should we gather it?
Researchers at San Francisco State University have specific answers to both of these questions.
As they pondered STEM teaching, this research team asked some basic questions: how much classroom time is devoted to lecture, how much to pair discussion, and how much to reflective writing or clicker questions?
(The underlying goal: encourage more discussion and writing.)
To answer these questions–that is, to gather this kind of data–they developed a system that can listen to classroom sound and keep track of lecture time, discussion time, and silent working time.
We can hope a) that this system will be tested for other disciplines and other academic levels, and b) that it will be as handy as an app in the near future.
If these hopes come true, then with the click of a few buttons, we can get useful information about our own teaching practices, and fine-tune the balance of our pedagogical strategies.
(The “DART” is currently “under revision”; I don’t know when it will be back up and running.)
Until then, it’s good to know that–despite all the vexations that come with technology–it can still help us hone our craft and benefit our students.
How can we encourage young women to pursue STEM fields?
In the German state of Baden-Württemberg, school leaders tried a substantial reform: they increased the math requirement during the final two years of high school. Instead of taking math three days a week, all students had to take math four days a week.
What were the results of increasing the math requirement by 1/3 for 2 years? (That sentence sounds like a word problem, no?)
A mixed bag.
The good news: this reform reduced the gap between male and female achievement scores in math. On the surface, in other words, it seems young women learned more.
This result should be very exciting. However…
The so-so news: this additional math work did very little to increase women’s participation in STEM fields in college. Instead, it increased the STEM interest of male college students–the enrollment gap remained about the same.
And, the bad news: although the women learned more math, they felt worse about their own math abilities.
The reason for this last result isn’t clear — the author’s hypothesis honestly sounds a little convoluted to me.
But, given the size of the data pool behind this study, the conclusion seems clear: requiring more math may boost math learning, but — for women — it’s not sufficient to boost math confidence and interest in STEM fields.
At a minimum, the study suggests that we should think not only about how much math students learn, but how they learn it.
A further point: I don’t know how the math curriculum in a typical Baden-Württemberg high school compares to that of a school in the US. Before we try this intervention, we should (again) think not only about how much math students learn, but what math they learn.
