WEBVTT 00:00:12.090 --> 00:00:17.490 The purpose of the practice guide is to develop guidelines about what we know works 00:00:17.490 --> 00:00:23.960 in encouraging more girls-and more women-to go into careers that use math and science. 00:00:23.960 --> 00:00:27.590 There are a number of reasons why we need to encourage more girls 00:00:27.590 --> 00:00:30.550 and more women to go into math and science. 00:00:30.550 --> 00:00:35.190 One of the reasons is that we're facing a serious shortage in the coming years 00:00:35.190 --> 00:00:40.670 in the United States in the number of mathematicians and scientists that we have. 00:00:40.670 --> 00:00:47.240 Increasing number of jobs require math and science and we have a real shortage. 00:00:47.240 --> 00:00:50.210 In addition, we know that there are a number of areas 00:00:50.210 --> 00:00:53.600 where girls and women are underrepresented. 00:00:53.600 --> 00:00:58.700 So we're missing out on an awful lot of talented people who really could be advancing these areas 00:00:58.700 --> 00:01:04.160 and be critical for our work force and critical for our ability to stay competitive 00:01:04.160 --> 00:01:07.190 and cooperative in a global economy. 00:01:07.190 --> 00:01:11.210 One of the things the guide wants to convey to educators, to parents, 00:01:11.210 --> 00:01:15.930 to the whole general public is that there are, in fact, things that we can do 00:01:15.930 --> 00:01:21.060 that will encourage more girls and more women to go into areas of math and science. 00:01:21.060 --> 00:01:26.080 And if we do these things, we definitely should be increasing-changing girls' and women's lives, 00:01:26.080 --> 00:01:31.880 changing their options and really increasing our preparedness for the workforce. 00:01:31.880 --> 00:01:36.260 We're often asked, "where are the differences between girls and boys 00:01:36.260 --> 00:01:38.050 and how they perform in math and science?" 00:01:38.050 --> 00:01:42.780 And in fact we find that on some of the measures, girls are doing way better. 00:01:42.780 --> 00:01:46.350 On some of the measures boys are doing way better. 00:01:46.350 --> 00:01:51.290 Different kinds of measures of the same sorts of skills, but that's why we say 00:01:51.290 --> 00:01:55.660 that on average we're not looking at real differences in abilities. 00:01:55.660 --> 00:01:59.570 We know that girls tend to get better grades in school, for example, 00:01:59.570 --> 00:02:02.320 in math and science courses and in all their courses. 00:02:02.320 --> 00:02:06.030 And, in fact, right now, they're taking slightly more math 00:02:06.030 --> 00:02:08.820 and science courses as they get through high school. 00:02:08.820 --> 00:02:12.840 So those are one of the measures where we know girls are doing particularly well. 00:02:12.840 --> 00:02:17.530 The other measures where they're doing less well is they do slightly less well than boys 00:02:17.530 --> 00:02:19.900 on some of the standardized exams. 00:02:19.900 --> 00:02:24.710 On average we count-there's no evidence that it's naturally better 00:02:24.710 --> 00:02:27.520 for boys to go into math and sciences. 00:02:27.520 --> 00:02:33.390 What we are concerned with is the low participation rate of girls in a number 00:02:33.390 --> 00:02:36.280 of the sciences and in mathematics. 00:02:36.280 --> 00:02:39.550 So, for example, what's the evidence that we have a problem? 00:02:39.550 --> 00:02:45.460 About almost half of all undergraduates in math majors are female, 00:02:45.460 --> 00:02:51.900 so very close to 50 percent numbers-46 percent, 45 percent, depending on what you're measuring. 00:02:51.900 --> 00:02:56.050 But when you look at the doctorates, they're only about 25 percent. 00:02:56.050 --> 00:03:00.850 So we're losing an awful lot of very talented women who are getting good grades 00:03:00.850 --> 00:03:04.480 who are then not pursuing careers in mathematics. 00:03:04.480 --> 00:03:06.480 If you looks at their participation rates 00:03:06.480 --> 00:03:11.730 in medical school-in medical school graduation rates are now 50 percent female; 00:03:11.730 --> 00:03:14.420 veterinary school, 75 percent female. 00:03:14.420 --> 00:03:19.560 And those are certainly areas that are very heavily scientific. 00:03:19.560 --> 00:03:23.620 But if you look at other areas like physics, like engineering, like certain areas 00:03:23.620 --> 00:03:26.410 of mathematics, the rates are much lower. 00:03:26.410 --> 00:03:31.360 So it's certainly not a matter of, "can girls do math, can girls to science?" 00:03:31.360 --> 00:03:32.440 Obviously they can. 00:03:32.440 --> 00:03:34.010 They're already doing it. 00:03:34.010 --> 00:03:38.620 But there are certain fields within math and science where they're not participating. 00:03:38.620 --> 00:03:40.130 Why is that important? 00:03:40.130 --> 00:03:44.950 It's important because we know, first, of the talent that we're losing. 00:03:44.950 --> 00:03:48.810 And secondly, when girls and women go into an area, 00:03:48.810 --> 00:03:52.900 they sometimes study somewhat different topics within an area. 00:03:52.900 --> 00:03:55.720 Girls are more like - and women-are more likely to be more concerned 00:03:55.720 --> 00:03:58.880 with environmental issues, environmental engineering. 00:03:58.880 --> 00:04:04.070 In biology, they're more likely to be concerned with some reproductive biological issues. 00:04:04.070 --> 00:04:09.600 By cutting out a portion of the population, we're also cutting the full range of diversity 00:04:09.600 --> 00:04:12.820 in our interests and where we make our advances. 00:04:12.820 --> 00:04:16.269 It isn't until you start getting to puberty-where lots 00:04:16.269 --> 00:04:21.860 of things are changing-that you find some of the differences in people's interests 00:04:21.860 --> 00:04:27.690 and in their choices when they have some choice of courses and some choice of activities. 00:04:27.690 --> 00:04:29.580 People say, "Well where does it come from?" 00:04:29.580 --> 00:04:32.580 They certainly come from our expectations. 00:04:32.580 --> 00:04:38.150 They may come from differences in our development and in our hormone levels. 00:04:38.150 --> 00:04:44.190 But none of it implies that we can't be encouraging more girls to get into math 00:04:44.190 --> 00:04:48.730 and science and that they can't all be achieving at equally high rate. 00:04:48.730 --> 00:04:52.790 People always ask the "why" question about why are girls 00:04:52.790 --> 00:04:56.930 and boys not, on average, exactly the same? 00:04:56.930 --> 00:04:59.070 And those answers are really complicated. 00:04:59.070 --> 00:05:04.800 There are certainly differences in developmental processes between girls and boys, 00:05:04.800 --> 00:05:10.140 but none of it puts a limit on anyone's intellectual growth or ability 00:05:10.140 --> 00:05:13.990 to study or excel in any academic area. 00:05:13.990 --> 00:05:17.380 We have five recommendations in our practice guide. 00:05:17.380 --> 00:05:20.350 Let me just briefly give you an overview of them. 00:05:20.350 --> 00:05:24.700 Our first is sort of interesting-and you know these are true for boys and girls. 00:05:24.700 --> 00:05:27.060 They may be particularly true for girls, 00:05:27.060 --> 00:05:30.620 but these will help everybody improve in science and math. 00:05:30.620 --> 00:05:37.660 And one is to really convey the idea to students that, in fact, abilities are expandable. 00:05:37.660 --> 00:05:42.390 I used to be a dean of undergraduate studies at a state university, 00:05:42.390 --> 00:05:47.260 and when I was dean sometimes students would come in to talk to me and they would say, 00:05:47.260 --> 00:05:50.490 "I can't do math," or, "I can't do science." 00:05:50.490 --> 00:05:53.680 And this was their way of saying "I need to get out of that requirement." 00:05:53.680 --> 00:05:56.560 And I would say, "What does it mean you can't do it? 00:05:56.560 --> 00:05:58.150 Is it like an allergy? 00:05:58.150 --> 00:06:01.200 Is it like, you know, you're allergic to it?" 00:06:01.200 --> 00:06:06.740 And when you listen to what people are saying, what they're saying is, "I find it hard." 00:06:06.740 --> 00:06:11.660 And, in fact, somewhere along the line they never learned that it's supposed to be hard. 00:06:11.660 --> 00:06:17.340 And it is hard, but with the appropriate work, with enough hard work, in fact, you get better. 00:06:17.340 --> 00:06:19.060 And then it gets easier. 00:06:19.060 --> 00:06:23.630 And we really need to teach people that it's not that: "You're good in math. 00:06:23.630 --> 00:06:25.360 You're not good in math. 00:06:25.360 --> 00:06:26.580 You're good in science. 00:06:26.580 --> 00:06:28.120 You can't do science." 00:06:28.120 --> 00:06:30.710 In fact, we can all get better. 00:06:30.710 --> 00:06:34.520 And with hard work, what is hard now becomes easy. 00:06:34.520 --> 00:06:39.120 Most teachers know that they need to provide feedback to students. 00:06:39.120 --> 00:06:41.700 Information about how well they're doing. 00:06:41.700 --> 00:06:45.870 But there's two kinds of feedback and they don't have the same results. 00:06:45.870 --> 00:06:48.590 One is a global, generalized feedback. 00:06:48.590 --> 00:06:52.630 So if, for example, you take a standardized exam, you get a number, 00:06:52.630 --> 00:06:56.270 and that number says you did well, you didn't do well. 00:06:56.270 --> 00:07:00.980 But it doesn't give you any more specific information to tell you what you know 00:07:00.980 --> 00:07:03.610 and more importantly what you don't know. 00:07:03.610 --> 00:07:07.420 And we really need to be sure that when we're giving people feedback-one, 00:07:07.420 --> 00:07:09.070 that it's real feedback. 00:07:09.070 --> 00:07:13.780 So it's not the sort of thing that regardless of what you do we tell everyone, "that's great." 00:07:13.780 --> 00:07:17.140 Because that's really not helpful, and that's misleading. 00:07:17.140 --> 00:07:21.480 But, in fact, we know what you do well, what you don't do well, 00:07:21.480 --> 00:07:25.160 and how you can use that feedback to improve. 00:07:25.160 --> 00:07:27.100 And those are just very good practices. 00:07:27.100 --> 00:07:32.470 They'll help girls and boys know what they need to focus on to advance. 00:07:32.470 --> 00:07:38.090 Learning math and science for most people is different from learning literature, for example. 00:07:38.090 --> 00:07:40.200 And they need a different kind of feedback 00:07:40.200 --> 00:07:44.020 because there's certain skills they may be missing, and then they can really work 00:07:44.020 --> 00:07:47.300 on developing those skills-filling in holes. 00:07:47.300 --> 00:07:51.070 Another one of our recommendations is to provide role models. 00:07:51.070 --> 00:07:55.000 And this comes out of an old psychological literature. 00:07:55.000 --> 00:07:57.780 I bet some of our teachers are familiar with the work 00:07:57.780 --> 00:08:01.220 of Albert Bandura, who talked about motivation. 00:08:01.220 --> 00:08:04.460 And one of the things with a role model is you're able 00:08:04.460 --> 00:08:08.470 to say, "Someone like me can do this." 00:08:08.470 --> 00:08:13.850 So if, for example, I'm a young, Hispanic girl growing up in a neighborhood 00:08:13.850 --> 00:08:19.870 where there's little English and I've never seen a scientist that looks like me growing up, 00:08:19.870 --> 00:08:25.790 what you're implicitly telling me is people like me don't become scientists. 00:08:25.790 --> 00:08:30.520 And even if that's not the message you mean to put across, that is the message 00:08:30.520 --> 00:08:33.710 that gets conveyed in a thousand small ways. 00:08:33.710 --> 00:08:37.690 And by showing you some Hispanic woman who grew up in a house 00:08:37.690 --> 00:08:41.610 where they didn't speak much English, who went on and made scientific 00:08:41.610 --> 00:08:45.950 and mathematical contributions, what you're really saying is people just 00:08:45.950 --> 00:08:48.160 like you, in fact, do do these things. 00:08:48.160 --> 00:08:51.410 And that turns out to be incredibly important. 00:08:51.410 --> 00:08:56.810 We have really new respect for how all of those implicit messages get put 00:08:56.810 --> 00:09:02.300 across without even meaning to - what we're telling people about their abilities. 00:09:02.300 --> 00:09:08.010 Our fourth recommendation is really to encourage more live experimentation 00:09:08.010 --> 00:09:14.520 in the classroom-things that involve people's own curiosity, that sparks their interest. 00:09:14.520 --> 00:09:20.540 We also found-and a lot of this work has been done by people looking at girls' expectancies 00:09:20.540 --> 00:09:24.710 of success-that even when girls value math and science, 00:09:24.710 --> 00:09:28.080 they don't see its value in their every day lives. 00:09:28.080 --> 00:09:30.040 They don't see how they're going to use it. 00:09:30.040 --> 00:09:32.740 They don't see it as something worth knowing. 00:09:32.740 --> 00:09:37.270 And if it's not something worth knowing, why should I work so hard at it? 00:09:37.270 --> 00:09:39.350 There is an old movie you may recall, 00:09:39.350 --> 00:09:44.030 I'll bet some of our teachers do, when - Peggy Sue Got Married. 00:09:44.030 --> 00:09:47.940 And she gets to come back from the future, and she's in her high school class, 00:09:47.940 --> 00:09:52.240 and she tells everyone, "Trust me, you're never going to use algebra." 00:09:52.240 --> 00:09:54.890 And, you know, everyone in the theater just breaks out laughing. 00:09:54.890 --> 00:09:59.120 And in fact, a lot of that are the messages that we have. 00:09:59.120 --> 00:10:05.090 So that if we could see the use for this in our lives and in our future careers, then in fact, 00:10:05.090 --> 00:10:09.980 it will increase motivation and time on task and all those other things that are important 00:10:09.980 --> 00:10:12.390 for learning that's really going to last. 00:10:12.390 --> 00:10:17.910 The last recommendation, the fifth one, is one that I've particularly done a lot of work in, 00:10:17.910 --> 00:10:21.440 and it's the area of visual spatial skills training. 00:10:21.440 --> 00:10:28.460 And what that means is training people how to think spatially. 00:10:28.460 --> 00:10:33.760 We have remedial reading, for example, for kids who have trouble with reading. 00:10:33.760 --> 00:10:35.900 We have remedial math. 00:10:35.900 --> 00:10:39.150 But we have nothing in our curriculum, typically, 00:10:39.150 --> 00:10:42.280 that teaches people about spatial thinking. 00:10:42.280 --> 00:10:47.930 And there are a number of areas of mathematics-particularly geometry for example, 00:10:47.930 --> 00:10:53.530 or topology, certain areas in the sciences-that tend to be more spatial. 00:10:53.530 --> 00:10:58.350 And if you look at some of the cognitive differences literature, this is one of the areas 00:10:58.350 --> 00:11:02.500 where you find some of the biggest differences between boys and girls, 00:11:02.500 --> 00:11:04.860 with the advantage going to boys. 00:11:04.860 --> 00:11:10.400 Everyone will benefit if we have a visual spatial skills training, 00:11:10.400 --> 00:11:14.570 so I know how to translate a word problem to a picture. 00:11:14.570 --> 00:11:19.040 And often that will make a very big difference in visualizing what you're solving 00:11:19.040 --> 00:11:23.650 for and understanding the problem.