WEBVTT 00:00:00.000 --> 00:00:07.500 I'm going to talk a little bit about our research paper and the link to it is at the end of the presentation. 00:00:08.280 --> 00:00:13.460 But I'm happy to send anybody a summary report if anybody's interested. 00:00:16.420 --> 00:00:16.980 Okay. 00:00:18.480 --> 00:00:20.700 So, what were the issues? 00:00:20.700 --> 00:00:29.880 Well, in England, 12% of primary school children do not speak English as a first language, referred to as non-native speakers. 00:00:30.000 --> 00:00:36.020 And the number of non-native speakers has increased by one-third between 2003 and 2009. 00:00:37.280 --> 00:00:44.040 So the question we were interested in is, does this matter for educational attainment for native English speakers at the end of primary school? 00:00:45.800 --> 00:00:47.080 And this is just a picture. 00:00:47.800 --> 00:00:54.500 In our paper, we particularly divide people between non-white ethnic origin and white ethnic origin. 00:00:54.500 --> 00:00:58.860 and that's just to show you a graph of the percentage of children in year six 00:00:58.860 --> 00:01:02.780 who were non-native speakers between 2003 and 2009. 00:01:04.680 --> 00:01:10.140 And this is just to show you the ethnic backgrounds of people who, 00:01:10.720 --> 00:01:15.540 native English speakers, people who speak English as an additional language, 00:01:16.200 --> 00:01:17.600 2003, 2009. 00:01:17.600 --> 00:01:20.820 There hasn't really been much change over time 00:01:20.820 --> 00:01:24.760 in the proportions of different ethnicities, 00:01:25.180 --> 00:01:28.920 but just to show you that there is some diversity. 00:01:29.380 --> 00:01:33.560 So obviously native English speakers aren't all white. 00:01:33.840 --> 00:01:35.580 There's a whole... 00:01:35.580 --> 00:01:39.040 All the ethnic minorities are represented. 00:01:41.800 --> 00:01:54.489 And this is the percentage of children who are non speakers of English and of white ethnic origin And there you show in particular there been a sort of acceleration from 2005 onwards 00:01:54.489 --> 00:01:59.089 and this is the impact of Eastern European enlargement. 00:01:59.089 --> 00:02:03.449 And this is the percentage of white non-native speakers by school type, because it's something 00:02:03.449 --> 00:02:08.689 that we particularly were interested in in the paper, that there was a disproportionate 00:02:08.689 --> 00:02:13.369 an increase in demand for Catholic schools because a lot of the people from Eastern Europe, 00:02:13.369 --> 00:02:17.009 of course, are from Poland, which is a very Catholic country, and that this is something 00:02:17.009 --> 00:02:18.549 we wanted to use in our analysis. 00:02:18.829 --> 00:02:19.569 I'll say something. 00:02:20.089 --> 00:02:21.489 There's a little thing about that later. 00:02:23.289 --> 00:02:27.869 So this is the average characteristics of pupils in each year group in 2009. 00:02:28.729 --> 00:02:37.149 So I suppose the main thing I want to emphasize here is that non-native English speakers are 00:02:37.149 --> 00:02:42.909 more likely to be eligible for free school meals. So they're from more socio-economically 00:02:42.909 --> 00:02:49.769 disadvantaged backgrounds themselves. And also to say that, of course, by the end of 00:02:49.769 --> 00:02:58.809 Key Stage 2, their scores in English reading, writing, and maths will be lower than native 00:02:58.809 --> 00:03:05.789 English speakers by something. Although within maths, actually, there's much more similarity. 00:03:06.149 --> 00:03:10.269 So it's more in reading and writing that they fall behind relative to, say, maths. 00:03:11.369 --> 00:03:19.689 And this is just the school characteristics, I think. 00:03:20.709 --> 00:03:23.649 Yes, this is the school characteristics within each category. 00:03:24.009 --> 00:03:26.389 And the thing I'd really like to draw your attention to here 00:03:26.389 --> 00:03:29.269 is that people who don't speak English as a first language 00:03:29.269 --> 00:03:32.109 are more likely to go to schools in more disadvantaged areas. 00:03:32.909 --> 00:03:35.309 They're more likely to go to schools with a higher proportion 00:03:35.413 --> 00:03:40.753 of people on free school meals relative to native English speakers, and that's quite 00:03:40.753 --> 00:03:42.853 important for the analysis. 00:03:43.913 --> 00:03:45.133 So what did we do? 00:03:45.173 --> 00:03:49.513 We look at the National Pupil Database, which is a census of all pupils and state schools 00:03:49.513 --> 00:03:51.933 between 2002 and 2009. 00:03:52.033 --> 00:03:53.453 2002 is when it starts. 00:03:53.973 --> 00:03:56.013 And we link that to the school-level data sets. 00:03:56.453 --> 00:04:00.653 So we have information on whether students have English as their first language, their 00:04:00.653 --> 00:04:08.233 free school meal status, their key stage data from the years that's there, and school level 00:04:08.233 --> 00:04:09.293 information as well. 00:04:11.293 --> 00:04:11.573 Okay. 00:04:12.353 --> 00:04:13.373 So what do we do? 00:04:13.533 --> 00:04:17.753 We look at the association between the percentage of non-native English speakers in year six 00:04:17.753 --> 00:04:23.913 and the key stage two attainment of native English speakers while at the same time taking 00:04:23.913 --> 00:04:25.293 account of detailed characteristics. 00:04:25.433 --> 00:04:26.473 That's one of our strategies. 00:04:27.473 --> 00:04:34.773 And then the second thing we do is we look at whether schools that experienced an influx of white non-native English speakers after enlargement 00:04:34.773 --> 00:04:43.453 also experienced a disproportionate change in the attainment of native English speakers at the end of primary schools. 00:04:43.453 --> 00:04:51.473 So in other words, we saw in the graph that I showed you that Catholic schools had a large increase in the percentage of white non-native English speakers. 00:04:51.473 --> 00:05:00.313 did they also see a disproportionate fall in the attainment of native English speakers at the end of primary school? 00:05:00.593 --> 00:05:02.133 And that was the question that we were looking at there. 00:05:02.953 --> 00:05:03.953 So what did we find? 00:05:04.213 --> 00:05:09.553 Well, when we look at the raw data, there's a negative association between the percentage of non-native English speakers 00:05:09.553 --> 00:05:13.153 and the percentage of native English speakers if we don't control for anything. 00:05:14.113 --> 00:05:28.422 Now that negative association goes away completely when we put in minimal controls for the characteristics of native English speakers Why Because non English speakers go to more disadvantaged schools on average As soon as you take that into account 00:05:28.802 --> 00:05:36.962 that negative association goes away. So what we conclude is that there is no association on average 00:05:36.962 --> 00:05:41.922 between the percentage of non-native English speakers in your year group in primary school 00:05:41.922 --> 00:05:44.002 and the attainment of native English speakers. 00:05:45.822 --> 00:05:46.882 So why? 00:05:47.142 --> 00:05:49.162 Why do we find this and there's no association? 00:05:49.782 --> 00:05:51.562 Well, two possible explanations. 00:05:52.262 --> 00:05:55.962 Schools are very well equipped to deal with increasing numbers of non-native English speakers 00:05:55.962 --> 00:06:00.222 and they organise things such that there's no negative impact on native English speakers. 00:06:00.322 --> 00:06:03.282 In other words, it's not that there isn't a challenge and it isn't difficult, 00:06:03.442 --> 00:06:06.402 but you do it very well and you have the resources to do it. 00:06:06.722 --> 00:06:10.662 And that's why when researchers like me look at data, you don't see any negative impact. 00:06:10.662 --> 00:06:17.062 The second possibility, not mutually exclusive, is that non-native English speakers may have 00:06:17.062 --> 00:06:23.302 less good language skills, but they catch up fast, and often their parents are very motivated 00:06:23.302 --> 00:06:29.662 because that's why their parents have immigrated, and they're often very well educated, and 00:06:29.662 --> 00:06:34.662 that may offset any disadvantage they have in terms of language. 00:06:34.662 --> 00:06:39.202 Now, this paper doesn't say that there aren't case studies 00:06:39.202 --> 00:06:42.522 and there aren't examples of schools that have had exceptionally difficult problems 00:06:42.522 --> 00:06:43.622 and where it has an impact. 00:06:44.102 --> 00:06:47.382 This is just looking at things on average across the country. 00:06:47.682 --> 00:06:50.202 But we have broken things down in various groups, 00:06:50.522 --> 00:06:52.982 and it's much the same story no matter how we cut the data. 00:06:52.982 --> 00:06:55.482 So personally, I'm quite convinced by this, 00:06:55.562 --> 00:06:58.642 that it isn't something that we should be really worried about 00:06:58.642 --> 00:07:03.722 in terms of having more students in our schools 00:07:03.722 --> 00:07:05.702 that don't speak English as a first language. 00:07:06.922 --> 00:07:07.082 Okay. 00:07:08.022 --> 00:07:09.202 Thank you very much.