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Trainee teachers’ knowledge of autism: implications for understanding and inclusive practice

August 2019
Oxford Review of Education 46(1):1-20

DOI:10.1080/03054985.2019.1645651

Authors:

Jonathan Vincent

Lancaster University

Kevin Ralston

The University of Edinburgh

This current study draws on data from a large sample of trainee teachers in England to provide a long overdue baseline assessment of the knowledge of autism. It has particular import given the recent research that shows that 60% of autistic young people identified ‘having a teacher who understands autism’ as the main thing that would make school better for them. We find that, based on the Autism Awareness Survey, levels of knowledge were comparatively high among our n = 326 respondents. However, whilst this is encouraging, our findings also point to an underestimation of knowledge, which indicates the need for additional resources and training to develop trainee teachers’ self-efficacy and confidence in their pedagogical practice. Finally, in order to understand more about the gap between autistic children’s experiences and teachers’ understanding, this study signals a need to review autism knowledge scales to better reflect an experiential knowledge that goes beyond the clinical descriptors.

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Trainee teachers’ knowledge of autism: implications for

understanding and inclusive practice

Journal:

Oxford Review of Education

Manuscript ID

CORE-2019-0006.R2

Manuscript Type:

Article

Keywords:

Knowledge, Autism, Pre-service teachers, Understanding, Inclusive

practice, Teaching

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Trainee teachers’ knowledge of autism: implications for

understanding and inclusive practice

Abstract

This current study draws on data from the largesta large sample of trainee teachers in England to

provide a long overdue baseline assessment of the knowledge of autism. It has particular import given

the recent research that shows that 60% of autistic young people identified ‘having a teacher who

understands autism’ as the main thing that would make school better for them. We find that, based on

the Autism Awareness Survey, levels of knowledge were comparatively high among our n=326

respondents. However, whilst this is encouraging, our findings also point to an underestimation of

knowledge, which indicates the need for additional resources and training to develop trainee

teachers’ self-efficacy and confidence in their pedagogical practice. Finally, in order to understand

more about the gap between autistic children’s experiences and teachers’ understanding, this study

signals a need to review autism knowledge scales to better reflect an experiential knowledge that goes

beyond the clinical descriptors.

Keywords: autism; knowledge; teaching; trainee; pre-service; inclusive practice

Introduction

Autism is the most common type of identified need for pupils in English schools who have an

education, health and care plan or statement of special educational needs (SEN). It is the

primary need for 27% of these pupils – 31% of boys with a statement or EHC plan, and 16%

of girls (Department for Education, 2017). Around 70% of autistic1 children in England

attend mainstream schools but based on survey data from 176 autistic young people, 2,573

parents and carers, and 308 teachers fewer than half of them report positive experiences (All-

Party Parliamentary Group on Autism, 2017). The same report finds that, for 60% of young

1 The term autistic children (rather than children with autism) is used throughout to reflect the identify-

first preference indicated by autistic people (Kenny et al, 2016).

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people and 70% of their parents, the main thing that would make school better for them is

having a teacher who understands autism.

In response to such findings, this paper aims to provide a long overdue baseline assessment of

the knowledge of autism amongst a sample of trainee teachers in England, drawing on the

largest dataset of its kind to date. According to Harrison et al. (2016) the last study among

education professionals in the UK, using a validated instrument, was two decades ago (Helps

et al., 1999) and surveyed only 72 teachers and teaching assistants in the London area. The

current study thus offers a useful litmus test of levels of knowledge and indicates where gaps

may still exist, which has particular import given the Carter Review’s (2015)

recommendation that every new teacher has a basic understanding of autism.

Understanding autism in context

Autism is a lifelong neurodevelopmental disability. It is frequently associated with

communication difficulties and the presence of rigid and repetitive behaviours, both of which

can impact on social functioning (American Psychiatric Association, 2013). It can also be

linked to difficulties in cognition, behavioural flexibility, emotional regulation, and altered

sensory sensitivity with many autistic children experiencing over- or under-sensitivity to

sounds, touch, tastes, smells, light, colours, temperatures or pain (Buckley, 2017; NAS, 2016;

Remington and Fairnie, 2017). Until 2013 autism was described as a triad of impairments

with variable intelligence quotient and language development leading to various subgroups

including classic autism, atypical autism, pervasive developmental disorder, and Asperger’s

syndrome although following changes to the Diagnostic and Statistical Manual of Mental

Disorders (DSM-5, 2013) and World Health Organization’s International Classification of

Diseases (ICD-11, 2018) these were collapsed into one autism spectrum disorder (ASD)

diagnosis.

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Today, autism is considered a common condition, with recent British epidemiological studies

estimating prevalence at between 1% (Brugha et al., 2011) and 1.7% (Russell et al., 2014).

There is, however, substantial heterogeneity both between and within autistic individuals

(Georgiades et al. 2013) with many presenting uneven profiles such as capacities in verbal

fluency but difficulties in flexible thinking or even variant capacities in the same area, such as

good long-term memory but poor short-term memory (Buckley, 2017). Thus an autistic

spectrum is generally understood as a useful tool for reflecting the wide diversity and varying

degrees of neurological differences across the population (Wing, 1988). According to Mandy

(2018:1), ‘there is not a single autism, but rather there are hundreds, or even thousands, of

‘autisms’.

Whilst much of the literature uses pejorative terms such as ‘disorder, ‘deficit’ or

‘abnormality’ to describe autism, there is a growing body of research which seeks to frame it

more positively in terms of individual ‘differences’ (Baron-Cohen 2015) and recognise the

positive implications of an autism diagnosis as well as the difficulties (Janzen 1996;

Remington and Fairnie, 2017; Soulieres et al., 2011). Van Hees et al. (2015:1684) identify

such autistic strengths as ‘strong memory, focus precision and an eye for detail, dedication,

the ability of putting one’s mind to a subject, analytical skills, remarkable powers of

observation’. Whilst such a strengths-based reading of an autism diagnosis could potentially

lead to more positive attitudes and outcomes for autistic learners, Russell et al. (2019) suggest

that this tends to be moderated by three factors including: the social and environmental

contexts in which they strengths are played out; the extent to which the autistic traits can be

controlled by the individual; and how they these are understood and perceived by the autistic

individual and others. It is therefore essential that teachers and educational practitioners have

a secure knowledge of autism within the context of the school setting in order to recognise

autistic pupils’ strengths and difficulties where they might arise.

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Enhanced knowledge can lead to more inclusive practice

Evidence suggests that knowledge of autism is a salient predictor of practitioners’ ability and

willingness to provide inclusive learning opportunities to autistic students in both mainstream

and special education settings (Baglieri & Shapiro, 2012; Busby et al., 2012; Segall and

Matthews, 2012). Whilst a couple of recent studies demonstrate that teacher education

students report accurate knowledge about most aspects of autism (Talib and Paulson, 2015;

Blackwell et al. 2017), the literature more generally indicates that there is a gap in teachers’

knowledge of autism (Able et al. 2015; Busby et al., 2012; Jung et al., 2011; McConkey and

Bhlirgri, 2003; Segall, 2008). For example, in Segall & Campbell’s (2012) study, of 196

education professionals surveyed mainstream teachers and headteachers responded that they

did not know the answer to around seven of 15 items assessing current knowledge of autism.

Moreover, some studies suggest the endorsement of inaccurate beliefs about autism,

particularly with respect to aetiology (Brubaker et al, 2010) and children’s abilities (Talib and

Paulson, 2015).

More broadly, the evidence indicates that there is a correlation between knowledge and

experience; where teachers had less exposure to autistic students, they tend to show lower

levels of knowledge of autism and as a consequence lower self-assessed confidence in

teaching autistic pupils (Haimour & Obaidat, 2013; Talib and Paulson, 2015). However,

according to Blackwell et al. (2017) whilst many trainee teachers often have exposure to

autism through personal or professional experiences, this does not necessarily translate into

accurate knowledge grounded in established research or lead to more effective teaching

practices for autistic students. In fact, Talib and Paulson’s (2015) study among 211

undergraduate trainee teachers showed that even where participants reported largely accurate

beliefs about autism, they did not feel competent about their abilities to cater for the needs of

autistic students. This trend is also verified in the UK by the All-Party Parliamentary Group

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on Autism (2017) which reports that of the 308 teachers surveyed, fewer than 50% of

teachers felt confident about supporting an autistic child in their classroom. Thus, current

evidence indicates a dual gap in practitioners’ knowledge about autism and self-assessed

confidence, with the outcome being uncertainty about their capacities to provide inclusive

provision for autistic pupils in school settings (Busby et al., 2012; Jung et al, 2011).

Given that knowledge of autism, experience, and attitudes are interrelated (Segall and

Campbell, 2012), it is suggested that the targeting of one area in training may have positive

effects on the other domains. Although there is strong evidence of effective educational

practice for autistic children including provision of early assessment, specific learning

strategies, behavioural interventions, and social skills programmes etc (see Parsons et al.,

2011) as well as an emphasis on SEN provision in Initial Teacher Training (Munday, 2016),

there remain significant gaps. Findings in British studies (Guldberg et al., 2017b; Helps et al.

1999; Jones et al., 2008) as well as those based in other international settings (Able et al.,

2015; Busby et al., 2012; Jung et al, 2011), report a lack of training among teachers working

with autistic students. Thus, improved provision for trainee teachers is central to enhancing

the levels of knowledge about autism and increasing practitioners’ confidence and positive

attitudes towards inclusive pedagogies for autistic pupils.

This is extremely important as research suggests that mainstream school settings place

increasingly complex demands on autistic learners (Brede et al, 2017). For example, with

enhanced knowledge about autism teachers may better understand difficulties with nonverbal

communication and pragmatic language such as interpreting body language and nonverbal

cues (Barnhill, 2014) and the challenges these can lead to in making and keeping friends

(Bauminger & Kasari, 2000; Calder, Hill, & Pellicano, 2013) or preventing bullying

(Sedgewick, et al. 2016). On a practical level, where teachers have more understanding about

autism they might more fully appreciate potential sensory issues including hyper-reactivity to

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stimuli including bright lights, loud noises, and strong odours. According to Fernández-

Andrés et al. (2015) auditory filtering and hearing is one of the most affected sensory

modalities in the classroom environment but Hanley et al. (2017) show how the presence of

highly visual displays in classrooms can also make orienting attention much more

challenging for autistic children compared to their non-autistic peers. Such environmental

challenges can lead, for some, to sensory overloads where stimuli cause extreme physical and

emotional distress as well as feelings of anxiety, despair, and the increase in ‘restrictive and

repetitive behaviours’ which stem from a child’s ‘attempt to introduce order into their chaotic

world’ (Wing 2001:99).

Finally, one important environmental predictor of potential academic achievement for autistic

pupils is where and how learning takes place, which is closely associated with knowledge of

autism (Segall and Campbell, 2012). Based on a systematic review of the literature

surrounding autism and academic achievement, Keen, Webster, & Ridley (2016) identify

trends that indicate that autistic pupils with higher IQ scores (IQ 80-120) tended to do better

on measures of academic achievement. Similarly, in comparing students across educational

settings Kurth and Mastergeorge (2010) found that autistic students in inclusive settings tend

to outperform others matched on IQ and adaptive behaviour in self-contained classrooms on

reading, writing and math. Overall, such findings signal an urgent requirement to address the

current gap in teachers’ knowledge about autism and how best to improve academic

opportunities and wider outcomes for these pupils.

Consequently, the present study aimed to address three research questions:

1. What is the overall level of knowledge about autism among trainee teachers in one

School of Educationhigher education institution in England?

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2. What is the relationship between respondents’ self-assessed knowledge of autism

and that demonstrated on the Autism Awareness Survey (AAS)AAS?

3. Are demographics (sex, age, relationship to autism etc) associated with self-

assessed knowledge of autism and responses to the AAS?associated with the

levels of self-assessed knowledge and that as demonstrated on the AAS survey?

Methods

Procedure

The sample was derived from trainee teachers in the School of Education at one university in

England. The response rate was 67% (n=326) of the total number of students enrolled on

initial teacher training courses (n=485). Of those respondents who returned the survey 312

(96%) had complete information on the variables used in the analysis presented and this

comprised the analytic sample. This sample is the largest on trainee teacher’s knowledge of

autism in the United Kingdom. Comparable studies are Talib and Paulson (2015), who

surveyed n=211 teacher education students from a Midwestern university in USA and

Blackwell et al. (2017) who drew on n=87 pre-service teachers from three universities in the

United States. Harrison et al.’s (2016) review identified other studies using validated

instruments to measure knowledge of autism among teachers and school staff with samples

ranging from n=64 teachers in Greece (Mavropoulou and Padeliadu, 2000) to n=391 general

and special education teachers in Saudia Arabia (Haimour and Obaidat, 2013). British studies

have also investigated this issue, for example Humphrey and Symes (2011) which surveyed

n=53 teachers and managers in schools; however, they did not report the use of a validated

instrument to assess knowledge.

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Respondents were invited to participate at the end of a core lecture at the University. They

were informed that their contribution was voluntary and all information collected during the

course of the study would be kept confidential on password-protected databases, under the

auspices of the university’s ethical review board (180110_ 000013045_ED). Surveys were

administered using the programme Qualtrics® enabling participants to complete the survey

on mobile devices, tablets or computers. Analysis was undertaken using Stata 15®.

Measuring knowledge of autism

Harrison et al. (2016) report that to date, the most widely applied measure of autism

knowledge is the Autism Knowledge Survey (AKS, Stone, 1987). This is a Likert-style

questionnaire comprised of autism ‘facts’ used to score respondents’ levels of knowledge.

Harrison et al. (2016) confirm that the measure is unidimensional and has reasonable internal

consistency (Cronbach’s alpha = 0.66), stable reliability, and showed initial reasonable

validity. Since 1987, the AKS has been updated to reflect new knowledge informed by

respective Diagnostic and Statistical Manuals of Mental Disorders (APA, 2013). The first

AKS revision occurred in 2007 (AKS-R, Swiezy, 2007) and has been used in several

subsequent studies (Bauer et al., 2015; Hartley-McAndrew et al., 2014; Heidgerken et al.,

2005). The Autism Awareness Survey (Tipton and Blacher, 2014), utilised in this current

study, is an updated version the original AKS measure with adequate internal consistency

(ω=0.60) reported by Cage, Di Monaco and Newell (2018). Given the AAS’s focus on

knowledge derived from the DSM-5, there are limits regarding the extent to which it can be

considered a measure of awareness of autism. Additional items related to practical

knowledge, current pedagogical practice or attitudes towards autism could uncover more

sensitised dimensions of autism not captured by the clinical descriptions. Nevertheless, the

AAS was perceived as being a useful validated tool for providing a much-needed baseline

measure of this dimension of autism knowledge among trainee teachers.

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The Autism Awareness Survey (AAS) consists of 14 forced choice Likert scale items ranging

from disagree, somewhat disagree, neutral, somewhat agree, agree (see, Table 2). If a

statement from the (AAS) was answered correctly by a respondent, with either an ‘agree’ or

‘disagree’ response, they score four points on the scale. Where the respondent was less sure,

but answers in the correct direction, choosing a ‘somewhat agree’ or ‘somewhat disagree’

response, they score three points. For a neutral response they score two points, a ‘somewhat’

wrong response scores one point and a fully wrong response scores zero. In principle, scores

can range from zero to 56. Respondents were also asked to estimate their own self-assessed

knowledge of autism (SAK) using the statement: ‘indicate your level of knowledge about

autism right now, with 0 representing no knowledge at all and 100 representing complete

knowledge’. This was on a scalefrom 0 to 100, where zero indicated no knowledge and 100,

complete knowledge; therefore, a score of 50 would represent the average population level of

knowledge of autism. This approach enabled a comparison between an objective measure of

knowledge about autism and trainee teachers’ subjective levels of knowledge. Similar

approaches have been applied elsewhere, for example within the medical field, Nabi et al.

(2008) asked respondents to score on a scale how knowledgeable they felt about the

prevention and detection of different forms of cancer, which was then examined in relation to

objective measures of knowledge.

[Table 1 – 90 words]

Background information on respondents was also gathered. The survey design reported by

Tipton and Blacher (2014) was taken as a benchmark template and equivalent data were

collected. The first three items gathered socio-demographic information about the participant,

including sex, age and level of study (undergraduate or postgraduate). The sample included

266 female (85%) and 46 male (15%) trainee teachers. Respondents were offered a

categorical selection of ages (18-20, 21-30, 31-40, 41-50, 51-60, 60+). The age of the sample

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was relatively homogenous, reflecting the age of students in teacher training. For this reason,

only two age groups of 18 to 20 and 21 to 30 were included in the analysis. Respondents

included those from both Undergraduate primary (94%) and Postgraduate secondary (6%)

courses.

The final survey items ascertained the respondent’s relationship to a person with a diagnosis

of autism (whether they self-identified as autistic; an immediate family member is autistic; an

extended family member is autistic; they come into contact with autistic persons through

social networks; or through professional experience; or have no relationship to autistic

persons). This classification was collapsed to three categories for analysis (see Table 1). In

response to the survey 56 people from the complete case sample (18%) reported having an

individual within their immediate or extended family with autism. This included one person

who self-identified as autistic. Over half the sample (160) had experience of autism through

work/social networks. One of the main variables of interest in Tipton and Blacher’s (2014)

article was a question asking whether autism is increasing or not. This was considered to

indicate awareness of the prevalent media message about the rise in autism diagnoses (Blair,

2016; Camarata, 2018), increasingly communicated through some form of social media

(Aboulkacem and Haas, 2018; Mitchell et al., 2016). This was also included here as a ‘yes’

(81%), ‘no’ (19%) dichotomy.

Bivariate analysis of key variables was undertaken using chi-square, ANOVA and t-tests

(Agresti et al. 2017). Multivariate OLS regression models were also used to assess

associations with outcome variables, the AAS and self-assessed knowledge of autism

measures (Kohler and Kreuter 2009). These outcomes were initially modelled in a base

model controlling for sex, age-group, and undergraduate/post-graduate education level.

Variables of interest were separately added to this base model. These comprised, the variable

identifying the level of contact to a person with autism and the variable indicating knowledge

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of whether autism is increasing in the UK. The measure of self-assessed level of knowledge

of autism was also included in the model with AAS as the outcome. A final model including

all factors together was estimated for both the AAS and SAK outcome variables.

Categorical variables were modelled using dummy category coding. Sex was included as a

binary male/female variable with men set as the reference category. The 18 to 20 age group

was set as the reference category in modelling. Undergraduate was set as the reference

category for the level of university study. The binary variable denoting whether autism is

increasing or not was included with the ‘yes’ category the reference in modelling. Those who

identified as autistic, or who have an autistic family member was set as the reference category

and compared with categories of whether an individual has social/professional contact with a

person with autism and a category denoting no contact with autistic people.

Results

In the first instance, results are presented as descriptive and bivariate analysisanalyses. A

systematic modelling analysis of the relationship between self-assessed level of knowledge of

autism and actual knowledge of autism is then outlined (see Appendix for a full breakdown

of results).

Overall levels of autism knowledge

The mean score of the self-assessed knowledge (SAK) measure for the sample was 38 (SD

19.8) with scores ranging from zero to 1002. The Autism Awareness Survey (AAS) scores

ranged from 27 to 553 with a mean of 43 (SD 4.6). A mean of 28 (the mid-point of the scale

2 One individual self-assessed as having zero knowledge of autism, whilst another self-assessed as having complete

knowledge of autism, scoring 100. The individual self-assessing as zero scored 39 on AAS, the individual self-

assessing as 100 scored 38 on AAS.

3 The individuals who score 27 and 55, at the extremes of AAS, both scored 60 on the SAK measure.

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range) would be equivalent to all neutral responses or as many correct as incorrect responses,

the sample score statistically significantly higher than this (t(310)=58 p=.000) indicating a

positive level of correct knowledge. This compares favourably with Tipton and Blacher’s

(2014) general university sample whose scores ranged from 17 to 55, with a mean of 38.5

(SD = 5.9). The most startling finding from the survey is that trainee teacher’s self-assessed

knowledge (SAK) of autism bears little comparison with their actual knowledge of autism as

measured by the AAS scale. The bivariate correlation between the AAS and the SAK

measure is very small and non-significant (r(310)=.08, p=.13).

[Table 2 – 225 words]

The two AAS statements identified as most correct were, there is one intervention that works

for all children with autism (correct response ‘disagree’ = 96% n=299) and children with

autism can grow up to live independently (correct response ‘agree’ = 95% n=295). The two

most incorrect statements were autism runs in families (correct response ‘agree’ = 27% n=84)

and autism is an emotional disorder (correct response ‘disagree’ = 61% n=190). Respondents

were asked whether they thought autism prevalence is increasing, which 253 (81%) correctly

identified to be the case. Interestingly, Tipton and Blacher’s (2014) study reported an

association between those who identity autism as increasing and those who erroneously

believe that vaccines cause autism. However, this was not the case here, where those who did

not think autism was increasing (an incorrect response) were more likely to correctly identify

that vaccines did not cause autism ( (3)=17, p=.001).

𝑥

Autism in self and family

Those with an autistic family member scored significantly higher on the AAS scale than

those with no relationship to autism (F=(2, 309)5, p=.007). The difference in score on the

AAS scale between the groups is small at around two points. By contrast, there is a large and

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significant difference in self-assessed knowledge of autism by the level of an individual’s

relationship to a person with autism (F=(2, 309)23, p=.000). Where a respondent had an

autism diagnosis, or had direct experience through a family member, then on average they

recorded a far higher self-assessed knowledge ( ) of autism than those who

𝑥

48,

report no experience of autism, ( ) or only professional/social contact with

𝑥

28,

autism ( ).

𝑥

40,

SD 19

Demographics

The sample of teachers in this study is relatively homogenous in education and age. There

were no significant bivariate associations found between the demographic factors and level of

knowledge in autism, or by whether people believe autism is increasing (see Appendix table

a1). Although there are no bivariate associations between demographic factors and AAS, the

modelling analysis below does reveal a significant relationship between sex and AAS.

Modelling knowledge and self-assessed knowledge of autism

Sex and whether people believe autism to be increasing was found to be significantly

associated with the AAS outcome. The contrast between males and females was consistently

significant across models (see Appendix Table a2). Females score on average 1.5 (p=.045)

points higher on AAS net of the other factors controlled in the full model. A significant

contrast between those who have an autistic family member and those with no wider

experience of people with autism (β=-1.9, p=.01) was reported in the base model adjusting

for age, sex and education. This contrast was no longer significant however, in the model

including all variables (β=-1.5, p=.06).

Following Tipton and Blacher (2014), we used the question regarding whether autism is

increasing as a proxy to indicate some awareness of a prevalent media message regarding a

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rise in the diagnosis of autism. The results here suggest that there is a significant contrast

between those who believe autism to be increasing and those who do not (β=2.1, p=.001).

Those who do not believe autism to be increasing score higher on AAS. This contrast remains

significant in the model containing all variables (β=1.9, p=.003). This is a counter intuitive

result because those who do not believe autism to be increasing are incorrect. It is therefore

initially surprising that they score more highly in knowledge of autism. It may be that people

who respond ‘no’ here believe the level of autism in the population to be steady whilst it is

the level of diagnosis, rather than level of autism, that has increased.

The results of the models of the AAS outcome contrast with models of the self-assessed

knowledge measure (Appendix Table a3). In modelling self-assessed knowledge of autism

the only significant predictor was the variable measuring the level of relationship an

individual has to a person on the autistic spectrum. This factor was not significantly

associated with the AAS measure in the equivalent models. This result indicates that those

with experience of autism, through work or social networks, score significantly lower (β=-

8.4, p<.01, lower confidence interval -14, upper confidence interval -2.5) on self-assessed

knowledge than the autism in family or self, reference category group. There is around an

eight-point difference between the categories. Those with no experience of people with

autism score lower again, around a 20 (p<.001, lower confidence interval -26, upper

confidence interval -14) point difference between this category and the reference category.

This model also provides the most explanatory power (F(6, 305) = .13 p<0.00) all other

𝑟

models are more limited in their explanatory power.

Overall sex and belief autism is increasing are significantly associated with the AAS scale.

The level of relationship to a person with autism is significantly associated with self-assessed

knowledge of autism. Self-assessed knowledge of autism, however, has no association with

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actual knowledge of autism as measured by the AAS scale. This is evident in both modelling

(β=.005 p=.7) and the very small correlation between the variables.

Discussion

These data make a new contribution to the field by offering a useful baseline assessment of

knowledge of autism from among the largest sample of trainee teachers in the UK using a

validated instrument. Although our study was limited to just one School of Education at one

institution in England, our findings indicate that trainee teachers do not suffer from a gap in

knowledge about the basic characteristics of autism. This is important as it contrasts with the

majority of other comparable research in the field among trainee teachers and educational

professionals (Able et al. 2015; Busby et al., 2012; Haimour and Obaidat, 2013; Helps et al.,

1999; Jung et al., 2011; Mavropoulou and Padeliadu, 2000; McConkey and Bhlirgri, 2003;

Segall, 2008; Segall and Campbell, 2012).

Our findings more closely align with those of Talib and Paulson (2015) and Blackwell et al.

(2017) which also report accurate beliefs about the basic characteristics of autism among

teacher education students. Given that the majority of literature reporting gaps in autism

knowledge using validated instruments were published before 2015, it could be inferred that

trainee teachers and educational practitioners are becoming more informed about autism. This

could be as a result of greater exposure to information through social media and the

accessibility of mobile technology (Aboulkacem and Haas, 2018; Mitchell et al., 2016). That

notwithstanding, these studies appear to indicate a positive trajectory.

As knowledge of autism is a predictor of teachers’ ability and willingness to provide

inclusive learning opportunities to autistic students (Baglieri & Shapiro, 2012; Busby et al.,

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2012; Segall and Campbell, 2012), the positive AAS scores from our sample ought to be

welcomed as a sign that the teachers of tomorrow understand some of the basic diagnostic

information related to autism and its implications for children, families and learning. This is

particularly relevant where higher levels of knowledge might lead to better access to early

screening, accurate identification and early referral of children for a diagnostic assessment

and specific intervention (Dillenburger et al., 2016) alongside the inclusion and success of

autistic pupils in mainstream schools (Keen, Webster & Ridley, 2016; Kurth and

Mastergeorge, 2010). However, instruments such as the AAS do not tell the whole story and

neglect educational professionals’ knowledge of practical strategies and the lived experiences

of autistic people and their families (APPAG, 2017). This was reflected most acutely in

relation to the data related to those with direct connection with autism.

Degrees of contact with a person with autism significantly predicted higher levels of self-

assessed knowledge of autism. Interestingly, this was not also associated with correct

knowledge of autism, based on AAS. It seems, therefore, that those who have more contact

with autism tend to feel more confident but somewhat remarkably do not ‘know’ more than

others. Similar trends are also reported by Gillespie-Lynch et al. (2017) where those with an

autistic family member did not differ significantly in their answers to others with less

relational contact with autism. As per their study and others, we could also conclude, that the

self-assessed knowledge of autism they feel confident of might be derived more from their

lived experiences rather than a formal set of diagnostic criteria (Guldberg, 2017a; Guldberg et

al, 2017b). In this sense there appears to be a gap between knowledge and understanding,

where the former can be assessed by instruments such as the AAS but the latter relates more

sensitively to the practical everyday experience of autism. Such a proposition has

implications for the content validity of autism knowledge scales, which are largely based on

the American Psychiatric Association’s medical criteria for autism as set out in the

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Diagnostic Statistical Manual (Harrison et al, 2017). Further research into knowledge of

autism among different populations ought to consider broadening ‘knowledge’ to comprise a

wider range of perspectives, including those of autistic people and their families, who

typically give greater emphasis to situational knowledge and tend to describe it in terms of

diversity over pathology (Gillespie-Lynch et al., 2017; Author 1 et al, 2017). It seems

reasonable to suggest that doing so could enable us to learn more about what professionals

and practitioners know and the training they require to better meet the needs of autistic

children in schools (APPGA, 2017; Guldberg, 2017a).

Although teachers scored highly in their knowledge of autism they tended to under-estimate

their knowledge, self-assessing as having a far lower levels of knowledge than they actually

demonstrate. This resonates with other findings in the literature. For example, Segall’s

(2008) study suggests that general education teachers tended to profess a lack of knowledge,

rather than an endorsement of incorrect knowledge based on a validated autism knowledge

scale. Other research indicates that even when trainee teachers’ knowledge of autism was

largely correct this did not always translate into feelings of competence in supporting autistic

students’ learning in schools (Blackwell et al, 2017; Busby et al., 2012; Jung et al, 2011;

Talib and Paulson, 2015). One explanation for this seeming lack of confidence in knowledge

may be related to the Dunning–Kruger effect (Dunning, 2011, Kruger and Dunning, 1999). It

shows that top performers are frequently inaccurate in self-assessing their knowledge and

typically display underestimation. Ehrlinger et al.’s study (2008) reported that top performers

misestimated their performances against their objective performances by roughly 6 percentile

points and Schlösser et al. (2013) report underestimations of performance by roughly 10–14

percentile points. It could thus be argued that our sample of trainee teachers, precisely

because of their high levels of awareness of autism, have underestimated their actual

knowledge.

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Finally, it was pleasing that our data based on the AAS replicated that of Cage, Di Monaco, and

Newell (2018) and Tipton and Blacher (2014) with respect to interventions for autistic children.

The top answer shared by all three studies was the (incorrect) statement that ‘there is one

intervention that works for all children with autism’. This suggests that whilst trainee

teachers, in our case, might not necessarily feel confident about how to support autistic

children in their classrooms, 96% of them recognise the need to treat each child according to

their individual challenges, needs, talents, and skills. As with other studies (Blackwell et al,

2017; Busby et al., 2012; Jung et al, 2011; Guldberg 2017a; Talib and Paulson, 2015), our

findings confirm the requirement in the UK to extend resources including autism training

provision for trainee teachers in order to further develop their knowledge of autism but, more

importantly, enable them to best support and develop their autistic pupils.

Limitations and future research

There are obvious limits to what can be claimed based on this research. Firstly, whilst our

study draws on a reasonably large sample of trainee teachers in the UK, it was conducted at

just one university in England. In order to gain a clearer view of just what this group know

about autism future research could replicate this study in other teacher training institutions to

gain a more representative sample. Secondly, whilst Tipton and Blacher’s (2014) AAS

instrument has been validated and is recognised as being a relatively robust measure of

knowledge of autism derived from the wider autism literature and particularly the DSM-5

(2013), the content is open to critique and subject to change (Volkmar and McPartland,

2014). There are other instruments that could have been used (see Harrison et al, 2017b)

which may reflect more up-to-date knowledge. Moreover, the AAS and instruments like it

reflect only medical knowledge of autism and as Gillespie-Lynch et al. (2017) suggest, these

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would benefit from being informed by autistic voices and experiences. Finally, whilst it was

useful to compare trainee teachers’ self-assessed autism knowledge (SAK) against the

validated knowledge of autism captured in the AAS, there is scope to develop enhanced

measures that offer a more comprehensive measure of this. Moreover, it would be useful for

future research to include additional items related to self-efficacy and sense of competence in

relation to their professional knowledge of autism and pedagogical practice with autistic

pupils.

Conclusions

This study offers the first baseline assessment of knowledge of autism using a validated

instrument from among the largest sample of trainee teachers in the UK. It demonstrates that

trainee teachers’ levels of knowledge about the basic characteristics of autism were high in

contrast to the majority of other comparable literature in the field (Able et al. 2015; Busby et

al., 2012; Haimour and Obaidat, 2013; Helps et al., 1999; Jung et al., 2011; Mavropoulou and

Padeliadu, 2000; McConkey and Bhlirgri, 2003; Segall, 2008; Segall and Campbell, 2012).

This is important as evidence suggests that knowledge of autism is a predictor of teachers’

positive attitudes towards inclusion of autistic students and the potential to recognise their

strengths (Busby et al., 2012; Russell et al., 2019).

We suggest that our findings, in conjunction with other more recent studies such as Talib and

Paulson (2015) and Blackwell et al. (2017), indicate a positive shift in autism awareness more

generally among trainee teachers, potentially based on the increase in mobile technology and

access to information through social media. However, whilst our findings are encouraging,

we acknowledge that instruments like the AAS only explore one avenue of knowledge and do

not reflect the professional and personal knowledge that many practitioners possess as a

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result of supporting the educational progress and development of autistic children and,

therefore, claims about levels of ‘knowledge’ ought to be viewed somewhat tentatively. On

this basis we make the case for a review of knowledge of autism scales and the development

of validated instruments which assess teachers’ pedagogical knowledge as well as knowledge

of autism derived from clinical descriptions.

Finally, our findings point to a lack of confidence and an underestimation of knowledge,

which indicate the need for additional resources and training to develop trainee teachers’ self-

efficacy and feelings of competence in their practice (Mundy, 2016). This is especially

important given the challenges that autistic children report in schools (APPGA, 2017) but

also with respect to the role that classroom teachers play in identifying learning needs and

providing opportunities for these pupils to succeed.

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Total words = 7862 including references, tables etc

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Table 1. Descriptive statistics of the variables of analysis

%(frequency)

Sex/gender

Men

15(46)

Women

85(266)

Age

18-20 years old

75(235)

21-30 years old

25(77)

Level of study

Undergraduate

94(293)

Postgraduate

6(19)

Is autism increasing

Yes

81(253)

19(59)

Level of contact with autism

I or a family member have autism

18(56)

Through work or social life

51(160)

No contact

31(96)

Autism Awareness Survey

AAS

Mean: 43 SD: 4.5

Self-assessed knowledge

SAK

Mean: 38 SD: 20

Source: Sample of trainee teachers in the School of Education at one university in England

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Table 2. Statements comprising the AAS scale by percentage of correct, incorrect and neutral

responses

Correct

Incorrect

Neutral

Autism is an emotional disorder (D)

22%

61%

17%

Vaccines are causing an increase in autism (D)

76%

21%

There is a cure for autism (D)

83%

11%

Autism runs in families (A)

48%

27%

25%

All children with autism display poor eye contact (D)

62%

24%

14%

Autism is diagnosed more frequently in males than in

females (A)

57%

13%

30%

Changing a child's diet will lessen the severity of autism

symptoms (D)

66%

25%

There is one intervention that works for all children

with autism (D)

96%

Children with autism can grow up to live independently

(A)

95%

Autism is a developmental disorder (A)

52%

18%

30%

Autism can be diagnosed as early as 18 months old (A)

55%

18%

27%

Children with autism are smarter than standardised

tests demonstrate (A)

58%

10%

32%

It is important that all children with autism receive

special education services (A)

71%

12%

17%

With the proper treatment, most children with autism

will eventually outgrow it (D)

81%

16%

D = correct response is a disagree response

A = correct response is an agree response

Source: Sample of trainee teachers in the School of Education at one university in England n=312

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Appendix

The appendix provides full bivariate associations and modelling results

Table a1, Bivariate analyses between key variables, relationship to a person with autism, sex, age and the Autism Awareness Survey (AAS)

and Self-assessed knowledge (SAK) outcomes

Mean AAS score

Mean SAK score

Relationship to person on Autism spectrum

I or a family member

43.8 (42.9, 44.8)

47.6

Work or social

43.2 (42.5, 43.9

39.9

None

41.8 (40.9, 42.7)

27.9

ANOVA: F(2, 309)=5 p=0.04, eta=.14

ANOVA: F(2, 309)=23 p<0.00, eta=.36

Sex

Male

41.9

37.15

Female

43.1

37.75

T-test: t(310)=1.8 p=0.07

T-test: t(310)=0.1 p=0.9

Age

18-20 years old

42.8

37.8

21-30 years old

43.4

37.6

T-test: t(310)=1.0 p=0.31

T-test: t(310)=0.06 p=0.96

Education level

Undergraduate

42.9

37.8

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Postgraduate

43.1

37.4

T-test: t(310)=0.1 p=0.91

T-test: t(310)=0.08 p=0.94

Is autism increasing?

Yes

42.6

36.9

44.6

41.4

T-test: t(310)= 3.14 p=0.002

T-test: t(310)=1.6 p=0.12

Source: Sample of trainee teachers in the School of Education at one university in England, n=312

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Table a2 presents several OLS models of the Autism Awareness Survey outcome. Model 1 is a base model which includes the variables age, sex and level of university study.

Model 2, also includes these variables along with the variable denoting relationship to a person with autism. Model 3, includes the demographic factors along with the self-

assessed knowledge of autism scale. Model 4, includes the demographic factors and whether people believe autism to be increasing. Model 5 includes all the variables

together.

1: Base model, Sex,

age,

level of study

2: Base model +

Level of contact

with autism

3: Base model +

Self-assessed

knowledge of autism

4: Base model +

Is autism

increasing

5: Full model

ci 95%

Sex

Male

Female

1.5*

(.75)

0047,3

1.3

(.75)

-.15,2.8

1.5*

(.75)

.00044,2.9

1.6*

(.74)

.17,3.1

1.5*

(.74)

.031,2.9

Age

18-20 years old

21-30 years old

.73

(.66)

-.57,2

.69

(.66)

-.61,2

.73

(.66)

-.57,2

(.65)

-.58,2

.67

(.65)

-.61,1.9

Level of study

Undergraduate

Post-graduate

.13

(1.2)

-2.3,2.5

.15

(1.2)

-2.2,2.5

.13

(1.2)

-2.2,2.5

.28

(1.2)

-2.1,2.6

.28

(1.2)

-2.1,2.6

I or family

member

Work/social

-.45

(.69)

-1.8,.91

-.25

(.69)

-1.6,1.1

Level of

contact

with autism

none

-1.9*

(.75)

-3.4,-

.41

-1.5

(.79)

-3.1,.061

SAK scale

.02

(.013)

-.0055,.045

.0055

(.014)

-.021,.032

Self-assessed

knowledge of

autism

Yes

Is autism

increasing?

2.1***

(.64)

.87,3.4

1.9**

(.64)

.68,3.2

Constant

41***

(.73)

40,43

42***

(.94)

41,44

41***

(.88)

39,42

41***

(.74)

40,42

42***

(1.2)

39,44

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𝑅

.015

.042

.023

.049

.071

(3, 308)=1.56

(5, 306)=2.66

(4, 307)=1.77

(4, 307)=3.98

(7, 304)=3.30

β: linear regression coefficients,

se: standard errors,

ci 95%: 95% confidence intervals

Source: Sample of trainee teachers in the School of Education at one university in England, n=312, *p<=.05, **p<=.01, ***p<=.001

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Table a3 presents several OLS models of the self-assessed knowledge scale outcome. Model 1 is a base model which includes the variables age, sex and level of university

study. Model 2, also includes these variables along with the variable denoting relationship to a person with autism. Model 3 includes the demographic factors and whether

people believe autism to be increasing. Model 4 includes all the variables together.

1: Base model, sex, age,

level of study

2: Base model + level of

contact with autism

3: Base model + Is

autism increasing

4: Full model

ci 95%

Sex

Male

Female

.38

(3.3)

-6.2,6.9

-1.5

(3.1)

-7.6,4.7

.68

(3.3)

-5.9,7.2

-1.3

(3.1)

-7.4,4.9

Age

18-20 years old

21-30 years old

.075

(2.9)

-5.7,5.9

-.38

(2.7)

-5.8,5

.019

(2.9)

-5.7,5.8

-.41

(2.7)

-5.8,5

Level of study

Undergraduate

Post-graduate

-.27

(5.4)

-11,10

.13

(5)

-9.7,10

.049

(5.4)

-10,11

.29

(5)

-9.6,10

I or family member

Work/social

-8.4**

(2.9)

-14,-2.7

-8.2**

(2.9)

-14,-2.5

Level of contact

with autism

none

-20***

(3.1)

-27,-14

-20***

(3.2)

-26,-14

Is autism

increasing?

Yes

4.5

(2.9)

-1.2,10

2.4

(2.7)

-3,7.7

Constant

37***

(3.3)

31,44

50***

(3.9)

42,57

36***

(3.3)

30,43

49***

(4.1)

41,57

𝑅

.000061

.13

.0079

.13

(3, 308)=0.01

(5, 306)=9.33

(4, 307)=0.61

(6, 305)=7.89

β: linear regression coefficients,

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se: standard errors,

ci 95%: 95% confidence intervals

Source: Sample of trainee teachers in the School of Education at one university in England, n=312, **p<=.01, ***p<=.001

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Table a5, contingency tables of the bivariate relationship between responses on variable of whether

autism is increasing and several analytic variables

In your view do you think autism is increasing in the UK

Yes

Level of contact with autism

I or a family member

73%

27%

Work/social

81%

19%

160

None

86%

14%

Chi-square(2)=4, p=0.13, Cramer’s V= 0.11

Sex

Male

76%

24%

Female

82%

18%

266

Table a4, contingency table of the bivariate relationship between responses on variable of

whether autism is increasing by whether people agree that vaccines are causing autism. This is

a distribution also examined by Tipton and Blacher (2014).

Vaccines are causing an increase in autism

Is autism increasing?

Disagree

Somewhat

disagree

Neutral

Somewhat

agree

Yes

51%

22%

23%

253

80%

10%

Chi-square(3)=17, p=0.001, Cramer’s V= 0.23, similar result with Fishers exact test

Source: Sample of trainee teachers in the School of Education at one university in England

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Chi-square(1)=1, p=0.34, Cramer’s V= 0.05

Age

18-20 years old

81%

19%

235

21-30 years old

81%

19%

Chi-square(1)=0.0, p=0.9, Cramer’s V= 0.0

Source: Sample of trainee teachers in the School of Education at one university in England n=312

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Reviewer(s)' Comments to Author:

We thank the reviewers for the time and effort put into making this a stronger research

paper. The meticulous comments and constructive advice offered throughout this

process have served to enhance the overall argument and important findings for teacher

education.

Referee: 1

Comments to the Author

I have read through the revised paper.

The specific revisions made to my original review have been addressed. Though I would

have preferred to see specific details of what text related to my points by reference to page

and line numbers, I could identify where changes recorded in the accompanying letter were

made in the text. I would also have preferred to not see the deleted text and juts new text.

But, what matters is that the key points I raised in the initial review have been addressed

satisfactorily. These relate to the distinction between teacher content and pedagogic

knowledge in relation to autism, the surprising elements in findings have been commented on

in the discussion, the limitations of the AAS measure and other kinds of teacher knowledge

that are important for teachers.

I have noted other revisions which make sense to me given other reviewer's comments. But

that is for this reviewer to comment on

I noted that on page 22 the DSM scheme was said to be from the Am. Psychological

Association; this should be Psychiatric.

This has been addressed and changed.

I support this being published

Referee: 2

Comments to the Author

Very many thanks to the authors for responding so constructively and fully to the feedback

received. This is a substantially improved paper as a result of the changes and I am sure that

the authors must feel that too. I think there are a few points where clarification and detail are

needed but these are minor points to improve overall transparency and readability:

The title - I would suggest a change to the title to reflect the updated argument and content. I

am not sure this point is really central to the paper anymore and so the title could be clearer.

We have altered the title to Trainee teachers’ knowledge of autism: implications for

understanding and inclusive practice, which we feel better reflects the main emphasis

and points raised in the discussion / conclusions.

In the abstract, I would recommend 'draw on data from a large sample' [you make the point

about the largest so far in the text and that's fine]

This has been addressed and changed.

p. 4 - sentence that begins Russell et al (2019) - the subject of the sentence (they) shifts

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across this sentence, making it more difficult to follow. Just check and tweak that the subject

is clear throughout.

We have aimed to clarify that the subject of the sentence is a strengths-based reading of

autism.

p. 7 research questions - RQ1 I would suggest a slight rephrase as follows: What is the

overall level of knowledge about autism among trainee teachers in one Higher Education

Institution in England?

RQ2 - suggest spelling out the AAS in full here as it's not been introduced in the text yet

RQ3 - suggest a slight rephrase: Are demographics (....) associated with self-assessed

knowledge of autism and responses to the Autism Awareness Survey

Agree. These have been addressed and changed.

p. 12 I think the point needs to be completed about why the AAS was used. So you've

introduced the AAS, said what it's limitations are [Give the AAS's focus...] but then you need

to add a sentence that says why you are using it anyway e.g. Nevertheless, the AAS was used

for this study because...

Agree. These have been addressed and changed.

p. 12 second para, the brackets around (AAS) can be removed

This has been addressed and changed.

p. 12 - please state the exact wording of the SAK question

This has been added

p. 13 - it does not seem valid to claim a score of 50 would represent the average population

level of knowledge about autism, unless you have some clear evidence to support this

statement. You could only claim this if you know the scores to questions like this tend to be

normally distributed (which I very much doubt). This statement needs to be removed or fully

justified with reference to evidence.

This is a fair point and has been removed.

p. 13 'This was considered....' - this sentence includes a repetition of increase / increasingly -

a slight rephrase needed

This has been addressed and changed.

p. 15 - should be '...results are presented as descriptive and bivariate analyses...'

This has been addressed and changed.

p. 16 - Please give the numbers and percentages for responses to the two most correct and

incorrect statements on the AAS.

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These have been addressed and changed.

p. 16 - you state here that 'Respondents were asked whether they thought the prevalence of

autism is increasing' - were they asked this specifically? Or were they asked the more general

wording as shown in your tables. Please clarify this and then be consistent throughout about

how you phrase and refer to this question.

This has been addressed and changed.

p. 16 should be '...those who identify autism as increasing...'

This has been addressed and changed.

p. 20 - do you really want to cite Dillenburger et al here in support for a specific intervention?

Are you advocating that ABA approaches should be rolled out for all children then? This is

highly controversial of course and if this is not what you are advocating then I would strongly

recommend changing this reference.

We understand the point regarding the suggested support of ABA as an effective

intervention. I don’t happen to agree with Dillenburger on this matter, however, the

study referred to is also useful as a large population which suggests that more

knowledge can lead to better outcomes for autistic children and young people, hence we

have retained it as a reference. I have removed the final clause “and specific

intervention” though.

pp.25-26 I would like to see a few sentences added to the limitations that fully acknowledges

the limitations of asking only one question about subjective knowledge and using a 0-100

scale for doing this. This is very limited in terms of what it can really tell us and this needs to

be fully acknowledged here.

We have aimed to add some additional detail about this limitation although, as per

revisions submitted previously, we suggest that single-item questions are not necessarily

to be avoided (Gogol et al., 2014; Williams and Payne, 2008).

Table a2 - you refer to the SAK scale here but in the main text you say SAK measure. The

latter is better because it's not really a scale at all, being based only on one question. I

recommend making the phrasing consistent here and using 'measure' throughout when you

talk about the SAK.

This has been addressed and changed.

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Jun 2017

Carole Buckley

Autism spectrum disorder is a complex life-long neurodevelopmental disorder that affects 1.1% of the UK population. It is a social communication disorder that is characterised by rigid and repetitive routines or behaviours. It is also commonly associated with difficulties in cognition, behavioural flexibility, sensory processing, emotional regulation, and altered sensory sensitivity.

Classroom Displays-Attraction or Distraction? Evidence of Impact on Attention and Learning From Children With and Without Autism

Article

May 2017
DEV PSYCHOL

Paying attention is a critical first step toward learning. For children in primary school classrooms there can be many things to attend to other than the focus of a lesson, such as visual displays on classroom walls. The aim of this study was to use eye-tracking techniques to explore the impact of visual displays on attention and learning for children. Critically, we explored these issues for children developing typically and for children with autism spectrum disorder (ASD). Both groups of children watched videos of a teacher delivering classroom activities-2 of "story-time" and 2 mini lessons. Half of the videos each child saw contained high levels of classroom visual displays in the background (high visual display [HVD]) and half had none (no visual display [NVD]). Children completed worksheets after the mini lessons to measure learning. During viewing of all videos children's eye movements were recorded. The presence of visual displays had a significant impact on attention for all children, but to a greater extent for children with ASD. Visual displays also had an impact on learning from the mini lessons, whereby children had poorer learning scores in the HVD compared with the NVD lesson. Individual differences in age, verbal, nonverbal, and attention abilities were important predictors of learning, but time spent attending the visual displays in HVD was the most important predictor. This novel and timely investigation has implications for the use of classroom visual displays for all children, but particularly for children with ASD. (PsycINFO Database Record

Evidence-based practice in autism educational research: can we bridge the research and practice gap?

Article

Nov 2016

Karen Guldberg

In order to develop deeper and better understandings of what constitutes effective educational practices, and to bridge the gap between research and practice, there is a need for a paradigm shift in autism educational research. The contribution of this paper is to examine the key methodological challenges that stand in the way of autism educational research impacting on practice. This research field is dominated by experimental research designs that evaluate the impact of ‘interventions’ that focus on developing the skills, knowledge, and understanding of pupils with autism. For educational research to have an impact on the lives of individuals with autism, their families, and the practitioners who work with them, movement towards a more balanced range of methodologies is needed. This needs to include methodologies that situate the knowledge base of practitioners on a par with the knowledge base of researchers, drawing on the evidence base from the classroom itself, and bringing in the perspectives and views of individuals with autism, their families, and the practitioners who work with them.

An international review of autism knowledge assessment measures

Article

May 2016
AUTISM

Autism spectrum disorder-specific knowledge deficits contribute to current disparities in the timing and quality of autism spectrum disorder services throughout the United States and globally. This study conducted a systematic review of Western and International literature to examine measures used to assess autism spectrum disorder knowledge. This review identified 44 unique autism spectrum disorder knowledge measures across 67 studies conducted in 21 countries. Measures used in each study were evaluated in terms of psychometric strength. Of the 67 studies reviewed, only 7% were rated as using a measure with strong psychometric support compared to 45% that were rated as using a measure with no reported psychometric support. Additionally, we examined content overlap and subdomains of autism spectrum disorder knowledge assessed (e.g. etiology, symptoms) and cross-cultural adaptation procedures utilized in the field. Based on these findings, the need for a cross-culturally valid and psychometrically sound measure of autism spectrum disorder knowledge is discussed and recommendations for improving current assessment methods are presented, including suggestions for measure subdomains.

Trainee teachers’ knowledge of autism: implications for understanding and inclusive practice

Abstract

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