High School Math and Science Preparation and Postsecondary STEM Pathways for Students with Autism Spectrum Disorder

Jose Blackorby & Jennifer Yu

Capacity Building InstituteDO-IT, University of WashingtonSeattle, WA

February 25-28, 2014

2

Overview

Background: Autism Spectrum Disorder (ASD) and STEM

Research questions

Data and Measures

Analysis

Results

Discussion

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The Stereotype: ASD = Good at Science & Math

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5

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Individuals with ASD Gravitate Toward STEM Fields

Elevated prevalence of relatives in STEM-related careers (Baron-Cohen, Wheelwright, Stott, Bolton, & Goodyer, 1997; Baron-Cohen et al., 1998; Jarrold & Routh, 1998; Wheelwright & Baron-Cohen, 2001).

Higher prevalence of ASD among mathematics majors compared with students in medicine, law, or social science at a UK university (Baron-Cohen et al.,

2007).

Higher proportion of students declaring a major in a STEM field (34%) than students with other disabilities or students in the general population (Wei et al., 2012).

– Lowest 2- or 4- year college attendance rates (32%)

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Reasons for the Link Between ASD and STEM

Theories suggest that individuals with an ASD are “systemizers”, i.e., analyze or construct rule-based systems to explain their world (Baron-Cohen, 2006).

Systemizing requires the same skills necessary to perform successfully in many STEM occupations (Baron-Cohen et al., 2007).

Early identification and early interventions provide students with ASD greater opportunities to pursue STEM careers that require postsecondary degrees (Hart, Grigal, & Weir, 2010).

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High School Math and Science Preparation Factors

No studies have considered any high school factors that may contribute to postsecondary STEM participation among students with ASD.

In the general education literature:

– Taking advanced math and science courses, such as AP math and science classes, Chemistry II, Physics II, and Calculus (Tai et al., 2006; Wai et al., 2010; Tyson et al., 2007).

– Strong performance on standardized tests (Bonous-Hammarth, 2000; Sahin et al., 2012).

– High school GPA (French et al., 2005; Zhang et al., 2004).

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Challenges Facing College-Aged Students with ASD

Despite an innate tendency toward STEM, students with ASD face numerous challenges to pursuing postsecondary STEM degrees (Hendricks & Wehman, 2009).

Characteristics of autism that may limit college success (Banda & Kubina, 2010; Donaldson & Zagler, 2010; Hart, Grigal, & Weir, 2010)

– Poor nonverbal communication skills

– Limited understanding of the rules of social behavior

– Noncompliance with academic tasks

Stress of a college environment (Dutton, 2008; VanBergeijk, Klin & Volkmar, 2008)

– Change in routine and consistency

– Increased academic courseload

– Professors inexperienced in dealing with autism

– Need to advocate for themselves

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Research Questions

What is the relationship between high school STEM preparation and STEM college enrollment among students with ASD?

What are the pathways to postsecondary STEM majors vs. non-STEM majors among college students with ASD?

What are the persistence rates among STEM majors vs. non-STEM majors following different pathways?

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Data Sources

National Longitudinal Transition Study-2 (NLTS2).

Collected five waves of data over 9 years for an initial sample of more than 11,000 13- through 16-year-olds in 12 federally defined disability categories.

National representativeness of the data set.

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Participants

Students with autism in NLTS2

This analysis uses

– postsecondary data from wave 5 parent and young adult telephone interviews and mail surveys collected in 2009

– high school transcript data collected from high school from 2002 to 2009,

– wave 1 parent survey

– wave 1 or wave 2 student direct assessments.

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Measures - Outcomes

College PersistenceParents and young adults were asked whether they remained in, graduated, or completed a degree from a 2-year or 4-year college

College MajorParents and young adults were asked about their course of study at postsecondary schools STEM major

•Computer science, programming, information technology•Engineering, electrical, mechanical, chemical •Mathematics and statistics•Science, biology, earth science, geology, physics, chemistry, environmental science

Non-STEM major•Social sciences (history, political science, economics, sociology, psychology, humanities, public policy, philosophy, religion, urban studies, women’s studies, American studies, ethnic studies, international relations, and social sciences)•Health, health care, medical•Others

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Measures - Predictors

General education inclusion - TranscriptPercent of units earned in general education settings from the high school transcript data

High school math and science coursework - Transcript Three levels of math class (Newman et al., 2011)

•Basic: general, basic, consumer, integrated, remedial math, and pre-algebra•Mid-level: algebra I, algebra II, and geometry •Advanced: trigonometry, pre-calculus, statistics and probability, and calculus

Two levels of science class•Basic: life skills, environmental, earth, geology, physical, astronomy, marine, aerospace, biology, anatomy, and physiology •Advanced: chemistry, physics, and integrated physics and chemistry

GPA in math and science - Transcript

High school math and science standardized test scores measured by WJ III (reliabilities range from 0.76 to 0.93) – wave 1 or 2 direct assessmentBackground characteristics variables: gender, age, race, family income, and conversation abilities – Wave 1 parent interview

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Analysis

Descriptive statistics (Weighted % or mean)

Weighted multiple logistic regression

SAS PROC SURVEY

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Results – Demographic Difference

Measures STEM Major Non-STEM MajorMale 97.30*** 79.40Black 11.79 16.40Hispanic 3.05 na

White 85.16 81.19Age at wave 5 23.61***

(0.20)23.12(0.23)

Income    <$25,000 5.03 10.07$25,001-50,000 26.23 18.20$50,001-75,000 33.52 29.05>$75,000 35.21 42.68

Conversation ability    No trouble 21.68 19.48Little trouble 71.31 50.27Lots of trouble or cannot converse at

all7.00*** 30.25

Unweighted N 40 110Weighted N 1,078 2,064

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Results – Logistic Regression Model to Predict Majoring STEM in College

Predictors College STEM Major

Male 3.01 [0.78, 11.67]

White 5.84** [1.54, 22.23]

Age at wave 5 2.40** [1.26, 4.57]

Family income 0.90 [0.69, 1.16]

No or little trouble conversing 15.08*** [4.46, 50.99]

Percent of units earned in GE 0.94 [0.88, 1.00]

Had mid-level math classes in GE 0.43 [0.10, 1.91]

Had advanced math classes in GE 4.08* [1.31, 12.68]

Had advanced science classes in GE 1.05 [0.21, 5.31]

Math GPA in GE 0.45 [0.14, 1.43]

Science GPA in GE 2.49 [0.72, 8.63]

WJ III Calculation 1.02 [0.98, 1.07]

WJ III Applied Problems 1.02 [0.96, 1.14]

WJ III Science 0.98 [0.93, 1.04]

Unweighted N 100

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Postsecondary Pathways

All majors STEM majors Non-STEM majors0%

10%

20%

30%

40%

50%

60%

2-year only Both 2-year and 4-year 4-year only

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Persistence Rates Among 2-year Community College Attendees

All majors STEM majors Non-STEM majors0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

Persisters Non-Persisters

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Results – Logistic Regression Model to Predict College Persistence

Predictors

Persist in collegeOR [CI]

Persist in STEM majorOR [CI]

Persist in non-STEM majorOR [CI]

Age0.99

[0.67, 1.49]1.94

[0.66, 5.68]0.91

[0.59, 1.41]

Male4.98**

[1.56, 15.89]16.29***

[3.56, 74.49]3.08

[0.90, 10.51]

Minority (Black, Hispanic, or other)

2.54*[1.00, 6.42]

21.04*[2.00, 21.66]

2.29[0.88, 5.93]

Annual household income0.96

[0.85, 1.08]1.02

[0.77, 1.36]0.96

[0.83, 1.11]

Parents attended postsecondary education

14.00*[2.10, 93.16]

27.07[0.55, 93.16]

10.38*[1.14, 76.13]

Conversation ability1.75

[0.98, 3.13]0.50

[0.04, 6.93]

1.94*[1.11, 3.38]

Postsecondary pathway

2-year community college0.04***

[0.01, 0.16]0.03*

[0.006, 0.19]0.03***

[0.005,0.15]

4-year university0.05**

[0.02, 0.18]0.01*

[0.003, 0.06]0.04*

[0.01, 0.19]

STEM 3.78*

[1.59, 9.01] NA NA

* p < .05; ** p < .01; *** p < .001.

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Discussion: High School Preparation

Although STEM majors with autism had a lower proportion of classes taken in general education settings than their peers with non-STEM majors, a much higher proportion of STEM majors took advanced math courses in general education settings than their peers with non-STEM college majors (42% vs. 22%).

This study emphasizes the importance of advanced math course-taking in an inclusive high school setting for students with ASD.

High school counselors and teachers should encourage more students with ASD to take challenging math courses.

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Discussion: High School Preparation

Conversation ability was significantly correlated with STEM majoring in college.

Conversation ability has the potential to be influenced through effective educational interventions and supports from both high school and college.

– High school transition planning should focus on communication support.

– Colleges should provide communication and social skills support in order for students with ASD to succeed in STEM fields.

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Discussion: Postsecondary Pathways

Students with ASD were more likely to persist in a 2-year community college, and further pursue higher level degrees in STEM.

Community colleges play an important role in increasing STEM participation rates and persistence rates for college STEM majors with ASD.

Future studies are needed to explore subgroups of students with ASD that are non-persisters (e.g., females, advantaged students) to understand why they do not persist and, ultimately, to improve college persistence rates among all students with ASD.

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Funding Acknowledgement

This work was supported by funding from the National Science Foundation (HRD-1130088), Institute of Education Sciences (R324A120012), the National Institute of Mental Health (R01 MH086489), and Autism Speaks.