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High School Math and Science Preparation and Postsecondary STEM Pathways for Students with Autism Spectrum Disorder Jose Blackorby & Jennifer Yu Capacity.

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Presentation on theme: "High School Math and Science Preparation and Postsecondary STEM Pathways for Students with Autism Spectrum Disorder Jose Blackorby & Jennifer Yu Capacity."— Presentation transcript:

1 High School Math and Science Preparation and Postsecondary STEM Pathways for Students with Autism Spectrum Disorder Jose Blackorby & Jennifer Yu Capacity Building Institute DO-IT, University of Washington Seattle, WA February 25-28, 2014

2 Overview 2  Background: Autism Spectrum Disorder (ASD) and STEM  Research questions  Data and Measures  Analysis  Results  Discussion

3 The Stereotype: ASD = Good at Science & Math 3

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6 Individuals with ASD Gravitate Toward STEM Fields 6  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%)

7 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). 7

8 High School Math and Science Preparation Factors 8  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).

9 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 9

10 Research Questions 10  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?

11 Data Sources 11  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.

12 Participants 12  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.

13 Measures - Outcomes 13 College Persistence Parents and young adults were asked whether they remained in, graduated, or completed a degree from a 2-year or 4-year college College Major Parents 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

14 Measures - Predictors 14 General education inclusion - Transcript Percent 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 assessment Background characteristics variables: gender, age, race, family income, and conversation abilities – Wave 1 parent interview

15 Analysis  Descriptive statistics (Weighted % or mean)  Weighted multiple logistic regression  SAS PROC SURVEY 15

16 Results – Demographic Difference 16 Measures STEM MajorNon-STEM Major Male97.30***79.40 Black11.7916.40 Hispanic3.05 na White85.1681.19 Age at wave 523.61*** (0.20) 23.12 (0.23) Income <$25,0005.0310.07 $25,001-50,00026.2318.20 $50,001-75,00033.5229.05 >$75,00035.2142.68 Conversation ability No trouble21.6819.48 Little trouble71.3150.27 Lots of trouble or cannot converse at all 7.00***30.25 Unweighted N40110 Weighted N1,0782,064

17 Results – Logistic Regression Model to Predict Majoring STEM in College 17 Predictors College STEM Major Male 3.01 [0.78, 11.67] White 5.84** [1.54, 22.23] Age at wave 52.40** [1.26, 4.57] Family income0.90 [0.69, 1.16] No or little trouble conversing15.08*** [4.46, 50.99] Percent of units earned in GE0.94 [0.88, 1.00] Had mid-level math classes in GE0.43 [0.10, 1.91] Had advanced math classes in GE4.08* [1.31, 12.68] Had advanced science classes in GE1.05 [0.21, 5.31] Math GPA in GE0.45 [0.14, 1.43] Science GPA in GE2.49 [0.72, 8.63] WJ III Calculation1.02 [0.98, 1.07] WJ III Applied Problems1.02 [0.96, 1.14] WJ III Science0.98 [0.93, 1.04] Unweighted N100

18 Postsecondary Pathways 18

19 Persistence Rates Among 2-year Community College Attendees 19

20 Results – Logistic Regression Model to Predict College Persistence Predictors Persist in college OR [CI] Persist in STEM major OR [CI] Persist in non-STEM major OR [CI] Age 0.99 [0.67, 1.49] 1.94 [0.66, 5.68] 0.91 [0.59, 1.41] Male 4.98** [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 income 0.96 [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 ability 1.75 [0.98, 3.13] 0.50 [0.04, 6.93] 1.94* [1.11, 3.38] Postsecondary pathway 2-year community college 0.04*** [0.01, 0.16] 0.03* [0.006, 0.19] 0.03*** [0.005,0.15] 4-year university 0.05** [0.02, 0.18] 0.01* [0.003, 0.06] 0.04* [0.01, 0.19] STEM 3.78* [1.59, 9.01] NA 20 * p <.05; ** p <.01; *** p <.001.

21 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. 21

22 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. 22

23 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. 23

24 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. 24

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