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Self-regulated learning (SRL) is a framework for understanding how individuals purposefully initiate and implement self-directed processes, such as time management and help-seeking, in learning environments. SRL encompasses three primary dimensions (cognition, motivation/affect, observable behavior) and is best understood by examining three phases of self-regulation (forethought, performance, self-reflection). Our focus on understanding the general dimensions of SRL among autistic students makes Zimmerman’s three-part framework and emphasis on principle and long-standing concepts appropriate.
In this project, we explore the relationship between GIST career preparation and self-regulation among autistic high school students using a complementary, mixed-methods research design (Greene, 2007). Participants were selected by engaging school and community leaders who work with autistic students and their families. There was a particular focus on area public schools in two local school districts to increase the likelihood of a more diverse sample, particularly socioeconomic diversity, level of support needs, and race/ethnicity. Seventeen participants were recruited and enrolled in Cohort 1 of this study.
The four research questions are as follows:
RQ1: How do autistic high school students engage in self-regulation during online GIST instruction?
RQ2: What supports are needed during online and hybrid GIST instruction to build and sustain self-regulation among autistic high school students?
To examine the ways in which our participants engaged in self-regulation during online GIST instruction, and to identify the supports that are needed during online and hybrid GIST instruction to build and sustain self-regulation among autistic students, we conducted qualitative observations and semi-structured interviews. Following data collection, the interviews were transcribed verbatim, and assessed for accuracy. We are currently completing data analyses of observations and interviews using a constant comparative method, and we expect data analysis to be complete by November, 2022.
RQ3: How does hybrid GIST instruction influence career interest and awareness in GIST among autistic high school students?
RQ4: How does hybrid GIST instruction influence motivation for learning among autistic high school students?
To examine the extent to which GIST instruction influences career interest and awareness regarding GIST among autistic high school students, we administered the Monitoring the Impact of STEM Outreach (MISO) Student Attitudes Towards STEM (S-STEM) Survey. To examine the ways in which hybrid GIST instruction influences motivation for learning among autistic high school students, we administered the Motivated Strategies for Learning Questionnaire (MSLQ). We also conducted semi-structured interviews to explore student attitudes and motivation. The pre/post survey was administered prior to the first GIST session of Year 1, and again following the completion of the summer session (i.e., at the end of Year 1) for the first cohort of participants. All data are currently being analyzed and expected to be complete by November 2022. The survey data will be analyzed via descriptive statistics to describe demographics and explore preliminary findings. Given the small sample size, we employed nonparametric statistics (i.e, Wilcoxon) to discern within-group differences before and after the GIST project activities.
Data collection is complete for year one of Cohort 1. Data analyses are ongoing and expected to be complete by November 2022. Our preliminary analyses indicate (a) specific strategies for self-regulation skills among program participants, (b) barriers and facilitators to support student self-regulation during GIST instruction, and (c) positive preliminary outcomes related to motivation and STEM career interests for GIST participants.
U.S. business and policy leaders have made it a priority to increase the number of students who pursue STEM careers. Yet, one source of STEM talent is often overlooked: young autistic people. Autistic adolescents are a particularly underserved group within an underrepresented group; that is, people with disabilities. Autistic teens are less likely to attend college or find work than their peers. However, when autistic students defy the odds and enter collegiate degree programs, STEM disciplines emerge as preferred areas of study. Wei and colleagues’ analysis of a nationally-representative data set for autistic students revealed that 34.3% of autistic college students selected STEM majors, with 12% picking science and 16% entering computer science. Notably, movement into STEM majors is higher among autistic college students (34.3%) than for the general population (22.8%), including for science and computer science sub-disciplines. Thus, autistic students are less likely to attend college than their non-disabled peers, but when they do enter a postsecondary degree program, they are more likely to earn STEM workforce credentials and enter corresponding careers. Therefore, restructuring learning to foreground evidence-based practices such as modeling, prompting, task analysis, and self-regulation, in combination with exposure to innovative technologies (mini drones, simulation software, mapping software) will support autistic youth to be motivated, inspired, and prepared to continue along STEM education and career paths. Further, our scholarship will advance the research frontier by facilitating insight into theoretical constructs around self-regulation and connecting conceptual ideas to best practices in STEM education.
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