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IntroductionRecent efforts including the U.S. Department of Education’sRaise the Bar: STEM Excellence for All Students, designed to strengthen Science, Technology, Engineering and Mathematics (STEM) education, typify the development of effective outreach programs implemented in high school settings to increase STEM achievement and literacy and to promote future careers in STEM. Specifically, artificial intelligence (AI) and machine learning (ML) are topics of great importance and interest but are often reserved for higher-level education. Introductions of complex subjects in high school promotes student efficacy, enthusiasm, and skill-development for STEM careers. Establishing strong partnerships between universities and high schools is mutually beneficial for the professional development of students, teachers, and professors. In this paper, we detail immersive outreach efforts and their effectiveness in a high school setting. MethodsFrom Spring 2021 to Spring 2024, we conducted eight data-science and analysis-coding style workshops along with two data science units, with 302 students participating in the data science workshops and 82 students in the data science units. All students who participated in the data science lessons completed a comprehensive final project. Surveys measuring knowledge and appeal to data science and coding were conducted both retrospectively and prospectively, before and after each workshop and the data science units. A 1 year follow up survey was conducted for students in the 2023 data science lessons (n= 23). ResultsOverall, average student interest significantly increased from 2.72 ± 1.08/5.0 (n= 205) to 3.15 ± 1.18/5.0 (n= 181,p= 0.001) during the data science workshops, while 70% of students expressed desire to continue with coding. Interest modestly increased in the data science lessons from 3.15 ± 0.65/4.0 to 3.17 ± 0.77/4.0 (n= 82,p= 0.8571), while knowledge significantly increased from 64.16% to 88.5% (% correct out of six questions) in the 2023 data science lessons and from 52.62% to 60.79% (% correct out of 29 questions) in the 2024 data science lessons. DiscussionIncreasing STEM exposure through outreach programs and a modified curriculum can positively alter students’ career trajectory and prepare them for the evolving technologically advanced world and the careers within it. 

There is a critical need for more students with engineering and computer science majors to enter into, persist in, and graduate from four-year postsecondary institutions. Increasing the diversity of the workforce by inclusive practices in engineering and science is also a profound identified need. According to national statistics, the largest groups of underrepresented minority students in engineering and science attend U.S. public higher education institutions. Most often, a large proportion of these students come to colleges and universities with unique challenges and needs, and are more likely to be first in their family to attend college. In response to these needs, engineering education researchers and practitioners have developed, implemented and assessed interventions to provide support and help students succeed in college, particularly in their first year. These interventions typically target relatively small cohorts of students and can be managed by a small number of faculty and staff. In this paper, we report on “work in progress” research in a large-scale, first-year engineering and computer science intervention program at a public, comprehensive university using multivariate comparative statistical approaches. Large-scale intervention programs are especially relevant to minority serving institutions that prepare growing numbers of students who are first in their family to attend college and who are also under-resourced, financially. These students most often encounter academic difficulties and come to higher education with challenging experiences and backgrounds. Our studied first-year intervention program, first piloted in 2015, is now in its 5th year of implementation. Its intervention components include: (a) first-year block schedules, (b) project-based introductory engineering and computer science courses, (c) an introduction to mechanics course, which provides students with the foundation needed to succeed in a traditional physics sequence, and (d) peer-led supplemental instruction workshops for calculus, physics and chemistry courses. This intervention study responds to three research questions: (1) What role does the first-year intervention’s components play in students’ persistence in engineering and computer science majors across undergraduate program years? (2) What role do particular pedagogical and cocurricular support structures play in students’ successes? And (3) What role do various student socio-demographic and experiential factors play in the effectiveness of first-year interventions? To address these research questions and therefore determine the formative impact of the firstyear engineering and computer science program on which we are conducting research, we have collected diverse student data including grade point averages, concept inventory scores, and data from a multi-dimensional questionnaire that measures students’ use of support practices across their four to five years in their degree program, and diverse background information necessary to determine the impact of such factors on students’ persistence to degree. Background data includes students’ experiences prior to enrolling in college, their socio-demographic characteristics, and their college social capital throughout their higher education experience. For this research, we compared students who were enrolled in the first-year intervention program to those who were not enrolled in the first-year intervention. We have engaged in cross-sectional 2 data collection from students’ freshman through senior years and employed multivariate statistical analytical techniques on the collected student data. Results of these analyses were interesting and diverse. Generally, in terms of backgrounds, our research indicates that students’ parental education is positively related to their success in engineering and computer science across program years. Likewise, longitudinally (across program years), students’ college social capital predicted their academic success and persistence to degree. With regard to the study’s comparative research of the first-year intervention, our results indicate that students who were enrolled in the first-year intervention program as freshmen continued to use more support practices to assist them in academic success across their degree matriculation compared to students who were not in the first-year program. This suggests that the students continued to recognize the value of such supports as a consequence of having supports required as first-year students. In terms of students’ understanding of scientific or engineering-focused concepts, we found significant impact resulting from student support practices that were academically focused. We also found that enrolling in the first-year intervention was a significant predictor of the time that students spent preparing for classes and ultimately their grade point average, especially in STEM subjects across students’ years in college. In summary, we found that the studied first-year intervention program has longitudinal, positive impacts on students’ success as they navigate through their undergraduate experiences toward engineering and computer science degrees.