What Factors Predict CS Student Outcomes? Part 4 of the State of Computer Science in Illinois High Schools Series
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| Title: | What Factors Predict CS Student Outcomes? Part 4 of the State of Computer Science in Illinois High Schools Series |
|---|---|
| Language: | English |
| Authors: | Ying Chen, Stephanie M. Werner, Illinois Workforce and Education Research Collaborative (IWERC) |
| Source: | Illinois Workforce and Education Research Collaborative, Discovery Partners Institute. 2025. |
| Availability: | Board of Trustees of the University of Illinois, State of Illinois for Discovery Partners Institute. 200 South Wacker Drive, 20th Floor, Chicago, IL 60304. Tel: 217-766-6779; e-mail: IWERC@mx.uillinois.edu; Web site: https://dpi.uillinois.edu/applied-research/iwerc/ |
| Peer Reviewed: | N |
| Page Count: | 26 |
| Publication Date: | 2025 |
| Document Type: | Reports - Research |
| Education Level: | High Schools Secondary Education |
| Descriptors: | High Schools, Secondary School Curriculum, Computer Science Education, Prediction, Student Participation, Enrollment, Course Selection (Students), Trend Analysis, Student Characteristics, Demography, Teacher Characteristics, Grade Point Average, Course Content |
| Geographic Terms: | Illinois |
| Abstract: | The purpose of The State of Computer Science in Illinois High Schools Series is to analyze the landscape, structures, and pathways of computer science (CS) education in Illinois and to create a baseline by which to measure the expansion of CS education in the coming years. Beginning in the 2023-2024 school year, all districts in the state that serve grades 9-12 must offer every student the opportunity to enroll in a CS course. Because not all districts in the state had CS offerings before this school year, it is imperative to measure capacity for, access to, participation in, and experiences in CS education (i.e., CAPE framework) before and after the mandate went into effect. Analyzing trends through the lens of the CAPE framework will highlight progress while identifying existing gaps in providing equitable access and outcomes for all students. The first report of this Series provided an overview of the CS education landscape in the state by analyzing overall participation trends and details about the most enrolled CS courses. The second report analyzed the CS student body, focusing on students from historically marginalized backgrounds, including trends of their participation in general and rigorous coursework and course outcomes. The third report uncovered the characteristics and assignability patterns of high school CS teachers (i.e., how qualified CS teachers are staffed to certain CS courses) to assess the state's capacity to deliver equitable CS education. This fourth installment of the Series investigates the factors predicting student learning outcomes and continued enrollment in CS courses. Part 4 of the Series expands upon previous reports that detailed participation trends (Part 1), student demographics (Part 2), and teacher workforce characteristics (Part 3). Specifically, Part 4 examines how student-, teacher-, and course-related factors are associated with disparities in students' outcomes during the 2017-2018 and 2021-2022 school years (labeled as SY 2018 and SY 2022, respectively). Examining disparities in students' learning outcomes across multiple levels helps to identify characteristics and conditions associated with improved learning outcomes in high school CS courses and areas in need of improvement. This report considers student learning outcomes using two measures: (1) students' likelihood of receiving a passing grade in their CS course(s), and (2) their enrollment in a subsequent CS course. This dual measure approach is important because academic outcomes and continued participation reflect distinct but equally meaningful dimensions of students' experiences in CS. While a passing grade indicates mastery of course content, enrolling in a second course signals students' sustained interest, identity development, and access to long-term CS pathways. |
| Abstractor: | ERIC |
| Entry Date: | 2026 |
| Accession Number: | ED679334 |
| Database: | ERIC |
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