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Lecture presentation

Sunday, November 29, 7:45–8:30 am PST (Pacific Standard Time)
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Examining the Current State and Interest of Computer Science in Secondary Schools

Dr. Keith Howard  
Dr. Nicol Howard  

The purpose of this research was to examine the differential impacts of high school students taking computer application courses as opposed to computer programming and AP computer science courses on the selection of STEM majors.

Audience: Chief technology officers/superintendents/school board members, Teachers, Principals/head teachers
Attendee devices: Devices useful
Attendee device specification: Laptop: Chromebook, Mac, PC
Tablet: Android, iOS, Windows
Topic: Computer science & computational thinking
Grade level: 9-12
Subject area: STEM/STEAM, Computer science
ISTE Standards: For Education Leaders:
Visionary Planner
  • Evaluate progress on the strategic plan, make course corrections, measure impact and scale effective approaches for using technology to transform learning.
  • Communicate effectively with stakeholders to gather input on the plan, celebrate successes and engage in a continuous improvement cycle.
Additional detail: ISTE author presentation, Session recorded for video-on-demand

Proposal summary


The increasing demand in the workforce for science, technology, engineering and mathematics (STEM) professionals has prompted K-20 education’s curricular offerings to evolve in order to meet industry’s challenges. Nonetheless, research has documented the shortage of capable STEM professionals in general, as well as an underrepresentation of women and specific minority groups in STEM fields (Nager & Atkinson, 2016). Prior research has documented relatively low participation in U.S. high school Advanced Placement (AP) computer science courses overall (, 2018). Research is limited on how high school course-taking selection and sequences impact students’ selection of STEM majors of study, as well as on how different course structures and emphases influence those selections in postsecondary education. These impacts have implications for secondary school curriculum design and course selection, as well as for students’ preparedness for postsecondary education.


In this study, data were used from the National Center for Education Statistics High School Longitudinal Study (HSLS:09) public-use dataset collected by the U.S. Department of Education from 2009-2017. The HSLS:09 is a nationally representative, longitudinal study comprised of over 21,000 students from 944 public, private, and charter schools across the United States. The base-year data were collected in 2009 when the cohort was in 9th grade. Subsequent waves of data collection have occurred in 2012, 2013-14, and 2016-17. The study’s cohort will continue to be followed throughout their postsecondary years. HSLS:09 focuses on understanding students' trajectories from the beginning of high school into postsecondary education, the workforce, and beyond (Ingels & Dalton, 2013). A final follow-up data collection will be conducted with the same cohort in 2025. In addition to predictors from the base year (2009-10), data from the first follow-up in (2011-12) and the second follow-up in 2016-17 (released July 2018) were examined in this study to assess their influence on students’ first declared major upon entering postsecondary education. Of particular interest were the possible relationships between the courses taken and the selection of STEM majors.

The researchers examined the relationship between computer applications, computer programming, computer science course enrollment, poverty status, and STEM major selection following high school graduation. Data from multiple waves of the HSLS:09 were analyzed using logistic regression with complex survey dataset procedures in Stata version 15. A set of computer applications, computer programming, and AP computer science courses were examined through regression analyses for their potential impact on students’ selection of STEM majors during years following their scheduled high school graduation date.

Dependent variable: The second follow-up data collection of the HSLS:09 cohort was conducted between March 14, 2016, through January 31, 2017, which was approximately three years after the scheduled graduation date for the cohort. Students were asked to identify their first undergraduate degree/certificate major of study in any program they ever enrolled in following high school. Their responses were classified according to the U.S. Department of Education’s Classification of Instructional Programs and then classified/coded as a dichotomous variable (Study variable X4RFDGMJSTEM = 1 for STEM, 0 for Not STEM).

Independent Variables: Demographic variables included a dichotomous poverty variable (at or above poverty line = 1). In addition, computer applications courses, computer programming and AP computer science courses taken during the spring semester of 11th grade for the cohort, as the cohort’s students were approaching college application and selection of major decisions, were included. These dichotomous course variables (taking course = 1, not taking = 0) were examined for their potential influence on the majors that students first selected after graduating.

The HSLS:09 uses a complex sampling design, which necessitates the use of sample weights and adjusted standard errors to ensure that estimates made from the data are representative of the population, and that hypothesis tests are accurate. The standard error calculation procedure that was used in these analyses is the Balanced Repeated Replication (BRR) method, conducted in Stata 15, utilizing the main sampling weight and its associated set of 200 replicate weights appropriate for each of the analyses. Logistic regression procedures were conducted to examine the influence of enrollment in various courses in the spring of the cohort’s 11th grade year (2012) on students’ initially declared or decided-upon major selection.


Logistic regression was conducted in a sample size of 17,369 observations, which was representative of a population size 1,909,703. The full regression model using independent variables Computer Programming (S2COMPPROG12), AP Computer Science (S2APCOMPSCI12), and Computer Applications (S2COMPAPP12), poverty status (X1POVERTY), and dependent variable STEM Major was statistically significant F(4, 196) = 5.67, p < .001). The strongest predictor of STEM major selection was AP Computer Science course enrollment (Odds Ratio = 7.48, p < .001) indicating students enrolled in this course were about 7½ times as likely to select a STEM major in college. The Computer Programming course was also a significant predictor (Odds Ratio = 2.61, p. = .001) indicating that students taking a programming course were better than 2 ½ times as likely to declare a STEM major as those who did not. The general Computer Applications course was not a significant predictor of STEM major selection (Odds Ratio = 1.13, p = .70), nor was poverty status (Odds Ratio = .83, p. = .28).


Similar to prior research (e.g., Howard & Harvard, 2019; Havard & Howard, 2019), the full logistic regression main effects model yielded statistically significant results. The emphasis in recent years on improving the preparation of U.S. students to meet the challenges of STEM careers has led to increased scrutiny of curriculum offerings in STEM-related coursework. Examination of HSLS data clearly illustrated the significant predictive strength of enrollment in courses with more programming involved (as compared to general computer applications courses) on the selection of STEM majors in postsecondary education. Given the potential influence programming courses appear to have on STEM major decisions, coupled with the increased demand for technology STEM professionals, increased emphasis on computer programming courses in high school is a viable instrument for encouraging more high school graduates in the U.S. to pursue STEM careers. In recent years, students have been learning to code as early as in elementary school in preparation for the changing world of technology. By the time they reach high school, they are arguably far more prepared than previous cohorts to engage in higher-level programming activities. The use of longitudinal data potentially demonstrates the significant influence of certain coursework on continued STEM interest in higher education.

References (2018). and diversity in computer science. Retrieved from

Havard, D. D., & Howard, K. E. (2019). All Advanced Placement (AP) Computer Science is Not Created Equal: A Comparison of AP Computer Science A and Computer Science Principles. Journal of Computer Science Integration, 2(1), 16-34.

Howard, K. E., & Havard, D. D. (2019). Advanced Placement (AP) Computer Science Principles: Searching for Equity in a Two-Tiered Solution to Underrepresentation. Journal of Computer Science Integration, 2(1), 1-15.

Ingels, S. J., & Dalton, B. (2013). High School Longitudinal Study of 2009 (HSLS:09) First Follow-up: A Look at Fall 2009 Ninth-Graders in 2012. National Center for Education Statistics.

Nager, A., & Atkinson, D. R. (2016). The Case for Improving U.S. Computer Science Education. Information Technology & Innovation Foundation, 1-38.

Wolfe, A. (2018). Rapid Rise of China's STEM Workforce Charted by National Science Board Report. Retrieved from’s-stem-workforcecharted-national-science-board-report

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Dr. Keith Howard, Chapman University
Dr. Nicol Howard, University of Redlands
ISTE Certified Educator

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