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Direct link to each section: Class Materials | Data Documentation | Recommended Readings

Quick Links

• ADRF Login (Okta sign-in required)

  • Setting up your ADRF Account

Class materials

• Project Template

• ADRF User Guide

• ADRF Do's and Don'ts

• JupyterLab Tips

DATA DOCUMENTATION

• Data Schema

• Texas Education Agency (ds_tx_tea)

  • High School Graduates Table

  • Post Secondary and Licensure Table

• Texas Higher Education Coordinating Board (ds_tx_thecb)

  • Data Overview

  • Enrollments Table

  • Graduations Table

  • Institutions Table

  • Licensure and Certifications Table

• Texas Workforce Commission (ds_tx_twc)

  • ETP Table

  • PIRL Table

  • Wages Table (_lehd)

Recommended Readings

• Literature:

  • Andrews, R. J., Li, J., Lovenheim, M. F. “Quantile Treatment Effects of College Quality on Earnings: Evidence from Administrative Data in Texas.” National Bureau of Economic Research. (2012).

  • Bennett, T. “Guest column: Texas lawmakers make unmatched progress on education-to-workforce alignment.” (2021)

  • Burgess, S., Lane, J., and Stevens, D. "Job Flows, Worker Flows, and Churning." Journal of labor economics 18.3 (2000): 473-502.

  • Chen, X., Smith, T., King, C., Christensen, K. “Findings on Student Outcomes: Results from an Employer Survey Pilot Project.” Ray Marshall Center. (2014).

  • Cumpton, G., Christensen, K., King, C., Demakis, C., and Smith, T. “Postsecondary Education, Training and Labor Market Transitions in Texas: A Regional Analysis.” Ray Marshall Center (2014).

  • Goldman, C. A., Butterfield, L., Lavery, D. C., Miller, T., Daugherty, L., Beleche, T., and Han, B. Using Workforce Information for Degree Program Planning in Texas. Santa Monica, CA: RAND Corporation, (2015).

  • Gyll, S. P. Career development by design, not default: Creating a more efficient and data-driven process by connecting aptitude-based learner guidance to post-secondary pathways, competency-based credentials, and high-demand jobs. Competency-based Education, (2021).

  • Hersh, A. S. “‘Build Back Better’ agenda will ensure strong, stable recovery in coming years.” Economic Policy Institute. (2021)

  • King, C. T., Tingle, K. “Wage Insurance and Wage Supplements: Final Evaluation Design Report.” Ray Marshall Center. (2016).

  • Mikelson, K. S., Giani, M., King, C. T., Khan, A. “Estimating Labor Demand and Supply in Texas: How Planning Tools and Data are Used.” Ray Marshall Center. (2014).

  • OECD. Labour Market Relevance and Outcomes of Higher Education in Four US States: Ohio, Texas, Virginia and Washington. Higher Education. OECD Publishing, Paris, chapter 5. (2020)

  • Texas Workforce Solutions. “Texas Growth Occupations.” Texas Workforce Commission. (2016).

  • Williams, A. “Texas must get serious about workforce development.” (2016)

• State and Technical Reports:

  • Wages of Employed Texans Who Attended Texas Public Schools - Statewide (2016 - 2018)

  • Wages of Texas Public College Graduates by College Major - Statewide (2016 - 2018)

  • College Enrollment and Employment Outcomes for High School Graduates - Statewide (2017 - 2018)

  • College Enrollment and/or Employment Following HS Graduation by CTE Participation - Statewide (2017 - 2018)

• Inference:

  • Illinois Department of Employment Security. (2021, November 18). Statewide Unemployment Rate Down, Payroll Jobs Up Significantly in October [Press release]. https://ides.illinois.gov/newsroom/2021/november/statewide-unemployment-rate-down--payroll-jobs-up-significantly-.html

  • Kreuter F. (2021). What surveys really say. Nature, 10.1038/d41586-021-03604-1. Advance online publication. https://doi.org/10.1038/d41586-021-03604-1

  • Michigan Department of Technology, Management & Budget. (2021, November 17). Michigan job growth solid, jobless rate edges down in October [Press release]. https://www.michigan.gov/dtmb/0,5552,7-358-99723-572565--,00.html#:%7E:text=LANSING%2C%20Mich.&text=%22The%20unemployment%20rate%20revisions%20by,in%20October%20to%204.6%20percent.

  • Rampell, C. (2021, October 8). Opinion: The September jobs report in one word: ‘Oof.’ The Washington Post. Retrieved December 12, 2021, from https://www.washingtonpost.com/opinions/2021/10/08/september-jobs-report-analysis-off-catherine-rampell/.

  • Van Dam, A. (2021, November 16). The government dramatically underestimated job growth this summer. The Washington Post. Retrieved December 12, 2021, from https://www.washingtonpost.com/business/2021/11/16/government-underestimated-job-growth/.

• Machine Learning:

  • Education

    • Akour, I., Alshurideh, M., Al Kurdi, B., Al Ali, A., & Salloum, S. (2021). Using machine learning algorithms to predict people’s intention to use mobile learning platforms during the COVID-19 pandemic: machine learning approach. JMIR Medical Education, 7(1), e24032.

    • Gray, C. C., & Perkins, D. (2019). Utilizing early engagement and machine learning to predict student outcomes. Computers & Education, 131, 22-32.

    • Ho, I. M. K., Cheong, K. Y., & Weldon, A. (2021). Predicting student satisfaction of emergency remote learning in higher education during COVID-19 using machine learning techniques. Plos one, 16(4), e0249423.

    • Kotsiantis, Sotiris B., C. J. Pierrakeas, and Panayiotis E. Pintelas. "Preventing student dropout in distance learning using machine learning techniques." International conference on knowledge-based and intelligent information and engineering systems. Springer, Berlin, Heidelberg, 2003.

    • Luan, H., & Tsai, C. C. (2021). A review of using machine learning approaches for precision education. Educational Technology & Society, 24(1), 250-266.

    • Lykourentzou, I., Giannoukos, I., Nikolopoulos, V., Mpardis, G., & Loumos, V. (2009). Dropout prediction in e-learning courses through the combination of machine learning techniques. Computers & Education, 53(3), 950-965.

    • Masci, C., Johnes, G., & Agasisti, T. (2018). Student and school performance across countries: A machine learning approach. European Journal of Operational Research, 269(3), 1072-1085.

    • Sansone, D. (2019). Beyond early warning indicators: high school dropout and machine learning. Oxford bulletin of economics and statistics, 81(2), 456-485.

  • Workforce development

    • Broda, M. D., Bogenschutz, M., Dinora, P., Prohn, S. M., Lineberry, S., & Ross, E. (2021). Using Machine Learning to Predict Patterns of Employment and Day Program Participation. American Journal on Intellectual and Developmental Disabilities, 126(6), 477-491.

    • Knaus, M. C., Lechner, M., & Strittmatter, A. (2020). Heterogeneous employment effects of job search programmes: A machine learning approach. Journal of Human Resources, 0718-9615R1.Wingrove, P., Liaw, W., Weiss, J., Petterson, S.,

    • Maier, J., & Bazemore, A. (2020). Using Machine Learning to Predict Primary Care and Advance Workforce Research. The Annals of Family Medicine, 18(4), 334-340.

    • Sobnath, D., Kaduk, T., Rehman, I. U., & Isiaq, O. (2020). Feature selection for UK disabled students’ engagement post higher education: a machine learning approach for a predictive employment model. IEEE Access, 8, 159530-159541.

*Disclaimer: This reference list is illustrative, not exhaustive. We would be delighted to include any other useful references provided by class participants.