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Predicting College STEM Enrollment using HPCC Systems in Educational Research

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As part of the 2018 HPCC Systems Summit Community Day event:

Up first, Shah Muhummad Hamdi, Georgia State University, briefly discusses his poster, Dimensionality Reduction on Pbblas.

Following, Itauma Itauma presents his breakout session in the Machine Learning track.

In this study, multiple regression analysis is used to determine if high school students’ perception of their science self-efficacy, identity, and utility predict consideration to enroll in a college stem major. The HPCC Systems ML library will be used to build a multiple regression model utilizing secondary data analysis of the United States High School Longitudinal Study of 2009 (HSLS:09) dataset. The HSLS:09 is a national cohort study of over 23,000 ninth graders from 944 schools in 2009 through their secondary and post-secondary years including choices of college majors and careers. This study demonstrates the use of the HPCC Systems ML library for statistical modeling in education.

Itauma Itauma is a doctoral candidate at Keiser University, a computer science instructor at Wayne State University and an online instructor at Southern New Hampshire University. His interests lie in learning analytics and utilizing HPCC Systems for educational research. He has an undergraduate degree in Electrical Engineering from the University of Ilorin and two Masters Degrees, a Master of Science in Computer Engineering from Istanbul Technical University, majoring in human-robot interaction and a Master of Science in Computer Science from Wayne State University where his thesis was based on leveraging HPCC Systems for Big Data analytics.

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Predicting College STEM Enrollment using HPCC Systems in Educational Research

  1. 1. Innovation and Reinvention Driving Transformation OCTOBER 9, 2018 2018 HPCC Systems® Community Day Presenter: Itauma Itauma Predicting College STEM Enrollment using HPCC Systems in Educational Research
  2. 2. Personal Information Itauma Itauma PhD Candidate, Keiser University @amightyo Itauma Itauma is a doctoral candidate at Keiser University and a computer science and data analytics instructor at Southern New Hampshire University. His interests lie in education analytics, data visualization, and utilizing HPCC Systems, SPSS, and R for educational research. He has an undergraduate degree in Electrical Engineering from the University of Ilorin in Nigeria and two Masters Degrees, a Master of Science in Computer Engineering from Istanbul Technical University in Turkey, majoring in human-robot interaction and a Master of Science in Computer Science from Wayne State University in US where his thesis was based on leveraging HPCC Systems for Big Data analytics. Predicting College STEM Enrollment using HPCC Systems in Educational Research
  3. 3. Motivation
  4. 4. Presentation Overview • Introduction • Background • Objective • Methods • Results • Conclusion
  5. 5. Introduction • STEM: Science, Technology, Engineering, and Mathematics • Critical thinking and problem solving skills • Increasing demand for STEM expertise • Employment growth • STEM 10.5% vs non-STEM 5.2% from 2009 - 2015 (Fayer, Lacey, & Watson, 2017) Predicting College STEM Enrollment using HPCC Systems in Educational Research
  6. 6. Background Supply of STEM workers needs to keep up with demand High schools important in the development of STEM skills • STEM pipeline Intent to major in STEM affected by experiences in high school Predicting College STEM Enrollment using HPCC Systems in Educational Research Self-efficacy, identity, and utility have been associated with motivation, selection of, and persistence in career choices
  7. 7. Definition of Terms Predicting College STEM Enrollment using HPCC Systems in Educational Research Science Self-efficacy • Confidence about ability to succeed in science Science Identity • Seeing oneself as part of a science community Science Utility • Perception of the utility of science in everyday life and for the future.
  8. 8. Objective • To determine if high school students’ perception of their science self- efficacy, identity, and utility are associated with consideration to enroll in a college STEM major. Predicting College STEM Enrollment using HPCC Systems in Educational Research Objective Self- efficacy Identity Utility
  9. 9. Methods Predicting College STEM Enrollment using HPCC Systems in Educational Research • High School Longitudinal Study (HSLS:09) • Dataset of over 23,000 ninth graders from 944 schools in 2009 • Bivariate analysis Science self- efficacy Science identity Science utility Intent to Enroll in a STEM Program
  10. 10. Correlation Matrix – Intent to Enroll in STEM Predicting College STEM Enrollment using HPCC Systems in Educational Research Science Self- Efficacy Science Identity Science Utility Science Self- Efficacy 1.0000 Science Identity 0.5051 1.000 Science Utility 0.3548 0.4449 1.000
  11. 11. Results Predicting College STEM Enrollment using HPCC Systems in Educational Research
  12. 12. Results Predicting College STEM Enrollment using HPCC Systems in Educational Research
  13. 13. Results Predicting College STEM Enrollment using HPCC Systems in Educational Research
  14. 14. HPCC Systems in Educational Research • Why HPCC Systems? • Machine learning vs Statistical learning • HPCC Systems advantage in educational research Predicting College STEM Enrollment using HPCC Systems in Educational Research
  15. 15. Data Visualization in HPCC Systems • Pie Charts, Line graphs, Maps, and other visual graphs • Simplifies the complex • Advanced features • Integration of Tableau in HPCC Systems Predicting College STEM Enrollment using HPCC Systems in Educational Research
  16. 16. Integration of Tableau in HPCC Systems Predicting College STEM Enrollment using HPCC Systems in Educational Research
  17. 17. Conclusion • Positive correlations regarding consideration to enroll in STEM • Science self-efficacy and identity • Science self-efficacy and utility • Science identity and utility • Results show interesting clusters that educators need to further investigate Predicting College STEM Enrollment using HPCC Systems in Educational Research
  18. 18. Predicting College STEM Enrollment using HPCC Systems in Educational Research Thank You!

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