A Multiple Regression Analysis of Gender, Self-Directed Learning, and Algebra Grades among Undergraduate Students Enrolled in University Science, Technology, Engineering, and Mathematics Programs

Evidence suggests females' mathematics skills and abilities are not as well developed as males resulting in females being underrepresented in science, technology, engineering, and mathematics (STEM) career fields. In addition, self-directed learning readiness (SDLR) plays a role in academic performance, giving those with greater SDLR and mathematics abilities better opportunities for entering a STEM related career. Guided by Knowles' adult education theory, the purpose of this study was to examine whether Algebra II grades could be predicted by gender and self-reported SDLR skills. Using criterion based purposeful sampling, 74 undergraduate students who had taken an Algebra II course in college and enrolled in a STEM-related program were recruited through an online recruitment agency. This model was tested using an ordinal multiple regression. No statistically significant results occurred that would demonstrate that Algebra II grades could be predicted by either gender or SDLR scores (?[superscript 2] = 2.81, p = 0.24), with only 0.49% of the variance being explained by the relationship between gender and SDLRS scores and Algebra II grades. Recommendations for future research include conducting a similar study in a different academic context as well as conducting a non-experimental group comparison whereby differences in SDL and mathematics skills between gender groups could be described more effectively. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]