An Experiential Online Training Approach for Underrepresented Engineering and Technology Students - Arefeh Mohammadi; Kevin Grosskopf; John Killingsworth (MDPI AG, 2020)
Workforce pipelines are essential to sustain a productive workforce in an increasingly competitive, high-tech environment. Advanced automation, sensors, materials and data analytics will increase the need for highly skilled workers in the manufacturing (and manufactured construction) sector. Attracting and developing the next-generation workforce is not without its challenges; however, students are often deficient in technical skills and generally have negative perceptions about manufacturing and construction. As a result, new education and training models have been developed to provide instruction at all levels of the educational system, with a focus on both traditional students and non-traditional students, including ethnic minorities, women, veterans, disabled persons and older adult learners. This study focused specifically on certain underrepresented students in STEM programs offered at community colleges in the Great Plains region of the U.S. An available online training program by the Society of Manufacturing Engineers was used as a contextualized online training tool. The Learning Management System embedded in this online training tool was used to gather student data. Conducting multiple regression analyses on the test outcomes, completion rates, and improvement between post-test and pre-test scores showed that female participants achieved greater improvement between pre- and post-test scores than males, and achieved higher rates of credentialing compared to all other demographic groups. African American participants achieved greatest improvement between pre- and post-test scores than all other ethnic groups while Hispanics achieved higher rates of module completion. Additionally, this study also examines the background related to contextualized teaching and learning, as well as the effectiveness of this delivery method for these underrepresented populations.