Science, Technology, Engineering, and Mathematical (STEM) schools lack gender and ethnic diversity. To stimulate change, STEM Generation Z digitally connected diverse student learning styles must be considered. Failure to implement online or blended STEM programs that integrate workplace simulations and gaming may contribute to a lack of STEM student diversity.
During the leadership and career building process, STEM educators are challenged to include programs and activities that enhance digital age learning. Through cross cultural gender and ethnic digital age STEM learners may prefer online or blended classroom options, US STEM school online or blended learning education programs are limited. A majority of STEM program models focus on inquiry-based curriculum or project based learning activities that occur in traditional classrooms, not in online or blended classroom settings; however, digital age Generation Z STEM learners may view these types of classroom environments as static and procedural (Rudi, 2012).
Future STEM workplaces are predicted to be highly digitally connected working environments; however, American STEM education leaders may tend to overlook the needs of digital age diverse Generation Z students who find connectivity a way of life, a preferred learning style, and the assumed work place environment of the future (Harrison, 2011). According to Oyana, Garcia, Haegele, Hawthorne, and Morgan (2015), STEM researchers mainly focus on geographical locations and the need for STEM schools; however, data on why students who express an interest in STEM educational programs but may not enroll in STEM schools are limited. Additionally, STEM comparisons of program models offered in STEM schools to meet the cross cultural needs of Generation Z digital age students entering STEM programs are lacking in the literature.
The first hypothesis predicted a statistical difference between students’ achievements in grades nine and ten math and science according to years existed. To evaluate these predictions, a two-way ANOVA was conducted to examine the differences according to students’ achievements and years. A two-way ANOVA indicated significant differences (F=4.929, df = 2, p <.016) across the span of three years, 2013, 2014, and 2015. The Fisher’s Least Significant Difference (LSD) test was used, and the degrees of students in 2013 was found to be greater than the degrees of students in both 2014 and 2015, deeming significant differences. However, no difference between other groups existed.
The second hypothesis stated a statistical difference between students’ achievements in grades nine and ten math and science in Florida, Michigan, and North Carolina. To evaluate these predictions, a two-way ANOVA was conducted to investigate the differences according to students’ achievements and states. A two-way ANOVA indicated significant differences (F=16.610, p <.05) across the span of Florida, Michigan, and North Carolina. The Fisher’s Least Significant Difference (LSD) test was used, and the results were that students who lived in North Carolina were less than the degrees of students who lived in both Florida and Michigan, A significant difference was shown to exist. However, no difference between other groups existed.
The third hypothesis was a statistical difference between students’ achievements in grades nine and ten math and science existed. To evaluate these predictions, a two-way ANOVA examining the differences according to students’ achievements and types of study was used. Using the two-way ANOVA, a significant differences (F=18.105, p <.01) according to the types of study (Math and Science) was found. The Fisher’s Least Significant Difference (LSD) test was used, which indicated that students’ achievement in math (M =46.50) and science (M=39.11) were deemed significant, which meant that students in mathematics were greater than science.
The fourth hypothesis was that a statistical difference between students’ achievements in grades nine and ten math and science according to gender, male and female would exist. To evaluate these predictions, a two-way ANOVA was conducted to examine the differences according to students’ achievement. Significant differences (F=14.986, p <.01) according to the types of gender, male and female were found after a two-way ANOVA was conducted. The Fisher’s Least Significant Difference (LSD) test was used, and students’ achievement for males was M =46.17 and for females M=39.44. These data are deemed significant, which means that male students’ achievement were greater than female students’ achievement. Interactions between gender and year (F=1.257, p <0.303), between gender and states (F=4.671, p <0.05), and between gender and types of study (F=0.640, p <0.432) were found. Significant differences were not found between these interactions, which meant no effects of interaction between gender and years, gender and states, and gender and types of study.
The fifth hypothesis was that a statistical difference between students’ achievements in grades nine and ten math and science according to ethnicity, African American, American Indian, Asian, Hispanic, pacific Islanders, White, two or more races existed. To evaluate these predictions, a two-way ANOVA examining the differences according to students’ achievements and ethnicity was conducted. A two-way ANOVA indicated significant differences (F=279.663, p <0.01) according to the types of ethnicity. The Fisher’s Least Significant Difference (LSD) test was used, and students’ achievement for African American students (M =17.33) were less than other ethnicities students’ achievement.
Students’ achievement for American Indian students (M =27.22) were less than other students that were of Asian, Hispanic, Pacific Islanders, White, or Two or More Races. Students’ achievement for Asian students (M =70.33) had the highest achievement among all ethnicities, while results for students’ achievement for Hispanic students (M =33.89) showed that Hispanic students were less than students who were of the Pacific Islanders, White, or Two or More Races. Students’ achievement for Two or More Races Students were M =43.00; whereas, results for students’ achievement for White students (M =52.89) included higher achievement than two or more race students. Though these differences were significant, no differences between other groups.
Secondary STEM program descriptions for Florida, Michigan, and North Carolina were derived from archived STEM data posted on state educational department Websites. The program descriptions were presented as teaching approaches. Based on differences between the distributions of different types of STEM teaching approaches for each state, the data were ranked and compared based on the number of times the teaching approaches appeared in the stored data.
In Florida, Michigan, and North Carolina STEM secondary schools, no difference in the first three teaching approach programs existed. Teaching approach programs aligned to the Standard Course of Study in each state, programs aligned to Career and Technical Education Pathway Standards, and teaching approaches primarily centered on project based learning and problem solving methods were used. In the fourth ranking, Florida and Michigan STEM secondary school leaders advocated STEM thinking and problem solving related to technology and engineering designs. North Carolina STEM was the inclusion of teaching approach programs that enhanced Standards-based mathematics, science, and language arts curriculum supported by the National Science Foundation and research.
In the fifth ranking, focuses were different. Florida secondary focused on social, emotional, physical, and academic needs being meet by school, families, and community STEM partners. Michigan emphasized the use of hands on learning practices, problem-based, authentic, engaging, and experimental methods, and North Carolina concentrated on direct, problem-based learning.
Florida espoused integrated experiences and opportunities for mentoring by businesses, industry, and research organization leaders in the sixth ranking. Michigan prescribed that instructors facilitate and model the integration of technology and engineering in the mathematics and science curricula. North Carolina utilized instructional strategies to meet needs of different learning styles.
In the seventh ranking, Florida included strategies to integrate coursework and projects. Michigan and North Carolina focused on the utilization of industry partners. In eighth ranking, all three states centered on the integration of learning across the curriculum, not just in science, technology, engineering, or math. Florida focus was on utilizing integrated learning approaches to enhance across the curriculum to enhance learning, and Michigan included opportunities to improve learning, processing, research, literacy, and communication skills. North Carolina promoted the use STEM teaching approaches to teach all subjects.
Florida, Michigan, and North Carolina secondary STEM leaders used integrated teaching approach programs to promote science, technology, engineering, and mathematics in the STEM classroom which were aligned to Standard Courses of Study and Career and Technical Education Pathway Standards. These secondary STEM leaders advocated project based learning and critical thinking. Though virtual schools existed in each state, STEM schools were not created as exclusively virtual or blended learning environments.
References
Harrison, J. (2011). Instructor Transformational Leadership and Student Outcomes. Emerging Leadership Journeys. 4, 82- 136.
Oyana, T., Garcia, S., Hawthorne, T., Haegele, J., Morgan,J., & Young, N. (2015). Nurturing diversity in STEM fields through geography: The past, the present, and the future. Journal of STEM Education. 16(2).
Rudi, A. (2012). The digital natives are restless: Inspiring a new generation of learners. School Business Affairs. 78(1), 8-10.