Table 1
Demographics of the Participant Students (2016–2019).
| Class Subjects | Participant Demographics (n = 597) | ||||||||
| Gender | Ethnicity | ||||||||
| ETE | Biology | Male | Female | White | Black | Hispanic | Asian | Multi | Other |
| 218 (36.5%) | 379 (63.5%) | 375 (62.8%) | 222 (37.2%) | 504 (84.4%) | 19 (3.2%) | 52 (8.7%) | 15 (2.5%) | 1 (0.2%) | 6 (1%) |
Table 2
S-STEM Survey STEM Attitudes Items (Friday Institute for Educational Innovation, 2012e, p.1)
| S-STEM Attitudinal Items | |
| Discipline | Item |
| Math | |
| Math has been my worst subject. (-) I would consider choosing a career that uses math. Math is hard for me. (-) I am the type of student to do well in math. I can handle most subjects well, but I cannot do a good job with math. (-) I am sure I could do advanced work in math. I can get good grades in math. I am good at math. | |
| Science | |
| I am sure of myself when I do science. I would consider a career in science. I expect to use science when I get out of school. Knowing science will help me earn a living. I will need science for my future work. I know I can do well in science. Science will be important to me in my life’s work. I can handle most subjects well, but I cannot do a good job with science. (-) I am sure I could do advanced work in science. | |
| Engineering and Technology | |
| I like to imagine creating new products. If I learn engineering, then I can improve things that people use every day. I am good at building and fixing things. I am interested in what makes machines work. Designing products or structures will be important for my future work. I am curious about how electronics work. I would like to use creativity and innovation in my future work. Knowing how to use math and science together will allow me to invent useful things. I believe I can be successful in a career in engineering. | |
[i] Note: (-) = items negatively worded
Table 3
Teacher Survey Subscale: Importance of DET (Yaşar et al., 2006).
| Importance Subscale Items |
| I would like to be able to teach my students to understand the use and impact of DET. I would like to be able to teach my students to understand the science underlying DET. I would like to be able to teach my student to understand the design process. I would like to be able to teach students to understand the types of problems to which DET can be applied My motivation for teaching science is to promote an understanding of how DET affects society I am interested in learning more about DET though in-service I would like to be able to teach students to understand the process of communicating technical information My motivation for teaching science is to prepare young people for the world of work. My motivation for teaching science is to promote an enjoyment of learning. I believe DET should be integrated into the K-12 curriculum. I am interested in learning more about DET through workshops. I am interested in learning more about DET through college courses. In a science curriculum, it is important to include the use of engineering in developing new technologies. I am interested to learning more about DET through peer training. My motivation for teaching science is to help students develop an understanding of the technical world. My motivation for teaching science is to educate scientists, engineers, and technologists for industry. In a science curriculum, it is important to include planning of a project. How important should pre-service education be for teaching DET? |
Table 4
Students’ STEM Attitude by Gender and Locale Type.
| Variables (N) | STEM Attitude | |
| M | SD | |
| Gender (597) | ||
| Female (222) | 83.12 | 15.79 |
| Male (375) | 92.30 | 15.69 |
| Locale (35) | ||
| Rural (26) | 88.75 | 16.17 |
| Town (2) | 86.90 | 12.01 |
| Suburb (2) | 89.90 | 16.35 |
| City (5) | 89.09 | 17.30 |
Table 5
Teacher Perception Locale.
| Variables (N) | DET | |
| M | SD | |
| Locale (35) | ||
| Rural (26) | 76.71 | 11.43 |
| Town (2) | 78.20 | 9.30 |
| Suburb (2) | 69.48 | 3.39 |
| City (5) | 73.94 | 3.09 |
Table 6
The results of MLM analyses for gender and locations.
| Fixed Effect | Unconditional Model | Random Coefficient Model | Intercept-and Slope-as-Outcomes Model | ||||
| Estimate | SE | Estimate | SE | Estimate | SE | ||
| Gender (β1j) | |||||||
| Intercept (γ10) | –5.04** | 1.53 | –5.30** | 1.85 | |||
| DET (γ11) | –0.11 | 0.19 | |||||
| Town (γ12) | –1.51 | 10.89 | |||||
| Suburb (γ13) | –6.87 | 7.56 | |||||
| City (γ14) | 3.02 | 4.39 | |||||
| Intercept (β0j) | |||||||
| Intercept (γ00) | 90.46*** | 1.50 | 92.50 *** | 1.26 | 92.54*** | 1.25 | |
| DET (γ01) | 0.33* | 0.13 | |||||
| Town (γ02) | –0.12 | 9.21 | |||||
| Suburb (γ03) | 0.67 | 4.64 | |||||
| City (γ04) | 3.18 | 3.29 | |||||
| Variance Estimates | Variance | Variance | Variance | ||||
| Between-Classroom | |||||||
| Intercept (τ02) | 61.37*** | 34.78*** | 26.00*** | ||||
| Gender Slope (τ12) | 15.86 | 29.22* | |||||
| Within-classroom (σ2) | 216.09 | 207.99 | 208.22 | ||||
[i] Note. * p < 0.05; ** p < 0.0; *** p < 0.001.