Factors that Influence Science Teachers’ Selection and Usage of Technologies in High School Science Classrooms

Abstract

This study contributed to our understanding of those factors that serve as predictors of science teachers’ selection and use of technologies and more specifically, how selection and usage was realized among teachers of different science disciplines. Notable descriptive statistics were examined, and we tested an explanatory model of how demographics, school context, pedagogical approaches and professional development (PD) influenced the likelihood of a teacher using a tool via a multilevel cross-classification-ordered logit analysis (Goldstein 1995). The findings revealed that science teachers were more likely to use hardware than software; more specifically, this included instructional tools (i.e., SMARTboards, clickers) and laboratory tools (probeware). Differences in teachers’ use of tools were largely due to differences in tools as opposed to differences in teacher characteristics. Use of a tool was more likely by teachers who taught physics, who taught via inquiry, or who had more PD with a tool. These findings have implications for how we conceptualize selection and usage of technologies that enter the science education pipeline; which tools become sustainable in the science classroom and how technological take-up differs across science disciplines.

Appendices

Appendix 1: Power Analysis of Sample Size

Level	Effect size
	0.1	0.2	0.3	0.4
2) Teacher	0.32	0.84	0.99	1.00
1) Tool	0.94	1.00	1.00	1.00

Appendix 2: Below We Illustrate The Coding Scheme that was Used for Identified Variables of the Study

Teacher demographics variables
Gender	0: female 1: male
Years of teaching experience	Manually inputted by participant
Highest educational level	0: college 1: teaching certificate 2: masters 3: doctorate
Subjects taught	manually inputted by participant
Grade levels taught	manually inputted by participant
Tenured	0: no 1: yes
Type of school	0: public school 1: charter school 2: private school 3: alternative school
School location	0: urban 1: suburban 2: rural
Age	0: 30 years and under 1: 31– 40 years 2: 41–50 years 3: 51+ years

Teacher attitudes about their students coding key
My students…	Coding key
a) have high learning ability	1: strongly disagree 2: disagree 3: neutral 4: agree 5: strongly agree
b) are interested in the subject matter
c) are well behaved in class
d) work well in groups
e) have participated in inquiry lessons
f) have experience using technology
g) have access to learning technology at home

Teachers top three tools per professional development
Tool category	Coding key
Left blank	0
Software—internet websites/software such as word, PowerPoint	1
Software—data analysis tools	2
Software—modeling/simulation	3
Hardware—learning tools (student use)	4
Hardware—instructional tools (teacher use)	5
Hardware—computers/tablets	6

Appendix 3: Correlation–Variance–Covariance Matrix

Correlation–variance–covariance matrix of outcome variables and explanatory variables for word-level analysis. The correlations, variances and co-variances are along the lower left triangle, diagonal and upper right triangle of the matrix.

Variable		1	2	3	4	5	6	7	8	9
1 Technological tool use by a teacher		0.596	0.008	0.014	0.057	0.021	0.024	0.012	0.009	0.000
2 Educational level: Masters		0.038	0.083	0.015	0.013	−0.001	0.000	0.000	0.001	0.000
3 Subject taught: physics		0.038	0.111	0.218	−0.042	0.001	0.000	0.001	0.000	0.000
4 Students have participated in inquiry lessons		0.072	0.045	−0.089	1.035	0.003	−0.002	0.001	0.003	0.000
5 Days of training: <1		0.112	−0.007	0.007	0.011	0.062	−0.002	−0.001	−0.001	0.000
6 Days of training: 1–2		0.171	0.006	−0.001	−0.012	−0.049	0.032	0.000	0.000	0.000
7 Days of training: 3–5		0.142	0.015	0.019	0.005	−0.030	−0.021	0.013	0.000	0.000
8 Days of training: 6–10		0.131	0.028	−0.011	0.030	−0.024	−0.016	−0.010	0.008	0.000
9 Days of training: more than 10		0.035	0.006	0.007	−0.002	−0.005	−0.003	−0.002	−0.002	0.000

1. Bold values indicate the variances