The teachers in the dedicated technology course plus technology infusion group were required to take a dedicated instructional technology course during their undergraduate programs, but also had instructional technology-infused across methods courses. The group consisted of 8 males and 59 females. Within this group, 8 teachers were 18-24 years old, 30 were 25-34 years old, 13 were 35-44 years old, 13 were 45-54 years old, and 3 were 55-64
years old. There were 13 teachers who had been teaching for less than a year, 6 who had been teaching for a year, 15 who had been teaching for 2 years, and 33 who had been teaching for 3 years.
The demographic information for each group is summarized in Table 1. Table 1
Demographic Information for Teachers by Group
Group 1 Group 2 Group 3
Gender Male 11 5 8 Female 26 17 59 Total 37 22 67 Age Group 18-24 6 5 8 25-34 16 8 30 35-44 12 3 13 45-54 1 3 13 55-64 2 3 3 Total 37 22 67
Teaching Experience Less than 1 year 4 3 13
1 year 5 6 6
2 years 7 4 15
3 years 21 9 33
Total 37 22 67
Instrumentation
While the topic of factors influencing teacher integration of technology has been well researched, there is a gap in the research on what contributes to the presence or absence of those factors. One of the factors that affect teachers’ integration of technology is their self-efficacy. This study investigated if there is any relationship between how teachers are prepared for technology integration in their undergraduate programs and their self-efficacy with technology. In general, self-efficacy has proven to be problematic to measure. There have been several
instruments developed, but problems have arisen with each in terms of construct, validity, and reliability (Tschannen-Moran & Woolfolk Hoy, 2001). Similarly, the existing studies on teacher self-efficacy, specifically with technology, use a multitude of different instruments as there has not been a consensus on the best instrument for measurement. Researchers have simply either adapted existing self-efficacy tests or created their own instruments for measuring technology self-efficacy, many specific to particular subject areas. Few of these tests are specific to
behavior, and even fewer are specifically aligned with the International Society for Technology in Education’s standards for teachers (Gentry, Baker, Thomas, Whitfield, & Garcia, 2014).
Although there were several instruments available for assessing teachers’ self-efficacy with technology, the Technology and Teaching Efficacy Scale (TTES) was selected because other instruments used in prior research were limited to measuring teacher technology use or attitudes rather than true self-efficacy. The TTES consists of 22 items on a 5-point Likert-type scale; the total scores range from 22 to 110. Total scores closer to 22 indicate that the teachers have low self-efficacy with technology integration, while total scores closer to 110 indicate teachers are highly self-efficacious with technology. Developed and tested by Tanguma, Underwood, & Mayo (2004), the instrument has a Cronbach’s alpha of .98, indicating strong internal validity. The scale was used in a longitudinal study geared at increasing pre-service teachers’ technology integration self-efficacy through a technology training program, and a study of a particular course design’s impact on pre-service teachers’ technology self-efficacy (Mayo, Kajs, & Tanguma, 2005; Willis, 2015).
Two subscales comprised the TTES: (a) the Use of Technology Efficacy Scale, and (b) the Teaching Efficacy Scale. Each subscale consisted of 11 questions with total scores for each section ranging from 11 to 55. The use of technology efficacy scale included items such as “I
am able to use technology to capture a student’s interest” and “Students are successful in my classes because of my ability to effectively incorporate technology into my teaching.” Items on the teaching efficacy scale included “I vary my teaching strategies to meet the needs of my students” and “Even students with poor academic records can benefit from my teaching.” Response options on the TTES were Strongly Disagree, Disagree, Neutral, Agree, and Strongly Agree, rated on a scale from 1 (Strongly Disagree) to 5 (Strongly Agree). This study utilized the 11 questions that comprised the Use of Technology Efficacy Scale, as the nature of the items included in that section of the TTES made it the closest match for data collection for this study (Appendix A). The survey took approximately 10 minutes to complete. Permission to use the survey was granted by one of the developers before dissemination to beginning teachers (Appendix B).
Procedures
With the written permission of the authors, the Use of Technology Efficacy Scale, a subset of the Technology and Teaching Efficacy Scale (TTES), was administered to the
participants in this study. Before the administration of the survey, permission was obtained from the school system’s central office (Appendix C) and Liberty University’s Institutional Review Board (IRB) (Appendix D). Once the central office granted permission and Liberty’s IRB approved the proposal, the 11-item survey including an informed consent form and additional items for collecting demographic information was emailed to the Beginning Teacher Coordinator at the central office (Appendices E-G). The Coordinator electronically disseminated the survey to the beginning elementary teachers with no more than three years of teaching experience.
All efforts were made to create a subject line for the email sent to teachers requesting their participation, to be attention-getting. The body of the email properly informed participants
of the intent of the study, explained the survey, and was crafted to evoke an emotional response that encouraged teachers to participate. The email included a link to the survey, and the first page of the survey included a link to the downloadable informed consent form. The participants then had the option of indicating their consent by proceeding with the survey or closing the survey. The survey took approximately 10 minutes for the teachers to complete, included the question items that captured demographic information.
Beginning teachers who completed the survey were eligible to be entered in a drawing for one of three $100 Visa gift cards. The emails for the gift card drawing were collected separately from the survey responses in the study to ensure teacher anonymity. No identifying data was collected. The survey remained open for four weeks. Data collected from the survey was exported from Qualtrics into SPSS. Within SPSS, the data was visually examined, and incomplete responses were removed. In accordance with Liberty University’s dissertation policy, all data collected will be stored in a secured area for three years, after which they will be destroyed.
Data Analysis
This study utilized a one-way between-subjects analysis of variance (ANOVA) for analyzing the data collected from the surveys. According to Gall et al. (2007), an ANOVA is used to compare more than two means. It compares the between-group variances in individual scores with within-group variances in those same scores. Warner (2013) stated that ANOVAs are appropriate for studies such as this one, where the means of groups that are naturally occurring are being compared, and where participants are members of only one group. Data from the study were analyzed to see if the mean technology self-efficacy scores of beginning elementary school teachers from each type of undergraduate teacher education program differed significantly.
A total technology integration self-efficacy score was calculated for each teacher by summing the responses to the 11 survey questions. The range of possible scores was from 11 to 55, with a score of 11 indicating low technology integration self-efficacy, and a score of 55 indicating a high level of technology self-efficacy. A box and whisker plot of the total scores was used to determine if there were any outliers in the data. One outlier was identified and removed. The normality of the data distribution was tested using a Kolmogorov-Smirnov test since the number of participants was greater than 50 (Warner, 2013). The Kolmogorov-Smirnov test indicated that there was a violation of the assumption of normality of distribution, so frequency histograms were created to provide a visual check of the shape of the distribution. The histogram showed the shape of the distribution curve to be acceptable, and this was supported by calculated skewness and kurtosis scores that fell within the acceptable range for normality of distribution. A Levene’s test was used to test the assumption of equality of variance, and the test determined that there was no violation of the assumption.
Descriptive statistics were used to examine the mean and standard deviation of the survey scores. If the ANOVA yields a difference between means by way of a significant F ratio, it is advisable to conduct follow-up testing via a t test for multiple comparisons (Warner, 2013). For this study, the results of the ANOVA were examined, and since none of the
differences in the means were found to be significant, no post hoc testing was conducted. Partial Eta squared (2) was used to calculate effect size with an alpha (α) of .05 (Warner, 2013). The sample size met the minimum (126 participants) that is required to achieve a medium effect size for a study utilizing a one-way analysis of variance (ANOVA) with three groups, with a statistical power of .7 at the .05 alpha level (Gall et al., 2007). All tests were run at the 95% confidence level.
CHAPTER FOUR: FINDINGS