CHAPTER 1
The Impact of Educational Technology: Students' Experiences and Perceptions of Educational Technology in Higher Education
Neba Nfonsang and Gary Schnellert
Introduction
The world of today relies on technology to operate more efficiently and effectively as technology is required in every context and walk of life to perform almost every task, to work faster and more accurately in ways previously not possible. Technology has been integrated into the fabric of modern society due to its vital role and purpose in the world. Kim (2009) asserted that the purpose of learning technology is to challenge the status quo of education, to provide learning environments that support multiple intelligence and different learning styles, to enhance the sharing of educational material with learning communities, and to enable students to be creators of knowledge and not just consumers of knowledge. To emphasise the need of technology in education, Daggett (2010) stated, "If the American education system is to prepare students to meet the demands of an increasingly technological world, indeed if it is to be effective at all, it must integrate technology into the academic curriculum" (p. 1).
It is essential to explore students' technology skill and preference levels as well as students' experiences and perceptions of educational technology for the following reasons. First, technological proficiency should be a characteristic of this digital age and students have indicated that they needed more technology training and skills instead of new or more technology (Dahlstrom, 2012). It is crucial for educational institutions to continuously assess the technology skill levels of students to understand the types and levels of technology training that should be offered to students to enhance their academic success. Second, since educational technology has historical roots and has evolved from the printing press to today's digital tools (Saettler, 2004) and will continue to rapidly evolve (Arora, 2013), it is imperative to understand students' preferences for educational technology. Third, though findings of studies carried out at an elementary school system at Southwestern Ontario, with a sample population of 106 students, showed that students had positive attitudes towards modern technology (Hurley and Vosburg, 1997), there is very limited research on the attitude of students towards technology especially in higher education. Fourth, the perceptions of usefulness of educational technology help faculties and institutions to make decision concerning integration of technology into the learning process (Moseley, 2010). Also, "Institutions of higher education must be aware of how students employ technology, and must consider student perspectives regarding how technology can best be integrated for instruction and communication" (Surry et al., 201, p. 31).
Purpose of the Study
Schools across the United States have spent large amounts of money for educational and computer-based technologies (Pearson and Young, 2002). In the year 2009, 63 billion dollars was spent on technology across all levels of education in the United States (Daggett, 2010). It is not enough to spend millions of dollars on educational technology. It is more important to find out whether the available institutional technologies are relevant and how students can benefit most from these technologies. Therefore, the purpose of this was to explore the relationships among students' technology skill levels, preference levels of educational technology, attitudes towards educational technology, and perceptions of the impact of educational technology on learning. The following research questions guided this study:
1. What skill levels, preference levels, attitudes, and perceptions do students have in using educational technology?
2. What are the relationships among students' technology skill levels, preference levels of educational technology, attitudes towards educational technology, and perceptions of the impact of educational technology on learning?
3. How do students' technology skill levels, preference levels of educational technology, and perceptions of the impact of educational technology on learning predict students' attitudes towards educational technology?
Significance of the Study
In this era, technology integration into teaching and learning is mandatory as educational institutions believe that technology has the potential to improve learning outcomes. The significance of this study lies in the investigation of higher education students' experiences of technology in an effort to understand the valuable technologies and technology skills required for academic success at postsecondary institutions. Kelly (2010) stated that though most high school students in the United States have grown up immersed in technology and are often called digital natives, many of them lack the skills to apply the latest technology to its maximum potential in an educational setting. Therefore, it is important for high school students to acquire skills for using educational technology before entering college as well and be prepared with the technology skills necessary to survive the higher education learning environment.
Research shows that only 45% of high school students who enter college graduate with a bachelor's degree; this high dropout rate of freshmen is attributed to lack of required skills to survive the college environment (United States Department of Education, 2011). The United States Department of Education (2013) explained that the application of digital technology to teaching and learning will increase the quality of education by improving high school graduation rate and preparing students to be college ready. Therefore, this present study would unveil the relevant technologies and technology skill levels necessary for students to enhance their learning experience.
Since technology is useful for college education, the reauthorized Elementary and Secondary Education Act requires students to be technologically literate before they enter college. The new goal for the educational system in the United States expects every student to be college and career ready after graduation from high school, as proposed by President Obama (United States Department of Education, 2011). The new goal was proposed because the standards required under the No Child Left Behind Act do not reflect the skills needed for college success. For high school students to be college ready, they need to use the types of technologies similar to those used in higher education institutions.
Moreover, this study is very necessary as it adds to existing literature about students' experiences and perceptions of the impact of educational technology. Currently, there is little research evidence and data to demonstrate the impact of technology on learning. Therefore, "there is a need to investigate whether education technology impacts on the teaching and learning experience in a positive way" (Joseph, 2012, p. 3) compared to learning that involves no educational technology.
Finally, this study should provide higher education institutions with actionable recommendations on how to address students' issues in relation to students' technology experiences. Generally, the results of this study should provide information on how technology services in high schools, colleges, and universities could be improved to enhance student's technology experiences. Specifically, this study should inform pre-K-12 schools in the United States about the technology skills and educational technologies that should be integrated into pre-K-12 curriculum to prepare high school students for college. Therefore the results of this study will benefit both high schools and colleges assuming that higher education leaders will establish relationships with pre-K-12 education leaders to facilitate the achievement of the college readiness. Pre-K-12 students will benefit most from educational technology if higher education leaders communicate the skills (including technology skills) required to prepare high school students for college success and contribute in developing the curriculum and standard requirements for pre-K-12 schools ("Postsecondary Readiness," 2013).
Methodology
This research used a nonexperimental, quantitative, cross-sectional, and survey study to analyze the relationships among students' technology skill levels, preference levels of educational technology, attitudes towards educational technology, and perceptions of the impact of educational technology on learning.
Participants
The participants of this study were students enrolled in the University of North Dakota during the 2013–2014 academic years. Two hundred student participants completed the survey. The participants consisted of 35% male, 65% female. The most representative age group consisted of students between thirty and thirty-nine years (43%), followed by students between forty and forty-nine (27%) years. A majority of respondents were graduate students (73%) and ninety-eight students indicated their disciplines representing about forty-three majors (mode = 16 educational leadership). Almost all the students (99%) completed their high school education in the United States, and most participants (40.59%) graduated from high school between 1991 and 2000 while 23.76% graduated between 1981 and 1990. Half of the students did not own any digital device during their high school education; however, 45% owned desktop computers. As university students, most respondents indicated that they owned laptop computers (92.46%), smartphones (75.40%), tablet devices (62.96%), and desktop computers (63.78%).
Procedures
The researcher started the study by meeting the requirements of the Institutional Review Board (IRB) at the University of North Dakota. An online survey was created through the Qualtrics Survey Software, and a list of one thousand randomly selected student e-mail addresses was collected from the Office of Institutional Research at the University of North Dakota. The e-mail addresses were selected using the Statistical Analysis System (SAS), a software system for data analysis. On November 24, 2013, a survey invitation e-mail containing the link to the survey was sent to the selected sample. On November 28, 2013, a follow-up e-mail message was sent to the selected students again to encourage those who did not respond to the questionnaire to complete the online survey. Completion of the survey took an average of five minutes. Survey data submitted by respondents was collected through the Qualtrics software and the last date for data collection was December 1, 2013.
Instrument
A survey instrument created by the Office of Information Technology, University of Minnesota, was modified and used to collect the data for this study. Through personal communication, the researcher obtained permission from the research associate of Academic Support Services at the University of Minnesota to use their technology surveys to guide the development of the instrument for this study. The technology surveys were obtained from the University of Minnesota Web site (http://z.umn.edu/techsurveys). The instrument for this study was used to measure the demographic variables of respondents, such as gender, age group, academic level, and program of study. More important, the instrument used Likert-type items to measure the main variables of this study, including students' technology skill levels (eight items), preference levels of educational technology (nine items), attitudes towards educational technology (nine items), and perceptions of the impact of educational technology on learning (seven items). The Likert scale was preferred because of its suitability for studies that involve the combination of Likert-type items into single composite variables (Boone and Boone, 2012).
Measures
Likert-type and Likert scales were used to measure the research variables: students' technology skill levels (1 = Never used, 5 = Very comfortable), preference levels of educational technology (1 = No preference, 5 = Very strongly prefer), attitudes towards educational technology (1 = Very useless, 7 = Very useful), and perceptions of the impact of educational technology on learning (1= Strongly disagree, 5 = Strongly agree). For the various scales, participants were asked the following:
• to rate their comfort level in using technology such as blackboard (students' technology skill levels scale);
• to select which communication methods — for instance, social media — they would prefer their instructors to use (students' preference levels of educational technology scale);
• to rate how useful educational technologies such as digital videos have been in their coursework (students' attitudes towards educational technology scale); and
• to rate statements concerning the effect of technology in their learning — for example, "educational technology has enabled me to access course material from anywhere at any time" (students' perceptions of the impact of educational technology scale).
To test the quality of each scale, an exploratory factor analysis with oblimin rotation was conducted for the items of each scale. A visual inspection of the scree plots (fig. 1, fig. 2, fig. 3, and fig. 4) for each scale indicated that the data for each scale supported a one-factor solution. A final factor analysis of each scale with number of factors constrained to 1 showed that all the items of the students' technology skill levels, attitudes towards educational technology, and perceptions of the impact of educational technology on learning scales were good for further analysis while two items of the preference levels of educational technology scale were removed as these items (preference 1 and preference 3) had a factor loadings below .30. Internal reliability was found to be sufficient for students' technology skill levels scale (α = .76), preference levels of educational technology scale (α = .73), attitudes towards educational technology (α = .83), and perceptions of the impact of educational technology on learning (α = 94). The scale distributions all approached normality based on the visual inspection of histograms for each scale. Items with factor loadings above .30 were then summed up into their respective composite variables.
