to achieve a goal more efficiently or effectively. This definition is broad enough to accommodate the wide variety of innovative tools and strategies included in this book, yet narrow enough to exclude tools that already enjoy universal use in schools, such as the chalkboard or the pencil. In keeping with this definition of technology, we define educational technology as the use of technology tools in the classroom to improve learning.
Several meta-analyses (along with numerous independent primary studies) have addressed the general effects of educational technology on student achievement, motivation, and behavior. A meta-analysis is a method of statistical research that involves gathering a large number of studies on a particular topic or strategy in order to calculate its average effect. Basically, a meta-analysis aims to quantify the general effectiveness of a certain strategy or topic by combining the results from numerous individual studies. The strategy’s level of effectiveness is frequently conveyed using a number known as an effect size. In educational research, effect sizes near 0.15–0.20 are considered small, sizes near 0.45–0.50 are considered medium, and sizes near 0.80–0.90 are considered large (Cohen, 1988; Lipsey, 1990). The greater the effect size, the more effective the strategy. Table 1.2 reports the findings from several meta-analyses that examined the effect of technology on student achievement.
Table 1.2: Selected Meta-Analyses for the Effects of Technology on Student Achievement
In his book Visible Learning, John Hattie (2009) combined the results from more than 800 meta-analyses, including over 52,000 studies and over 145,000 effect sizes, to pinpoint the elements that have significant correlations with student achievement. Some of the elements that Hattie analyzed pertained to the use of educational technology as defined in this book. Specifically, Hattie found that when used as a supplement to the teacher’s instruction, computer-assisted instruction had an effect size of 0.45, but when used as a replacement for the teacher’s instruction, it had an effect size of 0.30. In other words, computer-assisted instruction is likely to result in a 12 percentile point gain in achievement when it replaces the instruction of the teacher, but this gain is likely to rise to 17 percentile points when the technology is used as a supplement to the teacher’s instruction. Table 1.3 reports the findings for computers used as supplements versus replacements for teachers.
Table 1.3: Summary of Effects From Studies of Computer as Replacement vs. Supplement to the Teacher
Source: Based on results reported by Hattie, 2009, p. 223.
As shown in table 1.3, technology is best used as a supplement to effective instruction rather than a replacement for teachers. For the purposes of this book, we have categorized the research on using specific types of educational technology as a supplement (as opposed to a replacement) into four basic categories: (1) computers (including computer-assisted instruction and one-to-one laptop instruction), (2) the Internet (including distance learning and blended learning), (3) interactive whiteboards, and (4) mobile devices (such as smartphones or student response systems [SRS; also known as clickers]).
Computers
Perhaps the greatest concentration of educational technology studies has focused on the general use of computers. Since the 1960s, “thousands of comparisons between computing and noncomputing classrooms, ranging from kindergarten to graduate school, have been made” (Tamim et al., 2011, p. 5). The research on computers can be broadly organized into two categories: (1) computer-assisted instruction (CAI) and (2) one-to-one laptop instruction.
Computer-Assisted Instruction
The most common focus for computer-related research has been computer-assisted instruction. CAI is typically defined as “a method of instruction in which the computer is used to instruct the student and where the computer contains the instruction which is designed to teach, guide, and test the student until the desired level of proficiency is attained” (Jenks & Springer, 2002, p. 43). Table 1.4 reports the results of a number of meta-analyses on computer-assisted instruction.
The meta-analytic findings in table 1.4 indicate that CAI generally yields a percentile gain ranging from 6 to 14 points. Other studies have shown CAI to produce small to moderate gains in achievement in mathematics (Barrow, Markman, & Rouse, 2008; House, 2002; Huang & Ke, 2009), science (Azevedo, 2005), and beginning reading (Chambers et al., 2008). While the general results of the CAI studies are positive, research indicates that at least three factors mediate the effects of CAI: (1) individual teaching practices, (2) fidelity of teacher implementation and technology use, and (3) degree of student collaboration while using technology. We consider each mediator briefly.
Table 1.4: Selected Meta-Analyses for the Effects of Computer-Assisted Instruction on Student Achievement
Several researchers have postulated that individual teaching practices influence the effects of CAI in the classroom. Rana Tamim, Robert Bernard, Eugene Borokhovski, Phillip Abrami, and Richard Schmid (2011) reported a mean effect size of 0.33 in their meta-analysis on digital technology use in various settings and learner groups. Tamim and her colleagues (2011) wrote: “The average student in a classroom where technology is used will perform 12 percentile points higher than the average student in the traditional setting that does not use technology to enhance the learning process” (p. 17). Still, the authors emphasize mediating variables like instructional goals and individual teacher practices:
It is arguable that it is aspects of the goals of instruction, pedagogy, teacher effectiveness, subject matter, age level, fidelity of technology implementation, and possibly other factors that may represent more powerful influences on effect sizes than the nature of technology intervention. (p. 17)
Likewise, Qing Li and Xin Ma (2010) found that mathematics students who used computer-assisted instruction generally had higher achievement, but the authors were quick to point out that these results “should not diminish the importance of good teaching.… To achieve maximum benefit, the way to use CT [computer technology] matters” (p. 232). Similar findings have also been reported by Sara Dexter, Ronald Anderson, and Henry Becker (1999) and Barbara Means (2010).
Researchers have also cited fidelity of technology implementation as critical to the success of technology. For example, Kelly Glassett and Lynne Schrum (2009) drew such conclusions from a two-year investigation of MINTY, a schoolwide project designed to build technology-equipped classrooms, create a learning community for educators, and mandate teacher participation in trainings that prioritized instruction. They concluded:
Positive effects of technology … are mediated by the fidelity of implementation. Even if schools and teachers are provided with enough access to appropriate instructional technology, and teachers receive proper professional development in the use and integration of educational technology and technology is integrated in curricula, course objectives, and assessment, the outcomes are fundamentally grounded in self-reflective processes in human adaptation and change. (p. 148)
In short, Glassett and Schrum found that while the technology produced positive effects, ensuring that all teachers use it to its full potential could be challenging.
Students’
