Enhancing the Art & Science of Teaching With Technology. Robert J. Marzano

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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’ degree of collaboration while using technology also mediates its impact. For instance, Yiping Lou, Phillip Abrami, and Sylvia d’Apollonia (2001) found that students may get the most out of computer-assisted technology when they collaborate in small groups. In their meta-analysis, they reported an average effect size of 0.15 for the effect of collaborative technology use (as opposed to individual learning with one student per computer) on student achievement. They also reported an average effect size of 0.31 for the effect of collaborative technology use on task performance. Lou and her colleagues wrote, “When working with CT [computer technology] in small groups, students in general produced substantially better group products than individual products and they also gained more individual knowledge than those learning with CT individually” (p. 476). Multiple studies have corroborated the finding that collaborative use of technology may yield higher achievement gains than individual use (Gallardo-Virgen & DeVillar, 2011; Hattie, 2009).

       One-to-One Laptop Instruction

      Put simply, one-to-one (or 1:1) laptop use means that every student has access to his or her own laptop in the classroom. As laptop technology advances and becomes more affordable, there is a growing push to implement one-to-one laptop classrooms on a national and even global scale. As an example, consider One Laptop per Child (OLPC), a nonprofit organization that seeks to put a laptop into the hands of every child in the world. The program has already provided over two million students and teachers with laptops (OLPC, 2013a). OLPC’s mission statement is:

      We aim to provide each child with a rugged, low-cost, low-power, connected laptop. To this end, we have designed hardware,