ТЕОРИЯ И МЕТОДИКА ПРОФЕССИОНАЛЬНОГО ОБРАЗОВАНИЯ THEORY AND METHODOLOGY OF PROFESSIONAL EDUCATION

s (WilsonLine), Education: A SAGE Full-text Collection, Francis (CSA), Medline (PubMed), ProQuest Digital Dissertations & Theses, PsycINFO (EBSCO), Australian Policy Online, British Education Index, and Social Science Information Gateway. In addition, Google Internet searches were performed to help identify gray literature, including a search for conference proceedings. Review articles and previous meta-analyses were used for branching, and the tables of contents of major journals in the field of educational technology (e. g., Educational Technology Research & Development) were manually searched. Effect size calculation and synthesis A d-type standardized mean difference effect size was used as the common metric (i. e., Cohen’s d), and then was transformed into Hedges’ g metric [15] to provide necessary correction for small sample sizes. The random effects model [6] was the main analytical approach for this meta-analysis. A mixed effects model was used to test the difference in levels of moderator variables. In a mixed analysis, average effect sizes for categories of the moderator are calculated using a random effects model. The variance component Q-Between is calculated across categories using a fixed effect model [6]. All analyses, including sensitivity and publication bias analysis, were performed in Comprehensive Meta-AnalysisTM 2.2. 048 [5]. Results The findings of the meta-analysis of effects of classroom technology integration in higher education on student achievement outcomes are presented, first, overall, and then by individual research sub-question as outlined earlier. More detailed information regarding each of these follow-up meta-analyses can be found in respective publications. Overall findings The overall random-model results of the [24] study are shown in Table 1. The total of 879 effect sizes produced a weighted average effect size of 0.27 that was significantly greater than zero. The collection is significantly heterogeneous, based on findings from the fixed model where heterogeneity is tested in terms of the magnitude of Q-Total (i. e., total between-study variability). An effect size of 0.27 is considered to be small and represents a difference of 0.27sd between the mean of the treatment condition and the control condition, amounting to about an 11 % difference. These results suggest that technology-supported РОССИЙСКИЙ ПСИХОЛОГИЧЕСКИЙ ЖУРНАЛ • 2016 ТОМ 13 No 4 294 RUSSIAN PSYCHOLOGICAL JOURNAL • 2016 VOL. 13 # 4 instruction is advantageous compared to either non-use or limited use, but that this advantage is relatively modest. Table 1. Overall weighted average effect size (Random Effects Model) Population Estimates k g SE Lower 95 th Upper 95th Final Collection 879 0.27* 0.02 0.24 0.31 ** Heterogeneity Analysis QT = 3,183.10 (df = 878), p < . 001, I2 = 72.42 * p < . 01; ** Based on the fixed effect model for k = 879 Major function of technology use The results become differentiated when effect sizes are divided by pedagogical application. These results from [24] indicate that technology that is used to support student cognition outperforms all other categories, but especially presentational support (Table 2). This effect is interpreted as a difference primarily between “technology used by students” (for content understanding) and “technology used by teachers” (for content delivery). Other functions, such as support for communication and a mixture of cognitive and presentational support, fall in between these two. Table 2. Instructional moderator variable analysis: Major function of technology use Levels of Technology Use k g Lower 95th Upper 95th QBetween Cognitive Support (CS) 186 0.36 0.28 0.44 Presentational Support (PS) 113 0.15 0.07 0.23 Communication Support 27 0.24 0.12 0.35 Mixture (CS plus PS) 485 0.25 0.21 0.30 Between Groups, df = 3 13.28, p = . 004 Contrast: Cog. Supp. vs. Presentational Supp. , z = 5.14, p < . 0001 RUSSIAN PSYCHOLOGICAL JOURNAL • 2016 VOL. 13 # 4 295 РОССИЙСКИЙ ПСИХОЛОГИЧЕСКИЙ ЖУРНАЛ • 2016 ТОМ 13 No 4 Blended learning The effects of blended learning (i. e., partly in class and partly online) were derived from the [24] database and analyzed and reported in [3]. As evident from Table 3, the random-effects weighted average effect size (= 0.334, k = 117) is larger than the overall average effect of technology use in the original metaanalysis and is in line with the findings from the other meta-analyses of blended learning [21, 26]. Apparently, there is an advantage that accrues from balancing face-to-face instruction with online learning outside of class. The mechanisms of this effect have not been determined, so one of the challenges of educational technology research of the future will be to tease out the effects of variables such as amount of time devoted to each pattern of instruction, the most effective learning strategies, and the teacher’s role in the online portion of blended learning. Table 3. Weighted average effects for blended learning Analytical Models K g SE Lower 95th Upper 95th Random Effect Model 117 0.334* 0.04 0.26 0.41 Fixed Effect Model 117 0.316** 0.02 0.28 0.36 Heterogeneity Q-total = 372.91, df = 116, p < . 001 I-squared = 68.89 % τ2 = 0.11 * z = 8.62, p < . 001; ** z = 15.68, p < . 001. Interaction treatments Interaction treatments were defined by [4] as instructional setups in distance education that are intended to facilitate and promote interaction among students, between students and teachers, and between students and the content. This definition was used to code studies in the [24] study in classroom setting. Table 4 shows the results of this basic analysis. Conditions where interactions were greater in the treatment group, compared to the control condition, produced results that were significantly higher than when the control group was higher in the potential for interaction. As expected, when the two conditions were roughly the same (i. e., = 0.29, k = 703), the outcome was not significantly different from the former condition (i. e., = 0.34 vs. 0.29). РОССИЙСКИЙ ПСИХОЛОГИЧЕСКИЙ ЖУРНАЛ • 2016 ТОМ 13 No 4 296 RUSSIAN PSYCHOLOGICAL JOURNAL • 2016 VOL. 13 # 4 Table 4. Mixed effects analysis of the degree of student-student interaction Levels k g SE Lower 95th Upper 95th QBetween Equal in control and experimental groups 703 0.29 0.02 0.25 0.33 Control group higher 127 0.16 0.04 0.07 0.24 Experimental group higher 48 0.34 0.07 0.20 0.48 Between Groups, df = 2 8.93, p = . 012