# Virtual Reality or Videoconferencing for Online Learning? Evidence from Comparative Meta-Analyses
## 3 Method
### Data Analysis  
Comprehensive Meta-Analysis (CMA) 3.0 software was used for the analyses, which included calculating the effect size, as well as a heterogeneity test, moderator analysis, and publication bias analysis. Hedges' \(g\) was chosen as the measure of effect size due to its greater accuracy in studies with small sample sizes compared with [PERSON]'s \(d\). Hedges' \(g\) is a modification of [PERSON]'s \(d\)[79] that accounts for errors in estimating the effect size due to sample size. It corrects such errors by adding a correction factor to the summary estimate, which results in more robust results. While both [PERSON]'s \(d\) and Hedges' \(g\) are susceptible to positive bias to some degree, Hedges' \(g\) can reduce this bias through small-sample correction. Meta-analyses often combine samples of different sizes, which makes it common to choose the more universal and robust Hedges' \(g\) as the effect size indicator.  
The heterogeneity test provides researchers with insight into the extent of variability in effect sizes across different studies--that is, it indicates the presence of statistical  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Code** & **Description** \\ \hline \multirow{3}{*}{Basic information} & Title & Full title \\  & Author & Author names \\  & Year & Publication year \\ \hline \multirow{3}{*}{Research context} & Sample size & Number of participants \\  & & Natural science/humanities and social \\  & Discipline & sciences/medical science/ \\  & & engineering and technology sciences \\  & Grade level & K–12/college \\ \hline \multirow{2}{*}{Technological features (VR)} & Equipment & Desktop/HMD/glasses \\ \hline \multirow{2}{*}{Technological features (VC)} & Camera option & On/off \\  & Platform & Platform used in online learning \\ \hline \multirow{3}{*}{Instructional design} & \multirow{3}{*}{Pedagogy} & Inquiry-based learning/gamed-based \\  & & learning/collaborative learning/ \\ \cline{1-1}  & & direct instruction/experiential learning \\ \cline{1-1}  & Learning outcome & Knowledge/behavior/skill/affective \\ \hline \hline \end{tabular} Note: HMD = head-mounted display; VC = videoconferencing platforms; VR = virtual reality.  
\end{table}
Table 1: Coding manual.  
heterogeneity. This understanding is crucial for deciding whether to use a fixed-effects or random-effects model for meta-analysis. The two commonly used methods for heterogeneity testing are the \(Q\) test and \(l^{2}\) test. If the \(p\)-value of the \(Q\) test is greater than 0.1, it is generally assumed that there is no significant heterogeneity between studies. The \(l^{2}\) statistic reflects the proportion of heterogeneity in the total variation in the effect size. A value of 25% indicates low heterogeneity, 50% indicates moderate heterogeneity, and 75% indicates high heterogeneity [80].  
The effect size analyzed in this study was based on the effect size standard proposed by [PERSON] [81]. An effect size of 0.2-0.5 was considered small, 0.5-0.8 medium, and 0.8 or greater large. After obtaining all of the effect sizes, we chose to use the random-effects model (REM) to calculate the total effect size, as suggested by [PERSON] et al. [82], because we could not guarantee that the true effect sizes of all studies were exactly the same, which made the fixed-effects model implausible.