Interactive learning with WeChat’s Rain Classroom in a blended setting: The influence of three types of interactions on learning performance

Many advanced learning tools have been recently developed; however, demand for interactive environments that can enhance learning performance in a blended setting is growing. This study designed an interactive approach by integrating a learning tool, interactive sessions, and a variety of interaction types, and the effects of the types of interactions on learning performance were evaluated. Specifically, the WeChat-based app Rain Classroom was used to enhance learning. Rain Classroom has learner–learner, learner–teacher, and learner– content interaction sessions before, during, and after class; the method was denoted the Rain Classroom interactive approach. Analysis of covariance results revealed that the experimental group using Rain Classroom significantly outperformed the control group participating in conventional learning. A Pearson correlation analysis was conducted on Rain Classroom system log data, and learner interaction features, namely self-practice tests, Danmu discussion, and the instant response system (IRS), were positively related with learning performance; however, attendance was unrelated. Multiple regression analysis was conducted to determine which Rain Classroom interaction types predict learning performance; all interaction types predicted learning performance, but learner–content interactions had the greatest effect. Teachers could refer to the proposed Rain Classroom–based interactive approach when developing methods to improve student learning performance in a digital interactive environment.